Sci doi: 10.3390/sci8080220

Authors: Shailendra Pawanr Kapil Gupta

Sustainable manufacturing increasingly demands environmentally friendly machining strategies, and dry machining has become recognized as a sustainable alternative to conventional coolant-assisted processes. This study presents a framework built on a machine learning technique for optimizing the dry machining performance of Super Duplex Stainless Steel (SDSS 2507) using textured cutting inserts. Gaussian process regression (GPR) models were developed to predict maximum roughness depth (Rmax) and maximum flank wear (VBmax). Gaussian data augmentation was employed to enhance model generalization. The predictive performance was strong, with R2 values recorded above 0.95 on testing datasets. To identify optimal machining parameters, GPR was integrated with particle swarm optimization (PSO), enabling independent optimization of Rmax and VBmax. The framework achieved reductions of 13.97% in Rmax and 30.70% in VBmax compared to experimental benchmarks. The results confirm the effectiveness of data-driven optimization in enhancing surface quality, tool performance, and intelligent machining control.

Sci doi: 10.3390/sci8080219

Authors: Yasin Paşa

Mesh resolution is a central source of numerical uncertainty in two-dimensional flood modelling because it controls terrain representation, wetting–drying transitions, computational cost, and the transferability of calibrated parameters. Yet controlled evidence remains limited on whether two widely used solvers exhibit the same resolution response patterns across flood magnitudes. This study compares calibrated model configurations developed in HEC-RAS 2D version 7.0 and SMS 13.0–TUFLOW Classic for a mountainous to low-gradient reach of the Little River, Tennessee, USA, using identical terrain, land cover roughness, and hydrological boundary data. The models were calibrated with the 6–11 April 2025 event and independently validated with the 11–16 March 2026 event. A factorial experiment comprising two models, five cell sizes (15–55 ft), and five flow conditions (the observed event and Q50, Q100, Q200, and Q500 design floods) resulted in 50 simulations. The study simultaneously evaluates resolution sensitivity within each calibrated model and inter-model convergence under a common scenario matrix. Inundation extent was most resolution-sensitive during the lower-magnitude observed event, whereas inter-model extent differences decreased as flood magnitude increased. Outlet hydrographs and peak discharges were comparatively stable, but local water-depth distributions became less consistent as the mesh was coarsened, particularly in SMS-TUFLOW. HEC-RAS retained greater depth consistency, plausibly because its sub-grid property tables preserve cell-scale volume and conveyance information. Overall, mesh adequacy was output- and solver-specific, indicating that grid selection should be based on the intended hydraulic output and supported by explicit spatial stability testing.

Sci doi: 10.3390/sci8080218

Authors: Manuela Lageiro Jaime Fernandes Ana C. Marques Cristina Roseiro Ana Rita F. Coelho

The tropical fruits velvet tamarind, baobab, and African locust beans are used as foods and in beverages for medicinal purposes. They are important sources of nutrients and bioactive compounds. This research focused on the bioactive composition in the fruit pulps and seeds of these African plant species. Spectrophotometric analysis of the total phenolic and flavonoid contents, the antioxidant capacity (DPPH, FRAP, and ABTS assays), the quantification and profile of phenolic compounds, hydrosoluble vitamins B and C (HPLC), and fatty acids (GC-FID), and the toxicological analysis of amygdalin (HPLC) and elements (XRF) were assessed. The fruit velvet tamarind exhibited the highest TPC, TFC, and antioxidant capacity, with seed content higher than pulp, primarily due to naringin (54.45 mg/100 g). Baobab presented the second highest TPC and TFC content and antioxidant capacity, with pulp being higher than seed, mainly due to vitamin C (79.97 mg/100 g) and rutin (47.92 mg/100 g). Several bioactive compounds were quantified, such as vitamin C, various hydroxycinnamic acids (caffeic, p-coumaric, chlorogenic, ferulic), hydroxybenzoic acids (gallic, vanillic), and polyphenols (quercetin, catechin, epicatechin, rutin, naringin, procyanidin, kaempferol), as well as fatty acids from the ω-3 and ω-6 series. Amygdalin was not detected in any seeds, and Cd content was higher than 0.05 mg/kg of dry weight.

Sci doi: 10.3390/sci8080217

Authors: Vol Oberemok Kate Laikova Nikita Gal’chinsky

‘Genetic zipper’ technology—based on CUAD (Contact Unmodified Antisense DNA) biotechnology, briefly CUADb—represents a step forward in eco-friendly pest control. This unique innovative approach is based on a fundamentally new biological mechanism—a two-step DNA containment (DNAc) mechanism. DNAc employs short, unmodified antisense DNA molecules to selectively degrade target pre-rRNA and/or rRNA in insect pests recruiting up-regulated RNase H1 and RT-RNase H during DNAc, disrupting protein synthesis and causing the down-regulation of kinases due to ATP insufficiency and ultimately leading to high mortality rates. Demonstrating exceptional speed and precision, this technology enables the design of effective and selective DNA pesticides (oligonucleotide pesticides) for no less than 15% of known insect pests in a single day. In this review, we highlight the simplicity and global applicability of this technology using case studies involving 12 economically significant pest species, including hemipterans and one spider mite, from five continents. These oligonucleotide pesticides, computationally predicted via the DNAInsector web tool, are supposed to offer 80–90% efficacy against target pests within one–two weeks under laboratory or field conditions. Their action is primarily non-systemic, requiring direct contact. Oligonucleotide pesticides are environmentally safe, biodegradable, and highly specific, reducing risks to non-target organisms. The ‘genetic zipper’ technology not only provides a powerful tool for researchers and practitioners but also opens a new era in DNA-programmable pest management, where personalized, algorithm-driven pesticides can be easily created and applied for sustainable agriculture.

Sci doi: 10.3390/sci8080216

Authors: Luis Fernando Grisales-Noreña Fiderman Machuca-Martínez Oscar Danilo Montoya

The large-scale integration of photovoltaic generation into distribution grids has introduced significant operational challenges, including voltage excursions, reverse power flows, and increased variability. Battery energy storage systems (BESSs) offer a versatile solution by providing coordinated active- and reactive-power support. However, their scheduling in active distribution networks is challenging because of the non-convex alternating-current (AC) power-flow equations, the nondifferentiability of battery-degradation modeling, and uncertainty in renewable generation and demand. This paper proposes a two-stage methodology for the day-ahead operation of BESSs in ADNs. In the first stage, parallel particle swarm optimization (PPSO) determines the hourly active- and reactive-power schedules of the BESS units. In the second stage, a matrix-based multi-period AC power flow based on successive approximations evaluates the schedules and verifies voltage, thermal, converter-capability, and state-of-charge (SoC) constraints. A rainflow-counting degradation model is incorporated into the objective function to account for cycling and calendar aging costs. The methodology is assessed through ablation analyses comparing active-power-only and coordinated P–Q dispatches, degradation-unaware and degradation-aware scheduling, and serial and parallel PSO implementations. It is validated on modified 33-, 69-, and 136-node systems under deterministic and uncertainty-based operating conditions, including 100 demand and PV-generation scenarios. PPSO is compared with parallel versions of the adaptive Jaya algorithm (AJAYA), genetic algorithm (GA), multi-verse optimizer (MVO), salp swarm algorithm (SSA), grey wolf optimizer (GWO), and vortex search algorithm (VSA), using operating-cost reduction, computational time, solution variability, feasibility indicators, BESS lifetime, and weekly cost analysis. Additionally, exact one-sided Wilcoxon signed-rank tests with Holm adjustment are used to assess the statistical significance of the economic differences between PPSO and the benchmark methods. Results show that PPSO provides the lowest or most competitive operating costs and the shortest computational time in the evaluated cases, while all network and storage constraints remain satisfied.

Sci doi: 10.3390/sci8080215

Authors: Brent Tai Ajay Mittal Chijioke Okonkwo Yaroslav Zuyev Derek Snyder

Background: Acute kidney injury (AKI) is a common complication following cardiac surgery and is associated with increased morbidity and mortality. Contemporary national estimates of its clinical and economic burden in the United States remain limited. Methods: We conducted a retrospective cross-sectional study using the Nationwide Inpatient Sample (NIS) for 2022–2023. Adult hospitalizations undergoing coronary artery bypass grafting (CABG), valve surgery, or combined CABG and valve surgery were identified using ICD-10-PCS codes. Hospitalizations with end-stage kidney disease were excluded. The primary exposure was AKI. Outcomes included in-hospital mortality, length of stay (LOS), non-home discharge, and hospitalization cost. Survey-weighted multivariable regression models were used to evaluate the independent association between AKI and study outcomes. Results: The final cohort included 133,801 hospitalizations, representing an estimated 669,005 cardiac surgery hospitalizations nationally. AKI occurred in 123,240 weighted hospitalizations (18.4%). Compared with hospitalizations without AKI, those with AKI had higher unadjusted mortality (7.21% vs. 0.71%), longer LOS (14.4 vs. 6.3 days), greater rates of non-home discharge (68.1% vs. 41.2%), and higher hospitalization costs ($97,452 vs. $56,253). After adjustment for demographic, socioeconomic, clinical, and procedural characteristics, AKI remained independently associated with in-hospital mortality (adjusted odds ratio [aOR] 9.91, 95% confidence interval [CI] 8.95–11.00), non-home discharge (aOR 2.52, 95% CI 2.42–2.63), prolonged LOS (adjusted rate ratio [aRR] 1.88, 95% CI 1.85–1.91), and increased hospitalization costs (cost ratio 1.59, 95% CI 1.56–1.61). AKI was associated with an adjusted incremental cost of $33,497 per hospitalization, corresponding to an estimated national attributable cost burden of $4.13 billion during the study period. Conclusions: AKI complicates nearly one in five cardiac surgery hospitalizations in the United States and is associated with substantially increased mortality, healthcare utilization, and hospitalization costs. These findings highlight the significant clinical and economic burden of cardiac surgery–associated AKI and support continued efforts to improve prevention, risk stratification, and perioperative management.

Sci doi: 10.3390/sci8080214

Authors: Kullaya Poomithorn Supaporn Pengrawa Ponusa Songtipya Krisana Nilsuwan Soottawat Benjakul Thummanoon Prodpran

This study developed surface-functionalized polylactic acid (PLA) films by depositing electrospun gelatin/chitosan (GE/CH) nanofibrous coatings formulated with and without bioactive phenolic compounds (curcumin and anthocyanin). Evaluating various polymer blending ratios and operational parameters revealed that a GE:CH ratio of 7:3 (v/v), processed at an applied voltage of 25 kV and a collector speed of 300 rpm, provided the most stable electrospinning behavior among those tested, yielding a uniform nanoscale fibrillar network. The deposition of this selected GE/CH layer onto the PLA substrate significantly improved the composite bilayer film’s tensile strength and oxygen barrier properties, although it increased macroscopic opacity. Furthermore, active coatings containing 0.25% and 0.50% (w/w) curcumin or anthocyanin were successfully processed. This 0.50% level was the maximum concentration quantitatively evaluated in the present study, as preliminary observations suggested poorer processability at higher concentrations, which induced premature gelation and needle clogging. While interactions (mostly non-covalent physical interactions) associated with the phenolic compounds synergistically reinforced the mechanical rigidity and reduced the water vapor permeability of the bilayer films, the macroscopic bioactive functionality was limited. The low loading concentrations, coupled with severe optical masking and restricted aqueous extraction, resulted in moderate antioxidant activity (10.31–30.46% DPPH radical inhibition) and no visually detectable halochromic (pH-responsive) color changes. Overall, these findings highlight a significant functional trade-off in the design of active coatings, where structural and mass transport barrier enhancements are achieved, but macroscopic bioactive functionality is constrained, underscoring the necessity for advanced encapsulation strategies in future developments.

Sci doi: 10.3390/sci8080213

Authors: Michel Planat

This paper proposes that the origin of life may be understood as a cascade of dynamical integration events governed by Painlevé transcendental equations—a class of nonlinear differential equations arising widely in physics, from quantum mechanics to general relativity. Four prebiotic subsystems (mineral catalysts, information polymers, free-energy transducers, and lipid membranes) undergo progressive coupling, tracing a stepwise cascade (PVI→PV→PIIID6→PIIID7→PIIID8) along the Chekhov confluence diagram, culminating in the LUCA (Last Universal Common Ancestor). Each step corresponds to a specific biochemical integration event. The Painlevé framework is explicitly phenomenological: it classifies dynamical regimes of subsystem coupling rather than deriving biochemical mechanisms from first principles. A central quantitative feature is a characteristic separation parameter (Δmin≈0.15) between effective subsystem rates (ri=τi−1), with oscillation frequencies scaling as ω∝r1/2Δ−1/2. Several prospective test systems are identified, including the Belousov–Zhabotinsky reaction, the formose reaction, and chemically monitored extreme environments. Existing literature is used to define operational protocols, but no retrospective dataset is treated here as an independent validation of the proposed value of Δmin≈0.15. In particular, the formose calculation below is explicitly an illustrative model calculation whose parameters remain to be measured. The framework offers a potential unification of the RNA World, Metabolism-First, and Protocell theories as sequential stages of a single cascade; provides an indicative timeline (4.4–3.5 Ga) anchored to geological constraints; and makes quantitative, falsifiable predictions. A dedicated Scope and Limitations section discusses what the approach does and does not claim.

Sci doi: 10.3390/sci8080212

Authors: Daniel Sanin-Villa Kevin Alexander Leyton-Valencia Luis Fernando Grisales-Noreña

Battery energy storage systems can reduce the operating cost of active distribution networks while supporting voltage control through their power electronic converters. This paper develops an application-specific multi-operator Differential Evolution (DE) framework for the coordinated active and reactive power scheduling of distributed battery energy storage systems in radial distribution networks with photovoltaic generation. The optimization model minimizes the daily operating cost associated with conventional energy supply, photovoltaic and storage operation and maintenance, and battery degradation. Candidate schedules encode hourly active and reactive power references for three storage converters, producing a 144 dimensional decision vector for a 24 h horizon. Each candidate is repaired to satisfy active power, state of charge, terminal energy, and converter apparent power limits before being evaluated through an alternating current power flow based on matrix successive approximations. The search framework generates three competing trial schedules per target individual by combining established best-guided, random, and current-to-random DE mutation families with a discrete parameter pool, a common feasibility-repair operator, and greedy selection after AC network evaluation. The method is tested on modified 33-node and 69-node active distribution networks and compared with AJAYA, genetic algorithm, multiverse optimizer, and particle swarm optimization. In the deterministic 33-node case, Differential Evolution obtains the lowest best cost, USD 6846.206, and the largest best cost reduction, 2.1838 percent. The scenario study performs separate deterministic optimizations for pre-generated operating realizations and is therefore interpreted as a scenario-conditioned sensitivity assessment rather than as stochastic or robust optimization of one here-and-now schedule. In this assessment, DE achieves the largest average savings: 2.3487 percent in the 33-node network and 2.9314 percent in the 69-node network. Voltage magnitudes, branch loading, converter ratings, and cyclic state of charge constraints are satisfied in all evaluated cases. The results identify the proposed framework as a competitive day-ahead solver within the evaluated cases, while no claim of global optimality or universal superiority over alternative optimizers is made.

Sci doi: 10.3390/sci8080211

Authors: Oyinlola Oluwunmi Olaokun Folakemi Damilola Olaokun

Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α), encoded by PPARGC1A, plays an important role in the regulation of mitochondrial biogenesis, metabolism, and energy balance. Alterations in PGC-1α function have been found in type 2 diabetes mellitus (T2DM) and neurodegenerative diseases (NDs), but molecular similarities in these pathologies have not been comprehensively studied yet. The current systematic review provides a synthesis of preclinical data on the role of PGC-1α in relation to T2DM and NDs. This study was performed in accordance with the PRISMA 2020 statement. PubMed, Scopus, Web of Science and ScienceDirect were screened for studies published between January 2015 and March 2025. Preclinical studies, including in vivo animal studies with and without in vitro studies, were evaluated for risk of bias according to the SYRCLE checklist and modified CAMARADES checklist. Due to the high heterogeneity, data were qualitatively synthesized. Eleven preclinical studies met the inclusion criteria. Decreased PGC-1α signalling was indicated to be linked to mitochondrial dysfunction, oxidative stress, glucose metabolism impairment, insulin resistance and neurodegeneration via AMPK, SIRT1, CREB, FOXO, PPARγ, Parkin/PARIS, and BDNF pathways. Pharmacological interventions and exercise support increased PGC-1α signalling, potentially improved mitochondrial functions and metabolism and facilitated neuronal survival. Though most studies had a low risk of bias, methodological limitations were quite prevalent. PGC-1α is a key molecular mediator between metabolic dysfunction and neurodegeneration and is a potential target for intervention. However, more rigorous translational and clinical studies are required for the validation of results. This systematic review has not been prospectively registered.

Sci doi: 10.3390/sci8080210

Authors: Tiziana Pastore Giovanna Trevisi Michaela Remešová Vendula Bednaříková Ladislav Čelko Marek Doubrava Amirhossein Pakseresht Omid Sharifahmadian Michal Krbata Davide Costa Michele Poncini Davide Faverzani

This study explores the development of low-friction, water-based coatings tailored for industrial applications in pharmaceutical glass packaging. The study focuses on scalable deposition strategies to obtain durable low-friction coatings suitable for industrial implementation. To balance mechanical performance and application efficiency, two different application approaches based on a two-component coating (aminosilane primer and lubricant) were investigated. In the first case, the coating is deposited in two steps, while in the second, a single deposition step is used. Characterization through contact-angle measurements and X-ray photoelectron spectroscopy confirmed successful deposition of the primer on the glass surface. Scratch resistance tests revealed an increase in the critical load for fracture initiation from 4.5 N for uncoated glass to 6.5 N for the best-performing coating, indicating improved resistance to surface damage. Friction performance was assessed via tribological tests, which demonstrated that the primer–lubricant coatings achieved the lowest coefficient of friction (approximately 0.2), compared with uncoated glass (stabilizing at approximately 0.3 after an initial value of 0.5) and lubricant-only coatings (approximately 0.4–0.5), confirming the beneficial role of the primer in the coating system. Representative profilometry measurements indicated sub-micrometric coating thicknesses, while UV–Vis measurements confirmed that the coatings preserved the high optical transparency of the glass substrate, with average visible transmittance values above 90%. Furthermore, the successful implementation of the coating using an automated spray system demonstrates its potential for scalable industrial production. These findings support the potential of environmentally sustainable water-based coatings for pharmaceutical glass packaging by combining improved mechanical performance with preserved optical transparency and compatibility with scalable spray deposition.

Sci doi: 10.3390/sci8080209

Authors: Rodrigo Carrasco Matías Escobar-Aguirre Esteban G. Figueroa Patricio Acevedo Martín Armijo Nicolás Lobos Fernando Verdugo Rodrigo L. Castillo

Breast cancer is associated with systemic inflammation and increased cardiovascular risk, and anthracycline chemotherapy may contribute to persistent myocardial injury. This study aimed to evaluate acute changes in inflammatory cytokines and plasma redox status after the first anthracycline cycle and to assess long-term ventricular function after 10 years in women with breast cancer. We conducted a prospective study of 17 patients with breast cancer treated with anthracycline-based chemotherapy at Salvador Hospital, Santiago, Chile. Plasma cytokines were measured at baseline (day −7) and on day +3 after the first cycle using a MILLIPLEX Luminex® assay. Echocardiographic assessment of left ventricular systolic and diastolic function, together with oxidative stress parameters, was performed at baseline and after 10 years of follow-up. Anthracycline exposure was associated with an acute increase in several cytokines related to inflammatory and vascular remodeling, including EGF, eotaxin, MCP-1, and VEGF. In addition, markers of redox imbalance suggested an acute pro-oxidant response after treatment. At long-term follow-up (10 years), left ventricular ejection fraction (LVEF) remained within the normal range in all patients. However, integrative echocardiographic assessment revealed impaired left ventricular relaxation, evidenced by significant reductions in the mitral inflow E/A ratio, and septal and lateral e′ velocities with respect to baseline, despite preserved estimated filling pressures. These alterations were accompanied by persistent oxidative stress, reflected by persistently elevated levels of lipid peroxidation markers, such as in vivo 8-isoprostanes. In this pilot cohort, anthracycline chemotherapy induced an early inflammatory and oxidative response that was not associated with overt long-term systolic dysfunction but was accompanied by persistent biochemical and diastolic alterations. These findings support the concept of a long-term subclinical cardiotoxic phenotype and highlight the potential value of combining echocardiographic assessment with circulating redox and inflammatory biomarkers to improve long-term cardiovascular surveillance in breast cancer survivors.

Sci doi: 10.3390/sci8080208

Authors: George Kyriakarakos Maria Lampridi Charisios Achillas Amine Chekireb Levon Gevorkov Claus Aage Grøn Sørensen Dionysis Bochtis

Renewable-powered agricultural water systems are often assessed as solar-pumping devices, but their sustainability depends on a service chain linking crop-water demand, hydraulic duty point, power electronics, storage, water quality, governance, operation and end-of-life management. This structured integrative review synthesizes peer-reviewed and practice-oriented evidence on photovoltaic pumping, hybrid renewable irrigation, grid-interactive pumps, micro-hydro assistance and renewable-powered brackish-water reverse osmosis (PV-RO). Evidence was screened across four source families and coded by service function, energy architecture, hydraulic duty and dominant sustainability pathway; recurring combinations were consolidated using explicit separation and merge rules. It develops an archetype-based screening framework for ex ante appraisal of irrigation, desalination and circularity risks. Seven technology-service archetypes are identified: direct PV pumping, PV-to-tank pumping, PV with electrical buffering, grid-interactive PV pumping, PV–wind hybrid irrigation, micro-hydro-assisted irrigation and PV-RO water making. The framework links each archetype to its operating envelope, evidence maturity, enabling subsystems, sustainability pathways, minimum indicators and ordinal triggers for deeper due diligence. Hydraulic storage is usually the lowest-regret reliability buffer for open-field irrigation, whereas batteries are justified mainly when pressure stability, fertigation timing or night-time operation has high agronomic value. PV-RO is a distinct water-making archetype and is environmentally defensible only where feed-water characterization, energy recovery, pretreatment, product-water agronomy, membrane management and permitted concentrate disposal are embedded in design. Two synthetic applications demonstrate archetype selection and due-diligence escalation. Responsible deployment requires service-oriented screening that integrates hydraulic design, groundwater governance, procurement quality assurance, circularity obligations and social inclusion before field implementation.

Sci doi: 10.3390/sci8080207

Authors: Gowthami Mahendran Piriyankan Kirupaharan

Proteomics has emerged as a powerful tool for advancing our understanding of brain disorders by enabling large-scale characterization of protein expression, post-translational modifications, and interaction networks. Neurological conditions are often characterized by complex and dynamic molecular changes that are not fully captured by traditional diagnostic approaches. Proteomic technologies, particularly mass spectrometry-based and affinity-based methods, offer the ability to identify disease-specific protein signatures and elucidate underlying pathophysiological mechanisms, including neurodegeneration, neurodevelopment and neuroinflammation and alterations happening to the extracellular matrix and body fluid homeostasis. In recent years, artificial intelligence has emerged as a powerful tool to proteomics, enabling improved analysis of complex biological datasets. This integration has significantly enhanced the discovery of biomarkers for early diagnosis, disease stratification, and monitoring of therapeutic responses. Thus, cerebrospinal fluid and blood-based proteomic analyses have revealed promising candidates for neurological diseases. This review summarizes current advances in proteomics across a range of brain disorders, highlighting key molecular pathways, biomarker discovery efforts, and evolving clinical applications. Furthermore, it outlines future directions, including the application of machine learning for improved biomarker identification and precision medicine.

Sci doi: 10.3390/sci8080206

Authors: Hiedar Alyami Tharwat Gamal Eldin Shahanas Chathoth Waleed Albaker Ahmed Alsunni Abdullah Bamosa Saad Asaad Mohammed Al-Hariri

Type 2 diabetes mellitus (T2DM) is increasingly managed through lifestyle measures, yet the value of pairing structured exercise with a well-characterised phytotherapeutic adjunct remains poorly defined. We examined whether adding Nigella sativa (NS) to combined aerobic and resistance exercise (EX) yields greater improvements in metabolic, inflammatory, and oxidative-stress markers than exercise alone in men with T2DM. In this three-arm randomised controlled trial, 90 sedentary, non-smoking men (aged 40–60 years; T2DM duration > 2 years; glycated haemoglobin (HbA1c) 6.5–9.0%; body mass index (BMI) < 30 kg/m2) were allocated 1:1:1 to control, EX, or EX + NS (2 g/day; n = 30 per group) for four weeks. Fasting blood samples obtained before and after the intervention were used to measure glycaemic indices (fasting blood glucose (FBG) and fructosamine), a lipid panel (total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c)), inflammatory markers (tumour necrosis factor-α (TNF-α), interleukin-4 (IL-4), and adiponectin), and oxidative-stress markers (thiobarbituric acid–reactive substances (TBARS) and catalase activity). Outcomes were analysed by two-way repeated-measures analysis of variance (time × group), with partial eta-squared (ηp2) and 95% confidence intervals (CIs) reported for all principal effects. Relative to control, both exercise arms significantly improved body composition and glycaemic, lipid, inflammatory, and oxidative-stress profiles (all p < 0.05). The time × group interaction was significant for every outcome except systolic and diastolic blood pressure and triglycerides. Compared with EX alone, EX + NS produced significantly greater improvements in FBG, HDL-C, TBARS, and catalase activity; changes in fructosamine, total cholesterol, TNF-α, IL-4, and adiponectin favoured EX + NS directionally but did not reach significance after correction for multiple comparisons. Triglycerides, LDL-C, and blood pressure did not differ between the two exercise arms. These hypothesis-generating findings support NS as a candidate adjunct to structured exercise for selected metabolic and redox outcomes, and warrant larger, longer, sex-balanced confirmation.

Sci doi: 10.3390/sci8080205

Authors: Tanvir Hossain Sharmeen Hyder Ikrema Hassan

Electrochemical treatment can provide contaminant destruction, phase separation, ionic polishing, and resource recovery; however, performance cannot be judged by removal efficiency alone. This structured critical review compares electro-oxidation (EO), electrocoagulation (EC), electro-Fenton (EF), electrodialysis (ED), electrodeionization (EDI), capacitive deionization (CDI), flow-electrode CDI (FCDI), and bioelectrochemical systems (BES) in municipal wastewater, industrial effluents, sludge-related applications, and treatment side-streams. Searches of Scopus, Web of Science Core Collection, and PubMed were updated to 22 July 2026, and the evidence was assessed according to treatment function, wastewater realism, operating mode, durability, residual fate, energy and cost boundaries, resource recovery, and life cycle implications. Recent advances include porous flow-through anodes, oxygen-efficient cathodes, selective ion separation materials, and pilot BES configurations. However, scale-up remains constrained by electrode aging, by-products, sludge and concentrate management, oxygen transfer, fouling, competing ions, internal resistance, biological instability, and incomplete long-term economic and environmental evidence. The quantitative results show that the energy, cost, and carbon outcomes depend strongly on the treatment function and system boundary. The evidence for sludge and biosolids is less mature than that for liquid wastewater. Therefore, electrochemical technologies are best positioned as function-specific units within hybrid treatment trains rather than as universal replacements for conventional treatments.

Sci doi: 10.3390/sci8080204

Authors: Dorothea West Pearl Miller Jerica Wilson Hai-Feng Ji

Chronic inflammation is closely associated with cancer progression through the promotion of angiogenesis and tumor-supportive signaling pathways. Tumor necrosis factor-α–induced protein 8-like 2 (TIPE2) regulates leukocyte polarization through phosphoinositide transport and represents a promising therapeutic target for solid tumors. However, the large hydrophobic cavity of TIPE2 presents a significant challenge for inhibitor design. In this work, a quadrant docking grid system was developed to enable fragment docking in defined regions of the binding cavity, facilitating fragment linking with minimal overlap and maximal spatial coverage. To our knowledge, this is the first application of a quadrant grid docking strategy for fragment-based inhibitor design. Fragments were screened using AutoDock Vina 1.2.3 and selected based on both binding affinity and predicted aqueous solubility. The highest-binding linked compounds, F1-F12 and F1-F13, exhibited binding affinities of −12.4 and −11.5 kcal mol−1, respectively. Rational modifications yielded compounds MF112 and MF113 with improved predicted ADME properties while maintaining strong binding affinities of −11.7 kcal mol−1. Molecular dynamics simulations demonstrated stable binding complexes over 5 ns trajectories, with RMSD stabilization after approximately 1–2 ns. These results demonstrate that quadrant-based fragment docking provides an effective strategy for designing inhibitors targeting proteins with large binding cavities and provides promising lead compounds for the development of TIPE2 inhibitors.

Sci doi: 10.3390/sci8080203

Authors: Leonardo Chamorro-Anaya Adriana Quintana-Canabal Juan Cardenas-Guerra Jorge Romero-Polo Rosa Baldiris-Avila Alfredo Montes-Robledo

Maintaining genetic diversity is essential for the long-term conservation of migratory freshwater fishes facing increasing anthropogenic pressures. Prochilodus magdalenae (bocachico), one of Colombia’s most economically and ecologically important freshwater fish species, supports artisanal fisheries, regional food security, and the livelihoods of local communities but has experienced population declines associated with overfishing, habitat degradation, and hydrological alteration. This study evaluated the genetic diversity and population structure of wild and hatchery-associated P. magdalenae from northern Colombia using sequence-based microsatellite genotyping (SSR-GBS). Sixty individuals (48 wild adults from three localities in the Department of Sucre and 12 hatchery-associated fingerlings from Cereté) were genotyped at seven polymorphic microsatellite loci. Wild populations retained high genetic diversity and exhibited weak but significant genetic differentiation (FST = 0.017, p = 0.003), with only 2% of the molecular variation distributed among populations and high estimated gene flow (Nm = 14.899). Hatchery-associated fingerlings consistently exhibited lower allelic richness while maintaining relatively high heterozygosity. Rarefied allelic richness did not differ significantly among populations (Kruskal–Wallis, p = 0.400); however, hatchery-associated fish consistently showed lower values, highlighting the value of allelic richness as a complementary indicator for evaluating hatchery-produced stocks. These findings provide a genetic baseline for the conservation of P. magdalenae and support incorporating routine genetic monitoring into supportive breeding and restocking programs.

Sci doi: 10.3390/sci8080202

Authors: Han Nah Chung Hyang Yeon Kim Ji Woo Kim Jung Dae Lee Gi-Wook Hwang Kyu-Bong Kim

m-phenylenediamine sulfate (mPDS) is used as an oxidative hair dye intermediate and is permitted in cosmetic products in Korea at concentrations up to 3%. However, quantitative data on its dermal absorption remain limited. This study evaluated the in vitro dermal absorption of mPDS using a Franz diffusion cell system. An LC–MS/MS method was developed and validated for quantifying mPDS in receptor fluid, skin, wash solution, and tape-stripped stratum corneum samples. The method showed excellent linearity (r2 ≥ 0.9951), with overall accuracy of 85.9–110.9% and precision of 0.7–10.9%, meeting regulatory validation criteria. Dermal absorption studies were performed using mini-pig dorsal skin that satisfied integrity requirements. Oxidative hair dye formulations containing mPDS at 0.7% or 2.1% were applied at 10 mg/cm2, and receptor fluid samples were collected for 24 h. Most of the applied dose was recovered in the wash fraction after removal at 30 min. Dermal absorption was 2.90 ± 1.27 and 4.75 ± 2.06 µg/cm2 for 0.7% and 2.1% mPDS, respectively. These findings provide quantitative dermal absorption data to support regulatory exposure characterization of mPDS.

Sci doi: 10.3390/sci8080201

Authors: Sampurna Mukherjee Rakesh Ghosh Arunava Goswami Volker Hessel Sutanuka Mitra

Janus nanoparticles (JNPs), named after the bi-faced Roman God has emerged as a hot topic in the present era of nanoscience because of their chemical, structural and physical uniqueness. These particles stand out in the crowd as they are composed of two or more faces with contrasting functional properties in the same molecule. The need for two or more functions in a single molecule that can be used in chemical, biological or physical fields, such as delivering drugs combined with imaging, is the need of the hour and JNPs open gates for addressing this issue as these self-tailored particles have found their application in various in vivo and in vitro domains. However, despite their vivid application, a larger sector of application still needs to be explored. Synthesis methods involve masking, self-assembly and microfluidics, and comparative analysis of these methods, listing their pros and cons, would assist in overcoming the difficulties in the commercialisation of these particles. Moreover, the systematic analysis of differences in their structure with reference to their functionality and characterisation methods would lead us to a better understanding of the subject. This review discusses the various synthesis strategies and their comparison, the anisotropic nature of the JNPs conferring various distinguished properties, their application in emulsion stabilisation, bio-imaging, drug-targeting, drug-delivery, and biosensing domains, the characterisation methods involved, challenges and future aspects. The future aspects section maps a few hypotheses that might be useful in expanding the horizons of usage. The novelty of this review lies in the critical analyses of the synthesis methods and characterisation. Each type of JNP has been analysed for its advantages and limitations, and probable hypotheses to address the existing challenges have been jotted down.

Sci doi: 10.3390/sci8080200

Authors: Francesco Tajani Pierluigi Morano Felicia Di Liddo Marco Locurcio

The present research investigates the relationship between urban regeneration initiatives and residential real estate market dynamics by assessing market price appreciation associated with the factors most commonly considered in housing transactions. The study focuses on the Esquilino district in the city of Rome (Italy) with particular attention to the redevelopment of Piazza dei Cinquecento, the major public space located in front of Roma Termini railway station. The intervention aims to improve urban accessibility, reduce traffic congestion, and enhance public space quality through a new spatial configuration and the creation of a tree-lined area. The objective of the study is to verify whether, and to what extent, the ongoing regeneration project has influenced residential property values. To achieve this goal, an econometric analysis is implemented to quantify the contribution of different housing and locational attributes to residential asking prices and to identify the variables that significantly affect value formation within the local market. Given that the initiative is still in progress and approaching completion, the analysis adopts a diachronic perspective by comparing two distinct temporal stages: the ante project phase (second half of 2021) and the in itinere phase (first half of 2025). Building on the findings of a previous pre-intervention study, the research systematically examines changes in market behaviors over time, with the dual purpose of identifying variations in price determinants and analyzing the associations between the current urban transformations and the residential real estate market. The results indicate a substantial stability in the main determinants of residential property prices across the two periods, suggesting that the regeneration initiative has not yet been fully capitalized into market behaviors. However, variations in the contribution and functional relationships of some spatial variables highlight preliminary signs of market adjustment during the ongoing transformation process. The study highlights the importance of monitoring for assessing how urban regeneration processes are progressively incorporated into real estate market dynamics. The proposed framework provides a transferable tool for evaluating regeneration processes in different urban contexts, supporting evidence-based decision-making and the comparative assessment of urban transformation strategies.

Sci doi: 10.3390/sci8080199

Authors: Taylor D. Radtke William J. Hanney Abigail W. Anderson Morey J. Kolber

Background: Prolonged smartphone use has been proposed to influence postural alignment and pulmonary function; however, continuous relationships among these variables remain uncertain in young adults. Methods: Thirty healthy university students completed assessments of device-recorded average daily smartphone screen time over the preceding seven days, craniovertebral and shoulder-C7 angles during natural standing, forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and positive and negative affect. Eight Spearman correlations were evaluated using 5000-resample bootstrap confidence intervals and Holm adjustment for multiple comparisons. Separate linear regression models examined smartphone screen time as a predictor of FVC and FEV1 after adjustment for biological sex and height. Results: None of the primary correlations was statistically significant after Holm adjustment. The largest estimate was observed between smartphone screen time and FVC (ρ = −0.242, 95% CI −0.540 to 0.101; Holm-adjusted p = 1.000). In adjusted models, smartphone screen time was not significantly associated with FVC (B = −0.054 L per additional hour/day, 95% CI −0.127 to 0.019; Holm-adjusted p = 0.280) or FEV1 (B = −0.027 L per additional hour/day, 95% CI −0.123 to 0.068; Holm-adjusted p = 0.561). Secondary exploratory analyses identified an inverse correlation between shoulder-C7 angle and negative affect and positive correlations between FVC and height and body weight. Conclusions: Statistically significant primary associations among seven-day smartphone screen time, standing postural alignment, and pulmonary function were not detected. These findings do not establish that the variables are independent and should be interpreted cautiously given the small sample and wide confidence intervals.

Sci doi: 10.3390/sci8080198

Authors: Valentina Rivaldo Reyes Paulín Amaya Sotillo Yeimmy Peralta-Ruiz Teresa Corrales Diana Paola Navia-Porras Carlos David Grande-Tovar

The growing demand for sustainable alternatives to conventional plastic packaging has promoted the development of biodegradable materials derived from agro-industrial residues. In this study, starch extracted from green banana peel waste was used to produce biodegradable starch–glycerol films for food-packaging applications. Nine film formulations (PPM1–WP6) were prepared using starch obtained through different extraction approaches, including parenchyma-only and whole-peel processing, with and without sodium metabisulfite treatment. The extracted starches were characterized through proximate composition analysis, Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and differential scanning calorimetry (DSC). The resulting films were evaluated for structural, thermal, mechanical, and barrier properties using FTIR, X-ray diffraction (XRD), SEM, dynamic mechanical analysis (DMA), water vapor permeability (WVP), and water solubility (WS). The films exhibited characteristic starch functional groups, semicrystalline structures, thermal degradation temperatures ranging from 283 to 302 °C, and viscoelastic behavior associated with plasticized polymeric matrices. Increasing glycerol concentration improved film flexibility, whereas sodium metabisulfite treatment enhanced mechanical strength. Films prepared from whole-peel starch showed higher WVP and WS values than those obtained from parenchyma starch. A randomized factorial design was used to optimize film performance for maximum tensile strength and minimum water solubility. Among the evaluated formulations, PPM2 (4% starch with sodium metabisulfite treatment) was identified as the optimal formulation and was statistically validated. These findings demonstrate the potential of green banana peel waste as a sustainable source of starch for developing biodegradable films with promising applications in food packaging.

Sci doi: 10.3390/sci8080197

Authors: Claus Jacob

During the last decade, there has been an emergence of isolated scientific studies that have been selectively cited to support claims apparently contradicting the broader evidence base, thus opening the door for scepticism on the one hand and “evidence-based” conspiracies on the other. This dangerous development in science and its wider perception within the population have been facilitated by the rapid growth and digital discoverability of the scientific literature, making it easier to search selectively for the publications that lend support to any, and often the most bizarre, hypotheses. This bias of selective reading of literature constitutes a major challenge for modern science and scientific communication. AI-based tools may help broaden evidence retrieval, yet their outputs require transparent validation and expert oversight to minimise selection bias and to ensure reliable interpretation.

Sci doi: 10.3390/sci8080196

Authors: Kevin Böhm Shahrzad Safinazlou Jean-Philippe Reichheld Muhammad Jawad Nasim Claus Jacob

Selenium (Se) is an essential trace element in animals and humans, usually acquired via nutrition and thus often dependent on the Se content of the soil from where the food crops originate. The uneven distribution of Se in soils may therefore result in regional Se deficiencies, often further exacerbated due to climate change and its subsequent effects on Se speciation (i.e., the chemical forms in which Se occurs) and the element’s mobility. In recent years, biofortification has emerged as a promising approach to address this issue. This review provides an overview of how climate-driven changes in Se cycling influence soil Se availability and discusses the resulting implications for plant resilience, crop biofortification and dietary Se supply. Although plants do not require Se per se, an increased Se content of plants may improve their yields, antioxidant defences, and tolerance to various—climate (change)-related—stresses such as drought, salinity, and oxidative stress at low concentrations. So far, selenite (SeO32−) and selenate (SeO42−) are the most commonly employed Se species in agriculture, often via foliar and soil applications, and due to their excellent solubility in water are subject to leaching or other weather-related limitations. Alternative Se compounds, such as Se nanoparticles (SeNPs) and selenium sulfide (SeS2) present promising approaches that may enable a more controlled Se release in soils. Nonetheless, their long-term agronomic performance and environmental behaviour require further investigations.

Sci doi: 10.3390/sci8080195

Authors: Theetach Rabablert Amonnat Kaewnok Chitnarong Sirisathitkul Yaowarat Sirisathitkul

Diagnosing skin diseases remains a clinical challenge due to the visual similarity among diverse dermatological conditions. This study presents a prototype deep learning–powered system for multi-class dermatological screening, implemented through a cloud-based architecture and accessed via a smartphone interface. A pre-trained Convolutional Neural Network (CNN), EfficientNetV2B3, was fine-tuned on a composite dataset encompassing nine disease categories. The model achieved promising performance, with an accuracy of 0.87, precision of 0.87, recall of 0.87, and an F1 score of 0.86, indicating its potential reliability for automated classification. Prototype validation was conducted using a cloud API (Google Cloud Storage + PostMan) to verify the inference pipeline and user interaction. While the current implementation demonstrates the feasibility of cloud-based dermatological screening, real-device mobile performance metrics such as latency, model size, and memory consumption remain future work. Users can capture or upload skin images, which are processed to generate preliminary diagnostic feedback, including symptom descriptions and general treatment information. While not intended to replace professional medical evaluation, the prototype serves as a proof-of-concept tool for initial screening and early intervention. This work illustrates how artificial intelligence (AI) can be harnessed in mobile health applications to expand access to dermatological care and supports broader initiatives to integrate AI into healthcare delivery.

Sci doi: 10.3390/sci8080194

Authors: Shreedevi Balachandran Joshua Kanaabi Muliira Eilean Rathinasamy Lazarus Salma Ali Juma Al Bulushi Rashid Al Mamari Ayman Nabieh Al Bakri Suhair Al Alawi

Background: Artificial intelligence (AI) is increasingly embedded in critical care nursing through monitoring, decision support, and documentation systems, yet nurses’ readiness to use it remains uncertain, particularly in the Middle East region. Critical care nurses are central to AI implementation at the bedside, and their AI-related literacy and fears can influence safe and ethical integration into clinical practice. Aim: To assess AI-related literacy and fears among critical care nurses in Oman and the associated factors. Methods: A nationwide cross-sectional survey was conducted among critical care nurses (N = 477) working in tertiary hospitals in Oman. The Multidimensional Artificial Intelligence Literacy Scale and the Fear towards AI Scale were used to measure AI literacy and fears, respectively. Results: The participants had low overall AI literacy (146.62 ± 84.03), and low AI self-efficacy and AI self-competency. The lowest level of literacy was related to creating AI (2.43 ± 2.63). On the other hand, participants reported moderate overall fear towards AI and moderate levels of fear related to job issues and humanity and ethics. Age, marital status, levels of education, receipt of prior computer or information technology, AI-related training, and work experience, were significant predictors of AI literacy. The predictors of AI self-efficacy and AI competence are presented. Conclusions: Nurses working in critical care settings in Oman reported low levels of AI literacy, but moderate fears related to AI, and this provides an opportunity to build AI competencies and capacity. There is need for deliberate and structured continuing education programs to build capacity for AI utilization, competence, and self-efficacy among critical care nurses. Structured, hands-on AI training that integrates ethical reflection for older nurses with more experience but limited professional education is needed and essential to support safe and equitable AI utilization by critical care nurses in Oman.

Sci doi: 10.3390/sci8080193

Authors: Kun Chen Naser Khan Mohammad Boshir Ahmed Peter Wadewitz Philip Kwong Volker Hessel

Compost quality assessment remains challenging due to substantial variation in standards, analytical methods, and reporting practices across jurisdictions and end-use applications. Differences in testing protocols and quality criteria limit data comparability among studies and compost products, while the absence of a common classification architecture hinders consistent interpretation of compost quality. This study proposes the Compost Pharmacopoeia Framework (CPF), a conceptual framework inspired by the organisational structure of pharmacopoeial systems. Existing standards, including AS 4454:2012, PAS 100:2018, and Regulation (EU) 2019/1009, were critically analysed to identify inconsistencies in physical, chemical, and biological quality assessment. Based on this analysis, the CPF integrates hierarchical classification, analytical methods, application-specific quality criteria, and reference materials within a unified framework for compost characterisation and reporting. The framework further introduces method equivalence concepts to facilitate comparison across non-equivalent analytical approaches. A representative experimental system is used to illustrate how compost quality information can be organised and interpreted within the proposed structure. In the representative nitrogen-regulated green-waste composting case, total nitrogen loss remained below 20%, while treatments were differentiated according to pH stability, nitrogen conservation, and germination performance, illustrating how CPF-organised data can support specific function interpretation. The CPF provides a foundation for improved data comparability, knowledge integration, and application-oriented compost classification, supporting more consistent communication among researchers, regulators, producers, and end-users.

Sci doi: 10.3390/sci8080192

Authors: Elias Milios Kyriaki Kitikidou

Climate change and wildfire are often treated as separate policy problems, although their costs and ecological consequences converge in fire-prone landscapes. This critical integrative review examines the evidence requirements that connect Mediterranean wildfire-risk governance, restoration, biomass use, carbon accounting, ecosystem services, public procurement and professional capacity. A purposive, theme-based synthesis combines peer-reviewed research, international standards, European Union assessments and audit reports, and Greek implementation documents; Greece is used as a policy case within a wider Mediterranean evidence base. Building on the living- and green-infrastructure literature, this review treats forests as living public infrastructure: dynamic systems whose public functions depend on maintenance, ecological limits and repeated verification. It distinguishes evidence synthesis from policy recommendation and evaluates recurrent shortcuts such as unmaintained fuel continuity, tree planting without diagnosis, carbon dioxide equivalent (CO2e) claims without credible baselines or permanence strategies, and monitoring platforms without field validation. The resulting framework links risk diagnosis, prioritised intervention, field measurement, integrity gates and public audit. Its scientific contribution is to specify a common evidence chain through which prevention, restoration and climate claims can be compared and tested rather than accepted as activity counts or communication outputs.

Sci doi: 10.3390/sci8080191

Authors: Andrey A. Vorontsov Mikhail K. Rogachev Grigoriy Yu. Korobov Dmitriy V. Parfenov Thang V. Nguyen Maxim N. Limanov

The formation of asphalt–resin–paraffin deposits (ARPDs) and gas hydrate deposits (GHDs) in oil wells equipped with electric submersible pumps (ESPs) remains a significant challenge in the oil and gas industry. This study aims to develop an inhibitory technology to prevent these deposits by utilizing the synergistic effect of chemical reagents combined with the optimization of ESP operating parameters. Based on previously published mathematical modeling and laboratory studies by the authors, this work presents the technological implementation of the inhibition system and its economic assessment. Specifically, optimal reagent dosages were calculated considering their synergistic interactions, a periodic injection regime was established, and the impact on the well’s mean time between failures (MTBF) was evaluated. Results demonstrate that optimizing ESP parameters shifts the onset depth of GHD formation by 119.4 m (25%) and ARPD formation by 72.6 m (6%). The application of the selected ARPD inhibitor at 0.055 wt.% reduced the thermodynamic hydrate inhibitor (methanol) dosage by 12.17% and enabled a transition from continuous to periodic methanol injection. Consequently, the predicted MTBF increased from 277 to 419 days (+50%). An eight-year economic analysis showed a positive net present value with a payback period of 14 months. Thus, the proposed technology is recommended for field testing in high-paraffin, low-resin oil fields operating under permafrost conditions.

Sci doi: 10.3390/sci8080189

Authors: Adriana K. N. Vargas Diego A. Nunez Vallejos Edgar Mosquera-Vargas

Membrane technologies play a role in water treatment, energy conversion, and industrial separation processes; however, their performance is limited by fouling, polarization phenomena, transport inefficiencies, and energy consumption. This study presents a review of the integration of computational fluid dynamics and machine learning in membrane technologies, complemented by an environmental and engineering assessment of representative membrane materials. A systematic literature screening based on PRISMA guidelines was conducted using the Scopus (Elsevier B.V., Amsterdam, The Netherlands) and Web of Science (Clarivate, Philadelphia, PA, USA) databases, yielding 1421 records, of which 54 studies met the predefined relevance criteria. The analysis revealed a transition from conventional physics-based approaches toward hybrid simulation–machine learning frameworks, with artificial neural networks, surrogate models, and optimization emerging as the dominant methodologies. Energy consumption was identified as the most frequently investigated variable, particularly in desalination, fuel cell, electrodialysis, and hydrogen production systems. A complementary material-level assessment showed that conventional polymeric membranes, especially polyamide-based systems, remain dominant due to their performance and economic feasibility, whereas advanced materials such as graphene, carbon nanotubes, and perovskites offer promising functional properties but face challenges. The findings highlight the potential of integrated simulation–machine learning–material assessment frameworks to accelerate the development of intelligent and sustainable membrane technologies for future applications.

Sci doi: 10.3390/sci8080190

Authors: Nayara de Lira Esteves Pimenta Fabio Antonio Venancio Valter Aragão do Nascimento

Understanding the clinical and hospital trajectory of fatal COVID-19 cases is essential to clarify disease progression and support healthcare planning. This study analyzed the temporal trajectory of patients with confirmed COVID-19 who died in Campo Grande, Brazil, during 2021–2022. A retrospective observational study was conducted using secondary data from 3572 fatal cases. Time intervals between symptom onset, hospitalization, intensive care unit (ICU) admission, and death were calculated. Survival analyses were performed using Kaplan–Meier curves and Cox proportional hazards regression. Most patients were aged 61–80 years (48%), male (55%), and had at least one reported risk factor (86%). The most frequent comorbidities were cardiopathy (56%), diabetes mellitus (35%), and obesity (20%). The mean time from symptom onset to hospitalization was 7.70 days, while the mean time from symptom onset to death was 22.87 days. Survival analyses showed significant differences according to age, neurologic disease, pneumopathy, and obesity. In the multivariable Cox model, age remained independently associated with shorter survival time. Fatal COVID-19 cases were characterized by a relatively short interval from symptom onset to hospitalization and a prolonged in-hospital course until death. By characterizing the timing of clinical deterioration, ICU admission, and death, these findings may inform regional assessments of critical care demand, expected ICU bed occupancy, and capacity planning during periods of increased healthcare demand.

Sci doi: 10.3390/sci8080188

Authors: Cláudia Campos Pessoa Diana Freire Daccak Inês Carmo Luís Isabel Pereira Pais Paulo Legoinha José Cochicho Ramalho Fernando Cebola Lidon Maria Manuela Silva

Micronutrient deficiencies, collectively known as hidden hunger, affect more than two billion people worldwide and remain a major challenge for sustainable agriculture, global food security and human nutrition. Crop biofortification has emerged as a sustainable agricultural strategy to enhance the concentration and bioavailability of essential micronutrients in edible plant tissues while reducing reliance on post-harvest fortification and dietary supplementation. This review provides an integrated analysis of the soil, plant physiological, agronomic and molecular processes governing biofortification efficiency in agricultural systems. Particular emphasis is placed on how soil formation, mineralogy, nutrient speciation, organic matter and rhizosphere interactions regulate micronutrient availability, root uptake, translocation and accumulation in crops. The review further examines plant physiological mechanisms involved in nutrient acquisition and partitioning, together with the contribution of beneficial microorganisms, precision agriculture and digital technologies to improving nutrient-use efficiency under diverse agricultural conditions. Conventional breeding, agronomic biofortification, transgenic approaches and genome-editing technologies are critically evaluated as complementary strategies for developing nutrient-enriched and climate-resilient crop varieties. Particular attention is also given to nutrient bioavailability, post-harvest stability and consumer acceptance, which ultimately determine the nutritional effectiveness of biofortified crops. Furthermore, the review discusses how climate change modifies soil properties, plant physiology and crop productivity, thereby influencing micronutrient availability, nutrient accumulation and the long-term effectiveness of biofortification programmes. By integrating advances in soil science, plant physiology, agronomy and molecular biology, this review identifies current challenges, knowledge gaps and future research priorities for developing resilient biofortification strategies capable of supporting sustainable agricultural systems and improving global nutritional security.

Sci doi: 10.3390/sci8080187

Authors: Yameiri Mena María Belén Almendro-Candel Víctor Sala-Sala Manuel Miguel Jordán Vidal Jose Navarro-Pedreño Ignacio Gómez-Lucas Ana Pérez-Gimeno

Marine oil spills represent a critical environmental threat. Petroleum contamination systematically accumulates in the world’s oceans, driving severe and long-term damage to the biodiversity of vulnerable coastal ecosystems. As its primary objective, this study assesses the ocean’s intrinsic genetic capacity to degrade aliphatic and aromatic hydrocarbons. Using the Ocean Gene Atlas v2.0 (OGA2) database, bacterial metabolic pathways were profiled via a four-marker framework: PF00487 (AlkB) and PF03433 (LadA) for medium and long-chain alkanes, alongside PF00355 and PF00848 domains for aromatic ring activation. The analyses revealed that while salinity levels between 34–36 PSU sustain baseline abundances, temperature acts as a primary selective filter, segregating microbial communities into distinct thermal niches. Medium-chain aliphatic potential (PF00487) is ubiquitous, reaching maximum values in surface polar waters near 0 °C before declining with depth. Conversely, long-chain machinery (PF03433) is restricted to warm surface hotspots. Aromatic-degrading potential (PF00355/PF00848) displayed high thermal resilience, narrowing vertically except for a mesopelagic cluster in the Arabian Sea. Global taxonomic analysis confirmed the dominance of Pseudomonadota (59%), which was mainly represented by the class Gammaproteobacteria (15%), with Alcanivorax (10%) as the most abundant genus. On the other hand, aromatic degraders persist as a low-abundance seed bank. In conclusion, the mere presence of specific genes does not automatically imply metabolic expression; actual in situ biodegradation remains strictly governed by transcriptional triggers and environmental factors.

Sci doi: 10.3390/sci8080186

Authors: Valeria Salinas-Pérez Carlos Amaris Octavio Andrés González-Estrada

This study assesses the technical performance and environmental impact of traditional and eco-efficient materials used in civil construction in Colombia through a structured synthesis of scientific, technical, and regulatory evidence covering the period 2010–2025. The analysis integrates mechanical performance indicators, environmental footprint metrics, and the national regulatory framework supporting sustainable material adoption. Results show that conventional materials—Portland cement, structural steel, ceramic bricks, and timber—remain essential due to their proven structural reliability but are also responsible for the highest contributions to CO2 emissions, energy consumption, and resource depletion. In contrast, eco-efficient alternatives, including blended concretes with mineral additions, geopolymers, rammed earth, Guadua angustifolia, and natural biocomposites, achieve carbon emission reductions between 40% and 85% while maintaining comparable mechanical performance for specific applications. Colombian policies—notably Resolutions 1257 of 2021 and 0194 of 2025—promote waste valorization and low-impact materials, yet their implementation remains limited by technical, economic, and knowledge barriers. The findings support a decision-oriented framework for material selection that balances structural efficiency with environmental responsibility.

Sci doi: 10.3390/sci8080185

Authors: Mario Eduardo Carbonó dela Rosa Mario A. Millan-Franco Jesús E. Diosa Wilson Lopera Edgar Mosquera-Vargas

Series resistance (Rs) is a key parameter that limits photovoltaic cell performance; however, its conventional estimation from current–voltage measurements requires electrical contact and controlled testing conditions. In this study, a nondestructive image-based methodology is proposed to estimate Rs in crystalline silicon photovoltaic cells using electroluminescence images and a Sugeno-type adaptive neuro-fuzzy inference system. A dataset of 666 electroluminescence images was processed using normalized grayscale histogram descriptors, and reference Rs values were obtained from I-V characterization. Three global radiometric descriptors corresponding to low-, medium-, and high-intensity pixel fractions were used as model inputs. The ANFIS model achieved high predictive performance, with a testing RMSE of 0.0065 Ω and an R2 value of >0.98. These results indicate that the global EL intensity distributions contain information related to resistive losses. However, further validation under different acquisition conditions and using independent datasets is required before field-scale deployment. The proposed approach provides a compact and interpretable framework for rapid photovoltaic cell screening based on electroluminescence imaging, complementing conventional electrical characterization under controlled laboratory conditions.

Sci doi: 10.3390/sci8080184

Authors: Fanny Rodriguez-Jarama Manuel Carrillo-Zenteno Roger Pincay-Ganchozo Wuellins Durango-Cabanilla Karina Peña-Salazar Braulio Lahuathe Mendoza Bayron Vecilla-Nicola Israel Ampuño-Muirragui Katiusca Bermúdez Sotomayor

Cadmium (Cd) is absorbed by plants and bioaccumulated in their tissues, and its ingestion may cause adverse effects on human health. However, information on the relationship between soil moisture and Cd bioaccumulation in cocoa-growing soils remains limited. The objective of this study was to evaluate the effect of different irrigation volumes (50, 75, 100, and 125%) on Cd bioaccumulation and biomass production in two cocoa genotypes (EET-103 and CCN-51), as well as on soil pH and electrical conductivity (EC), in two soils from El Oro and Guayas, Ecuador. A split-plot design was used with three factors: A (soils), B (genotypes), and C (irrigation). The variables evaluated included plant height, stem diameter, shoot and root dry biomass, Cd absorption, translocation, and bioaccumulation, as well as soil pH and EC. The highest dry matter production and Cd bioaccumulation were observed in genotype EET-103 grown in El Oro soil and in genotype CCN-51 grown in Guayas soil. A higher irrigation volume (125%) increased the pH of El Oro soil and reduced Cd bioaccumulation by 38.48%, whereas pH decreased in Guayas soil under the same irrigation condition. In El Oro soil, pH and EC showed an inversely proportional relationship. These results suggest that irrigation management and the selection of cocoa genotypes with lower affinity for Cd may be useful strategies to reduce Cd bioaccumulation in cocoa plants.

Sci doi: 10.3390/sci8080183

Authors: Qasim Aljamal Nawal Louzi Ayoub Alsarhan Kholoud Alkayid Malek Barhoush Mohammad Q. Al-Jamal Hussein Al-Ofeishat Fiyad Ahmad Alenazi Osama Harfoushi

Smart and sustainable construction requires methods that jointly manage construction processes, carbon emissions, energy supply, and decision-making. This study develops a renewable energy (RE)-aware carbon digital twin using Siemens Tecnomatix Plant Simulation, version 2022 (Siemens Digital Industries Software, Plano, TX, USA), hereafter referred to as STPS, and artificial intelligence (AI)-based multi-objective optimization. The framework integrates activity records, equipment-demand profiles, renewable-generation data, battery-storage states, emission factors, cost parameters, schedule indicators, and reinforcement learning (RL) state–action–reward records within a discrete-event simulation environment. Construction activities are represented through event-driven source, queue, buffer, processor, resource-pool, transporter, event-controller, table-file, and sink objects, enabling predecessor validation, resource allocation, processing, completion tracking, and key performance indicator (KPI) updates. The energy layer coordinates equipment demand, solar photovoltaic (PV) generation, battery charging and discharging, grid electricity, diesel backup, RE share, battery state of charge (SOC), and emissions. The AI-control layer observes carbon, cost, delay, queue length, utilization, idle time, renewable share, and SOC; filters infeasible actions; evaluates corrective interventions; and ranks policies under carbon, delay, and SOC constraints. Three scenarios are evaluated: diesel–grid baseline operation, rule-based solar-battery operation, and AI-controlled renewable-aware operation. The AI-controlled scenario achieved 16,850 kg carbon dioxide equivalent (kg CO2e), a cost of 2.28 million United States dollars (M USD), a delay of 4.2 h, an 81.6% RE share, 86.7% resource utilization, and 8.6% idle time. Relative to the diesel–grid baseline, it reduced emissions by 32.2%, cost by 18.0%, delay by 66.7%, and diesel-equivalent energy by 97.7%, while increasing RE share by 69.3 percentage points. Reward-weight sensitivity results show that the balanced policy preserves low-carbon performance across stakeholder priorities. The findings demonstrate that integrating digital twins, RE dispatch, and AI-based decision control can support data-driven, optimized low-carbon construction management.

Sci doi: 10.3390/sci8080182

Authors: Sara Sablone Andrea Costantino Federica Laurenzano Giulia Ferretti Emma B. Croce Fabio Vaiano Simone Grassi

The digitalization of healthcare systems has transformed the production, storage, and sharing of clinical information. While electronic health records (EHRs) enhance care efficiency, accessibility, and continuity, they simultaneously introduce complex ethical, legal, and cybersecurity challenges that directly affect patient interests. This narrative review examines the emerging rights associated with digital health records, particularly the right to privacy and the right to be forgotten, alongside threats to confidentiality, cybersecurity, and patient safety. A comprehensive literature search was conducted across PubMed, Web of Science, MEDLINE, and the Cochrane Library. First, the right to be forgotten appears particularly relevant for oncological patients facing financial discrimination and for individuals asserting gender identity rights, yet it cannot be unconditionally extended to genetic data, given its relevance to relatives and future generations. Second, confidentiality risks are amplified by re-identification vulnerabilities, unauthorized access by personnel, and the broad connectivity of digital systems. Third, the secondary use of data from EHRs, including artificial intelligence (AI) integration, commercial exploitation, and large language model training, raises substantial privacy concerns. Fourth, ransomware, phishing, and data breaches can erode patient trust. In this article, we analyze these issues within the evolving European regulatory framework, highlighting the tension between individual privacy rights and broader public interests. We argue that robust data protection must be balanced with scientific progress and that this requires opt-in consent frameworks, staff training, and transparent AI governance.

Sci doi: 10.3390/sci8080181

Authors: Mihail Kolev

Bibliometric analysis is widely used to map scientific fields, identify influential publications, and visualize thematic structures, but maps, rankings, and network clusters cannot by themselves produce a scientifically critical review. This article proposes the Bibliometric-Assisted Critical Review (BACR) framework as a writing-oriented conceptual and pedagogical model for authors, doctoral researchers, supervisors, and reviewers working with large and heterogeneous literatures. BACR is presented as a methodological framework and authoring heuristic, not as an empirical study, a new reporting standard, or a substitute for systematic reviews, scoping reviews, meta-analyses, or conventional bibliometric reviews. The framework distinguishes a broad bibliometric corpus from a narrower critical reading corpus and explains how bibliometric signals can be translated into reading decisions, appraisal questions, synthesis moves, and transparent reporting practices. Its practical outputs include a nine-phase writing process, a bibliometric-to-critical translation matrix, a critical appraisal template, a worked mini-example, a recommended manuscript architecture, a preliminary checklist, and a table of common errors. BACR is intended to help authors use science mapping to strengthen, rather than replace, close reading and critical synthesis. The framework is still preliminary and needs to be adapted to specific fields, tested through pilot applications, and validated.

Sci doi: 10.3390/sci8080180

Authors: Liliya R. Safina Karina A. Krylova Ramil T. Murzaev Stepan A. Shcherbinin Julia A. Baimova

Metal/graphene composites with a metal matrix and graphene reinforcement are very promising innovative materials due to their improved mechanical and physical properties. In this paper, the possibility of fabricating a composite from a graphene network filled with nickel (Ni), copper (Cu) and aluminum (Al) nanoparticles is shown by molecular dynamics simulation. Composites are obtained by hydrostatic compression at 0.7 of the melting temperature of the metal nanoparticles. It is found that the Ni/graphene composite exhibits the highest ultimate tensile strength (89.5 GPa) and Young’s modulus (296.9 GPa) compared to 35.1 and 65.9 GPa for Cu/graphene and 36.8 and 109.1 GPa for Al/graphene, respectively. The Ni nanoparticles were uniformly distributed throughout the graphene network, providing high strength. Indentation simulations confirm this trend: the Ni/graphene composite exhibits a hardness 1.7 times higher than that of pure crumpled graphene, while Al/graphene shows a value 2.2 times lower, which directly correlates with the tensile strength. The Cu/graphene composite has the best ductility (fracture strain of 0.75 versus 0.45 for Ni/graphene and 0.47 for Al/graphene) due to the easier sliding between the Cu nanoparticles and the graphene during tensile loading. The Al/graphene composite has low strength and ductility because the Al nanoparticles tend to coagulate inside the graphene network and hardly interact with the graphene. For Cu/graphene and Al/graphene composites, fracture occurs at the metal/graphene interface. The results show that it is possible to fabricate metal/graphene composites that are much stronger than pure metal by deformation-temperature treatment. In addition, the mechanical properties can be modified by varying the type of metal.

Sci doi: 10.3390/sci8080179

Authors: Ágata Cristiana Correia Cláudia Campos Pessoa Paulo Alexandre Legoinha Fernando Henrique Reboredo Fernando Cebola Lidon Maria Manuela Silva

Biochar has emerged as one of the most promising nature-based strategies for improving soil quality, enhancing crop productivity and supporting climate-smart agriculture. However, the agronomic performance of biochar remains highly variable because its effects are governed by complex interactions among feedstock characteristics, pyrolysis conditions, soil properties and management practices. This review synthesizes recent advances in biochar research (2019–2026), examining how production variables determine biochar physicochemical properties and how these properties subsequently influence soil functioning, plant performance and long-term agricultural sustainability. The review integrates evidence on feedstock selection, pyrolysis technologies, biochar modification strategies and the relationships between biochar properties and soil physical, chemical and biological processes. Particular attention is given to crop productivity, nutrient use efficiency, stress mitigation, contaminant immobilization, greenhouse gas mitigation and long-term soil resilience. Across the literature, the most consistent agronomic benefits were observed when biochar was applied to degraded or resource-limited soils and integrated with complementary management practices, whereas responses were often limited under fertile soils, low application rates or short experimental periods. Rather than identifying a universally superior biochar, the evidence indicates that agronomic performance depends on matching biochar characteristics to specific production objectives and environmental conditions. Based on these findings, this review proposes a transition from generalized biochar application towards optimized deployment strategies supported by standardized characterization, long-term multi-site validation and integrated environmental and economic assessments. This synthesis provides a comprehensive framework for guiding future research and facilitating the effective implementation of biochar within sustainable and regenerative agricultural systems.

Sci doi: 10.3390/sci8070178

Authors: Sanat Tolendiuly Karakat Bolatzhan Nursultan Rakhym Sergey Fomenko Kaster Kamunur Beibit Karibayev Aigerim Sovet Sharafkhan Assylkhan

Radiation-induced defect engineering is an effective approach for modifying the vortex-pinning landscape in high-temperature superconductors, particularly REBCO-coated conductors and Bi-based cuprates. This review critically summarizes the relationship between irradiation parameters, defect morphology, and superconducting performance. The discussion covers point defects, defect clusters, columnar tracks, planar defects, and displacement cascades generated by electrons, gamma rays, light ions, heavy ions, and neutrons. Special attention is given to the dual role of irradiation: moderate defect concentrations can enhance the critical current density by introducing artificial pinning centers, whereas excessive disorder suppresses the superconducting transition temperature and degrades current transport. The review also discusses the relevance of irradiation effects for fusion magnets, space technologies, accelerator systems, and high-field applications. Finally, the review identifies key challenges for future HTS radiation engineering, including cryogenic in situ irradiation, coupled radiation–strain–field experiments, damage metrics beyond dpa, and multi-scale models capable of linking atomic defect production, oxygen disorder, vortex pinning, and macroscopic Jc/Tc degradation.

Sci doi: 10.3390/sci8070177

Authors: Abu Shahed Mst. Manjuma Khanom Fahmida Akter Rapa Md. Masum Billah Mohammad Abdul Qayum Riasat Khan

This work develops an effective platform to enhance the data collection process for researchers. The proposed platform connects researchers who need specific datasets with data curators, employing blockchain and artificial intelligence (AI) techniques. This system includes individual researcher and worker dashboards, an AI-integrated validation process, and a blockchain-based automatic payment mechanism. The AI-driven verification system evaluates the quality and correctness of a sample fraction of the collected data to ensure reliability before approving the full dataset. Payment transactions are handled using blockchain technology, which provides a transparent, secure, and tamper-resistant system. Workers are paid based on verified accuracy, and funds are transferred directly to MetaMask wallets on the blockchain. The prototype demonstrates how AI-based data validation and blockchain-based payments can be combined to support a more trustworthy and automated data collection process. This unique concept provides workers with an efficient and secure way to get paid while ensuring high-quality research data. Experimental results show that the proposed platform achieved validation accuracies of 94.5% and 95.8% with 10% and 20% sampling, respectively, during AI-based dataset verification. The Sepolia blockchain transaction was completed in approximately 12 s, with a transaction fee of 0.00042 ETH for transferring 0.001 ETH. The system handled 1000 users without lag and achieved an 88% satisfaction rate based on survey responses.

Sci doi: 10.3390/sci8070176

Authors: Salvatore Benfratello Antonino Cirello Luigi Palizzolo

The present paper concerns the study of the influence of some geometrical parameters, in particular the depth-to-height ratio, and some mechanical properties, in particular Young’s modulus and Poisson’s ratio, on the response of homogeneous isotropic specimens subjected to the Brazilian splitting test. For this purpose, principal stresses and strains are evaluated at the specimen barycenter through finite element analyses carried out in the ANSYS environment. The relevant analyses are performed by adopting systematic variations in both geometrical and mechanical parameters within suitably defined ranges. The obtained numerical results are compared with commonly adopted reference stresses from the literature, showing that the plane stress assumption is independent of material properties for low depth-to-height ratios, and it is generally not realistic under standard testing conditions. Furthermore, the obtained results prove that the tensile principal stress typically assumed in the literature overestimates the actual value. Moreover, the strain field analysis indicates that the validity of the plane strain hypothesis depends jointly on the geometrical and mechanical characteristics of the specimen. Finally, the study proposes a graphical approach for the estimation of the actual tensile principal stress starting from conventional literature-based values, accounting for specimen-specific parameters. Future work will include the numerical validation of the behavior of the principal stresses along the specimen axis, as well as the experimental validation of the numerical results on a specimen basis, for instance, by employing full-field, contactless displacement measurement techniques.

Sci doi: 10.3390/sci8070175

Authors: Suthon Srivaro Thammarong Eadkhong Apinya Decharun Pacharamon Kaedkaew Chitnarong Sirisathitkul

Although research on cross-laminated timber (CLT) has been extensively investigated, its sound absorption characteristics in relation to CLT’s structure are still sparse in the literature. This study examined the influence of wood species, density, and edge gaps on the sound absorption performance of single or hybrid species of CLT. Six three-layer CLT configurations were fabricated using rubberwood (600–700 kg/m3) and coconut wood of two density ranges (600–700 kg/m3 and 700–800 kg/m3). Three types of edge gaps were introduced—between major laminations, between cross laminations, and a combination of both—at widths of approximately 6, 12, and 18 mm. Sound absorption was strongly affected by wood density, species, and their placement within the CLT structure. Panels incorporating high-density coconut wood showed improved low-frequency absorption, while low-density coconut layers enhanced high-frequency absorption due to their parenchyma-rich microstructure. In addition, performance varied according to gap type and frequency. Core gaps reduced sound absorption across most frequencies, whereas gaps between major laminations improved absorption at low and high frequencies through diffraction and increased surface area. Smaller gap widths further enhanced performance. Panels with combined gap types demonstrated superior absorption compared with those containing a single gap type or no gaps.

Sci doi: 10.3390/sci8070174

Authors: Bedassa Tadesse

Medical device innovation depends not only on technological development but also on institutional review processes that determine when new or modified devices can enter the market. This study examines the role of review route choices and accumulated regulatory experience in shaping the duration of FDA 510(k) reviews. Using 97,623 submissions from 22,838 firms over 1995–2025, this study models FDA 510(k) review duration across review channels and pathways while accounting for the nonrandom use of third-party review. The results show that third-party review is strongly associated with shorter review durations, both after selection adjustments and within Traditional 510(k) submissions. Special 510(k)s also clear faster, while Abbreviated submissions show limited timing advantages unless paired with third-party review. Prior FDA and product-space experience are also associated with faster clearance, suggesting that regulatory capability influences the institutional timing through which medical device innovation moves from development to market availability. The findings highlight the importance of regulatory execution within the broader innovation process in approval-mediated industries.

Sci doi: 10.3390/sci8070173

Authors: Thattapon Surasak Kou Yamada Jirayu Samkunta

This paper proposes a federated learning framework for privacy-preserving patient monitoring with lightweight blockchain anchoring. The framework keeps synthetic patient monitoring records local at each client and uses federated model aggregation to support collaborative learning without centralizing raw records. To improve traceability, the blockchain layer is specified as an anchoring mechanism that records compact evidence, including model hashes and participation metadata, rather than raw data or full model parameters. Experiments were conducted on synthetic patient monitoring data to evaluate framework behavior under non-IID client distributions, label noise, different client counts, partial client participation, and aggregation strategies. The centralized MLP baseline achieved approximately 0.89 overall accuracy but failed to detect alert cases, with 0% alert-class recall, showing that accuracy alone can be misleading in imbalanced monitoring scenarios. In the federated simulations, the model reached approximately 0.99 accuracy under clean labels, approximately 0.90 under 10% label noise, and approximately 0.70 under 30% label noise. Under a more difficult noisy, non-IID, dropout, and fixed skewed-client evaluation setting, the model stabilized at approximately 0.80 accuracy after 25 communication rounds. Client scaling from 5 to 20 clients remained stable, and FedAvg, weighted aggregation, and accuracy-trimmed robust aggregation produced similar final accuracy of approximately 0.98 in the 10-client setting. The results indicate that label quality strongly affects federated convergence, while blockchain anchoring should be interpreted as an auditability mechanism rather than a direct accuracy-improving component. This study provides a framework-level foundation for auditable federated patient monitoring in semi-trusted healthcare networks.

Sci doi: 10.3390/sci8070172

Authors: Nkendirim Godswill-Oguzie Muhammad Zahid Iqbal

Postnatal depression (PND) is a critical mental health issue that affects approximately one in seven women and has a profound impact on infant bonding and family dynamics. Despite its prevalence, PND often goes undiagnosed as a result of ignorance and stigma. This study uses machine learning techniques to predict PND, offering a novel approach to early detection and intervention. Employing multilayer perceptron (MLP), decision tree, XGBoost, support vector machine (SVM), and logistic regression and KNeighbors, this research aimed to analyze various demographic, medical, and psychosocial factors to identify patterns and predictors of PND. The data was subjected to rigorous model evaluation, where the MLPClassifier emerged as the superior model with an AUC of 0.99, indicating near-perfect predictive capability. The study also explored the model’s practical application by integrating it into a web interface using Streamlit, facilitating real-time prediction and analysis of PND risk, thereby making strides toward enhancing maternal mental health care. The findings of this study not only augment the existing body of literature on PND prediction but also highlight the role of ML as an indispensable tool in advancing healthcare technology. By achieving a high level of accuracy in the prediction of PND, the MLPClassifier model has the potential to significantly improve clinical outcomes for mothers and their families.

Sci doi: 10.3390/sci8070171

Authors: Abdallah El Chakik Bilal Nakhal Ghalia Nassreddine

Alzheimer’s disease (AD) remains a critical global health challenge for which early diagnosis is essential for effective intervention. However, AD detection is a challenging and complex task due to the scarcity of labeled clinical data, class imbalance, and lack of prediction interpretability in healthcare systems. Although current machine learning frameworks are promising, they are frequently based on fully supervised environments and lack transparency, which limits their ability to be adopted in real-world clinical scenarios where labeled data are often scarce. This study proposes a new lightweight, semi-supervised, SHAP-enhanced CatBoost (LSSSC) framework that covers the challenges related to insufficient label data, interpretability of prediction models, and performance in a single learning paradigm. The proposed solution provides two major innovations: (i) a hybrid semi-supervised learning algorithm that incorporates the use of confidence-aware pseudo-labeling with cost-sensitive learning, allowing the effective use of the unlabeled data and alleviating the problem of class imbalance, and (ii) an intrinsic SHAP-based feature selection mechanism that assists not only in increasing interpretability but also in minimizing the dimensionality of the feature variables. The suggested approach is tested on a real AD dataset of 2149 patients and 34 diverse clinical, demographic, and lifestyle characteristics. Experimental findings show that LSSSC outperforms traditional supervised and ensemble frameworks, with an accuracy of 95.35%, macro-F1 score of 0.9490, and AUC of 0.9513. Moreover, the combination of SHAP and LIME provides global and local interpretability, permitting a meaningful understanding of the model’s predictions. These contributions make LSSSC a strong, scalable, and interpretable framework for early AD prediction and promise much with respect to its application to real-world clinical decision-support systems.

Sci doi: 10.3390/sci8070170

Authors: John D. Johnston Khadija Rerhou Rantell Essam Kerwash

The FELIX trial is an ongoing, single-arm, open-label study without an internal control, in which adults with relapsed/refractory B-cell precursor acute lymphoblastic leukaemia receive Aucatzyl (a CAR-T cell therapy) and are followed for overall survival. Here, we combine the Toulmin form of argument with a structural causal model to assess the causal effect of Aucatzyl in the FELIX trial.

Sci doi: 10.3390/sci8070169

Authors: Bruno Santos Luiz Lucas Orbolato Carvalho Daniel Alberto Pamplona

Critical ground time in organ, tissue, and team transport missions of the Brazilian Air Force exhibits substantial variability, creating a gap between deterministic fatigue-management assumptions and observed aeromedical operations. This study consolidates the dataset to mission level to avoid pseudoreplication from multiple-organ records. The analytical dataset comprised 583 mission-level observations derived from 712 organ records collected from January 2023 to May 2025 after mission-key reconciliation and consolidation. Rounded five-minute ground-time observations were analyzed with grouped-likelihood duration models, comparing Poisson, Negative Binomial, Gamma, lognormal, Weibull, and log-logistic alternatives. Refitted parametric bootstrap intervals and a chronological 2025 holdout were used to quantify uncertainty and temporal calibration. The mission-level median critical ground time was 80 min for unaccompanied transport, 245 min for simple procurement, and 275 min for multiple procurement. The corresponding fitted P95 benchmarks were approximately 155, 380, and 375 min, respectively, with later-mission exceedance rates close to or below the nominal 5% level in the chronological validation. These results support probabilistic planning benchmarks while clarifying that P95 is a risk-based planning percentile, not a guarantee of mission duration or safety.

Sci doi: 10.3390/sci8070168

Authors: Suttiwan Wunnoo Chanawee Jakkawanpitak Nattanan Methaspornpong Saowakon Indoung Teeraporn Kongtawee Chonlada Namdokmai Natthanit Hemtanon Supayang Piyawan Voravuthikunchai Krittee Dejyong

Oral infections are common health problems in companion animals, often associated with bacterial colonization, biofilm formation, and inflammation. This study investigates the efficacy of hypertonic salt tablets as an antibacterial approach and their effect on bacteria-associated inflammatory stimulation against important oral pathogens, Staphylococcus aureus and Pasteurella canis. Agar well diffusion showed that 0.3 g salt tablets exhibited antibacterial activity, with inhibition zones of 13.33 ± 0.89 mm for S. aureus and 28.75 ± 1.18 mm for P. canis, compared with 23.10 ± 0.87 mm and 31.43 ± 0.81 mm for 0.12% chlorhexidine, respectively. In vitro cytotoxicity assessment demonstrated that 1% v/v and 10% v/v released salt solutions maintained high cell viability (>80%), while morphology assays confirmed non-cytotoxicity of 1% v/v 24 h released salt solution. The 1% v/v and 10% v/v 10 min-released salt solution reduced relative biofilm formation in S. aureus to approximately 61% and 45%, respectively, and in P. canis to approximately 89% and 52%, respectively. Salt-treated bacterial suspensions induced lower NO production in RAW264.7 macrophages, decreasing NO levels from 33.17 ± 2.01 to 4.34 ± 0.65 µM for S. aureus and from 60.73 ± 0.99 to 36.33 ± 0.69 µM for P. canis, suggesting attenuation of bacteria-associated inflammatory stimulation. In a preliminary mouse oral mucosal wound model, local application of NaCl crystals for 30 min reduced total bacterial counts from 8.9 × 102 to 2.0 × 102 CFU/mL, corresponding to a 77.5% reduction. These findings suggest that hypertonic sodium chloride tablets may provide a simple localized approach for short-term reduction in oral bacterial burden and bacteria-associated inflammatory stimulation; however, further validation is required in clinically relevant oral disease models and companion animals.

Sci doi: 10.3390/sci8070167

Authors: Carmen Imma Aquino Arianna Ligori Raffaele Tinelli Renzo Boldorini Stefano Cosma Daniela Surico

(1) Background: Chronic endometritis (CE) is a chronic disease of the endometrium characterized by inflammatory infiltration into the endometrial stroma. The diagnosis of CE is complex. Clinical examination and transvaginal ultrasound are not specific for CE. Biopsy is an indispensable tool. Two specific populations can be analyzed at the endometrial level: CD138 positive plasma cells and CD56 positive Natural Killer cells. (2) Methods: Our narrative review was based on an online search with a focus on the last ten years of literature in the English language. (3) Results: Twenty studies on CD138 positive plasma cells and 16 articles on CD56 positive Natural Killer cells highlighted the correlations of CE with diagnostic possibilities and reproductive outcomes. (4) Conclusions: The etiological pathway and management of CE require further scientific studies and investigations. Hysteroscopy is the diagnostic gold standard, but other blind biopsy techniques (i.e., Perma, Pipelle, Novak, etc.) and molecular procedures may help in the diagnosis of chronic endometritis.

Sci doi: 10.3390/sci8070166

Authors: Fernando Mata Meirielly Jesus Joana Santos

Global food trade exposes sharp differences in food safety regulation, especially between the EU and the US. The EU follows a precautionary, hazard-based model, allowing intervention under scientific uncertainty to protect consumers, maintain public trust, and avoid long-term risks. The US applies a science-based, proof-of-harm approach, requiring clearer evidence of risk before limiting market access, supporting innovation and regulatory efficiency. These contrasting philosophies create trade tensions and non-tariff barriers, as seen in disputes over hormone-treated beef, genetically modified organisms, and chlorine-washed poultry. Beyond the transatlantic context, countries adopt precautionary, science-based, or hybrid systems depending on domestic priorities, institutional capacity, and trade commitments. Hybrid models in India, China, and parts of Africa combine precautionary safeguards with evidence-based risk assessment to balance consumer protection and market access. International bodies such as Codex Alimentarius, the WHO, and the WTO help manage regulatory divergence through standards, guidance, and dispute resolution, while recognising precaution under uncertainty. Recent EU agreements with Mercosur and India show pragmatic cooperation through transparency, safeguards, and sanitary and phytosanitary commitments. Overall, effective global food governance depends on hybrid, coordinated, and adaptive approaches that reconcile health protection, trade facilitation, and innovation.

Sci doi: 10.3390/sci8070165

Authors: Din-Mukhammed Shabaz Talgat Almenov Carsten Drebenstedt Raissa Zhanakova Akmaral Daurenbekova Nurzhigit Sarybayev Bakytbek Bektur

Lithological, metasomatic, and structural heterogeneity in ore-hosting rocks limits the reliability of applying a single set of mechanical parameters without domain subdivision. This study evaluates intact rock properties, shear resistance along natural discontinuities, and conditional upscaling to the rock mass scale. The database comprised 232 laboratory records from 36 geotechnical borehole identifiers: 73 Brazilian tensile tests, 68 uniaxial compression tests, 49 triaxial compression tests, and 42 direct shear tests. Four domains were defined: D1, beresite-altered granodiorites; D2, diorites; D3, granodiorites; and D4, lamprophyre dikes. The lowest mean uniaxial compressive strength occurred in D1 (91.75 ± 40.65 MPa), whereas D2 showed the highest mean value, although its small sample size precludes confirmation as a domain characteristic. D3 provided the most representative dataset and exhibited intra-domain variability, while D4 showed the greatest variability in Young’s modulus (CV = 82.21%). Mean apparent cohesion along natural discontinuities was 0.125 ± 0.039 MPa for D1 and 0.140 ± 0.083 MPa for D3; D2 and D4 remain preliminary. GSI values of 48–58 were used only in scenario-based Hoek–Brown calculations. A ±5-point change in GSI altered equivalent rock mass strength by approximately −25% to −26% and +33% to +34%. The results support domain-based parameterization but require in situ verification.

Sci doi: 10.3390/sci8070164

Authors: Evgeniya Ishchukova Kirill Romanenko Sergei Petrenko Alexey Petrenko Alexey Nekrasov

With the development of quantum computing, classical cryptosystems (RSA, ECDSA) that ensure the security of distributed ledgers face an existential threat. This paper examines protocols for protecting personal data (PD) in blockchain, taking into account the “Harvest Now, Decrypt Later” strategy. We propose and formalize a family of protocols designed for storing and exchanging personal data in blockchain systems. The article describes in detail approaches to software implementations of smart contracts for the Ethereum (using ECIES (Elliptic Curve Integrated Encryption Scheme) and Keccak-256) and Hyperledger Fabric 2.5 (integrating NIST post-quantum standards: ML-KEM (Module-Lattice-Based Key Encapsulation Mechanism) and ML-DSA (Module-Lattice-Based Digital Signature Algorithm)) platforms based on the developed protocols. For all developed protocols, a Threat Agent Model (TAM) is presented, threat scenarios are examined, and resilience to typical attack scenarios is demonstrated. A comparative analysis of computational efficiency and overhead is conducted. The results show that using lattice cryptography provides high performance, but the 50-fold increase in signature size makes direct implementation of PQC (Post-Quantum Cryptography) in Layer 1 public networks economically unfeasible. A hybrid model and the use of Layer 2 to ensure quantum resistance are proposed.

Sci doi: 10.3390/sci8070163

Authors: Luís Grilo José Almeida Manuela Simões Ana Coelho Marques Ana Rita F. Coelho Lara Maistrello

The brown marmorated stink bug (Halyomorpha halys) is a major invasive pest threatening fruit production across Europe. This study integrates spatiotemporal geostatistical modelling with degree-day and photoperiod analyses to characterise its seasonal dynamics in Emilia-Romagna (Italy) from 2020 to 2022. Weekly pheromone trap data were used to quantify developmental succession among Small nymphs (early instars, N1–N3), Large nymphs (late instars, N4–N5), and Adults. Time-series and cross-correlation analyses confirmed consistent developmental delays across years, with Small preceding Large by approximately two weeks and Adults emerging after an additional two to three weeks. However, global inter-stage correlations were moderate (r ≈ 0.4–0.5), indicating substantial spatial heterogeneity among monitoring sites and suggesting that regional averages do not fully capture local population dynamics. To address this variability, a three-dimensional spatiotemporal geostatistical model (space × time) was implemented using Direct Sequential Simulation. The model successfully reproduced seasonal population waves and interannual differences in onset and persistence. The identification of persistent hotspots and stage-specific temporal windows is biologically relevant because it highlights where and when H. halys populations are most likely to increase. As such, from an IPM perspective, these outputs can support earlier monitoring, more precise timing of management interventions, and spatial prioritization of control efforts. These findings demonstrate that combining stage-specific temporal analysis with spatially explicit modelling improves forecasting accuracy and supports more precise timing of biological and chemical interventions. The proposed framework provides a scalable tool for climate-responsive integrated pest management in fruit-growing systems.

Sci doi: 10.3390/sci8070162

Authors: Sasmita Kumari Pradhan Suryakanth V. Gangashetty

Smart-home environments require secure and reliable user authentication mechanisms to prevent unauthorized access and spoofing attacks. Traditional password- and PIN-based methods remain vulnerable to theft, replay attacks, and credential compromise. To address these challenges, this study proposes an intelligent voice-based authentication and anomaly detection framework for secure smart-home environments. The framework utilizes benchmark ASVspoof 2019 and ASVspoof 2021 datasets containing bona fide and spoofed speech samples. After preprocessing, discriminative acoustic features, including Mel-Frequency Cepstral Coefficients (MFCC) and Constant-Q Cepstral Coefficients (CQCC), are extracted and provided to a Hybrid CNN-LSTM model for speaker verification. An integrated anomaly detection module further enhances security by identifying replay, spoofing, and synthetic speech attacks. Access is granted only when the input voice is authenticated and classified as non-anomalous. Experimental results demonstrate the effectiveness of the proposed framework, achieving an overall accuracy of 97.2% and a macro-AUC of 0.972. The model also achieves low Equal Error Rates of 3.8%, 2.9%, and 2.1% across the evaluated classes, indicating robust spoof detection and anomaly generalization capabilities. These results highlight the suitability of the proposed framework for secure and intelligent smart-home access control applications.

Sci doi: 10.3390/sci8070161

Authors: Aida Dama Eni Çelo Sokol Hasho Leonard Deda

Chronic diabetic foot ulcers remain a major clinical challenge due to persistent inflammation, impaired tissue repair, and microbial dysbiosis. We report the case of a 62-year-old male with diabetes mellitus presenting with two chronic diabetic foot ulcers managed using different local treatment strategies within the same patient. One wound received conventional wound care, while the second was treated with topical probiotic therapy containing Enterococcus faecium and Saccharomyces boulardii. Wound progression was assessed over eight weeks using the RESVECH 2.0 scale (Results Expected from Chronic Wound Healing), serial wound measurements, and microbiological cultures obtained before and after treatment. Although baseline wound characteristics differed between lesions—with the probiotic-treated ulcer being smaller and less severe at presentation—the probiotic-treated plantar ulcer demonstrated progressive reduction in RESVECH 2.0 scores and complete closure by week 8, whereas the conventionally treated ulcer remained partially open at the end of follow-up. Serial microbiological assessment demonstrated persistent colonization with Providencia stuartii and changes in antimicrobial susceptibility profiles during follow-up. Although limited to a single-patient observation, these findings support further investigation of microbiome-targeted approaches as adjunctive strategies in chronic wound management.

Sci doi: 10.3390/sci8070160

Authors: Mary Luz Bueno-Ospina Ibeth Emilse Castiblanco-Mañozca Daniel Eduardo Gómez-Agredo Jimmy Alexander Guerrero-Vargas Javier Hoyos-García Alejandro Montoya-Gómez Eliécer Jiménez-Charris Gerardo Corzo Leydy Lorena Mendoza-Tobar

Anuran skin secretions are rich sources of bioactive metabolites whose composition may vary according to environmental conditions. In this study, skin secretions from two populations of Dendropsophus columbianus exposed to contrasting environmental conditions (Clarete and Las Guacas, Popayán, Cauca, Colombia) were evaluated for antifungal activity against Hemileia vastatrix, the causal agent of coffee leaf rust. Secretion from the Clarete population showed no significant inhibition of urediniospore germination (p = 0.9999). In contrast, secretion from Las Guacas, a polluted and poorly conserved environment, exhibited significant dose-dependent antifungal activity (p = 0.0004–<0.0001). Chromatographic profiles from Las Guacas were more complex than those from Clarete. Two fractions, designated 5* and 7*, reduced urediniospore germination to 17.6% and 9.6%, respectively, compared to the negative control (75.37%; p < 0.0001). LC-MS analysis detected low-molecular-weight singly charged compounds (400–1500 m/z) consistent with alkaloid-like secondary metabolites. Although no definitive structural identification was achieved, these findings highlight the chemical plasticity and biotechnological potential of D. columbianus for the sustainable control of coffee leaf rust.

Sci doi: 10.3390/sci8070159

Authors: Sanja Kapetanović Mile Dželalija Nina Bijedić Dražena Gašpar Sanja Tipurić-Spužević

Agent-based neural models often encode transmission within neuron state updates, which can make it difficult to separately log and quantify spatial recruitment patterns, delay structure, and environment-mediated feedback effects. We present LANA (Local Adaptive Neural Agents), a dual-agent neural agent-based model in which neurons and propagating signals are represented as distinct interacting entities embedded in a dynamic environmental field. The model combines discrete leaky integrate-and-fire neuron dynamics, mobile signal agents, synaptic links with distance-dependent delays, and a bounded environment-to-neuron feedback mechanism. LANA is intended as a normalized phenomenological mesoscopic framework for mechanism-level comparison rather than as a circuit-specific biophysical reconstruction. To support interpretability and reproducibility, we report a compact internal verification block for the implemented operators, including delay propagation, environmental decay and diffusion, threshold activation, and refractory enforcement. We then compare the full LANA model against a matched neuron-only baseline and summarize spatial recruitment using first-spike maps, cumulative recruitment times, and wavefront speed as a secondary descriptive metric. Finally, we evaluate two controlled operating regimes, a resting regime (S1) and a hyperexcitable regime (S2), under fixed network size, stimulation schedule, and matched random seeds. Relative to the baseline, the full model sustains and spreads activity more effectively and provides spatially resolved recruitment summaries, including first-spike timing and cumulative recruitment measures, that are not available in the same form when transmission is represented only through neuron-level updates. Relative to S1, S2 exhibits earlier activation, higher firing activity, stronger environmental accumulation, and faster cumulative recruitment. Local and factorial sensitivity analyses further identify the parameters that most strongly govern these regime differences. Together, these results position LANA as a normalized mesoscopic and computationally tractable framework for studying how excitability, transport state dynamics, delayed coupling, and environment-mediated feedback jointly shape emergent activity in controlled simulation settings.

Sci doi: 10.3390/sci8070158

Authors: Mina Tadros Evangelos Boulougouris Evangelos Stefanou Panagiotis Louvros

Maritime accident analysis increasingly uses machine learning to support safety management, but many existing studies focus on single-output prediction, such as accident-occurrence probability, severity class, near-miss frequency, or one specific consequence. This study proposes a data-driven decision-support framework based on a Multi-Input Multi-Output Artificial Neural Network (MIMO-ANN) for the simultaneous prediction of multiple maritime accident consequences. A dataset of 582 recorded accident cases is constructed by integrating SafePASS project records with consequence, severity, and structural-damage information from the literature. The dataset includes 15 input variables covering ship characteristics, operational context, environmental conditions, accident type, and geographical zone and 15 consequence outputs covering structural damage, casualties, emergency-response indicators, total loss, and secondary consequence/escalation mechanisms. The ANN is trained using the Scaled Conjugate Gradient (SCG) algorithm and evaluated under different network configurations and data-partitioning strategies. The best-performing model uses 30 hidden neurons with a 60/20/20 split, achieving a correlation coefficient (R) equal to 0.9249 and a mean squared error (MSE) equal to 0.0240 for testing, and a R equal to 0.9278 and a MSE equal to 0.0231 for validation. Ten-fold cross-validation further confirms internal predictive stability, with mean testing R equal to 0.8803 ± 0.0827 and MSE equal to 0.0445 ± 0.0478. Permutation-based sensitivity analysis shows that accident type, zone, flag, natural light, environment, and visibility are key drivers of predicted consequences, whereas vessel-specific parameters have a secondary, context-dependent influence. The framework should be interpreted as predicting the relative likelihood, severity, or magnitude of accident consequences in recorded or scenario-defined accident cases, not the probability of accident occurrence. Future work should address dataset imbalance, include near-miss and nonserious records, incorporate richer AIS and metocean data, integrate exposure data, and validate the framework using independent accident datasets.

Sci doi: 10.3390/sci8070157

Authors: Lorenzo Albanese

Hydrodynamic cavitation is assessed as a conditional process-intensification platform for the sustainable recovery and transformation of bioactive and functional fractions from agri-food residues, food-processing by-products, and plant-derived matrices. The analysis focuses on fractions enriched in polyphenols, flavonoids, pectins, carotenoids, proteins, pigments, essential oils, and other value-added compounds with potential relevance for food, nutraceutical, formulation-oriented, and related high-value applications. Rather than being considered an inherently green or universally superior technology, hydrodynamic cavitation is evaluated according to the specific process functions it can provide, including matrix disruption, mass-transfer enhancement, solvent-use reduction, recovery of pectin-associated fractions, protein extraction, macromolecular restructuring, dispersion, and process integration. Quantitative and scale-relevant indicators are considered where available, including recovery yield, target-compound content, solvent use, operating conditions, treated volume, energy input, fraction quality, and reporting limits. Comparison with ultrasound-assisted extraction, microwave-assisted extraction, pulsed electric fields, subcritical water extraction, natural deep eutectic solvents, and enzyme-assisted extraction indicates that its advantage is most defensible when hydrodynamic effects address a clearly identified matrix or process limitation. The available evidence supports substantial potential for wet matrices, plant by-products, aqueous suspensions, and liquid food systems. However, critical gaps remain in energy reporting, selectivity, recovered-fraction stability, scale-up, downstream processing, and application-oriented validation. Recovered fractions should therefore be regarded as candidate ingredients or functional intermediates, rather than as direct evidence of efficacy in final products.

Sci doi: 10.3390/sci8070156

Authors: Abderrahim Bouaid Loubna El Faroudi Karima Abdelouahdi Abderrahim Solhy

To mitigate the environmental footprint of the aviation sector, this study proposes an integrated cascading biorefinery scheme to produce Sustainable Aviation Fuel (SAF) precursor bloodstock via enzymatic transesterification of coconut oil. Utilizing a synergistic binary alcohol system (ethanol-butanol) and the liquid lipase Eversa Transform 2.0, a strategic molecular reconfiguration of fatty acid esters was achieved. Optimization through Response Surface Methodology (RSM) identified critical parameters—5% catalyst loading, total binary alcohol-to-oil molar ratio of 7:1 (specifically comprised of a 2.5:4.5:1 ethanol/butanol/coconut oil matrix), and an operation temperature of 57.5 °C—yielding a 97% conversion efficiency. A sequential vacuum fractional distillation process was implemented to partition the ethyl-butyl esters into high-value streams. Notably, the light distillate fraction, characterized by a specific carbon chain distribution (C6: 27.2%, C8: 52.5%, C10: 6%, and C12: 13.6%), perfectly aligns with the molecular window of aviation kerosene. This fraction exhibits excellent cold-flow properties, viscosity, and volatility profiles, positioning it as an ideal high-performance SAF precursor blendstock to increase the renewable content of current aviation fuels. Simultaneously, the remaining C16–C18 residue serves as a high-density energy source for internal refinery processes, while C8–C14 species are recovered as high-purity chemical feedstocks. This circular model maximizes carbon atom economy and economic viability by cogenerating high added-value biochemicals alongside jet-grade blendstocks. These findings provide a scalable, enzymatic framework for the next generation of decarbonized aviation fuels.

Sci doi: 10.3390/sci8070155

Authors: Ainhoa Rubio-Clemente Sergio Agudelo Flórez Edwin Chica

This study develops two independent energy transition indices, the Global North energy transition index (GNETI) and the Global South energy transition index (GSETI), using principal component analysis (PCA) to evaluate energy transition performance during the period 2013–2022. Each index was calculated independently using a balanced panel of 70 observations, corresponding to 7 countries observed over 10 years. The Global North sample includes Denmark, Germany, Sweden, Japan, Canada, the United Kingdom, and the United States; while the Global South sample includes India, Brazil, South Africa, Indonesia, Mexico, Colombia, and Ghana. For the Global North, PCA results show that PC1 explains 59.68% of the total variance, while PC2 and PC3 describe 21.10% and 17.42% of the total variance, respectively. The first two components account for 80.78% of the total variance, while the first three components explain 98.20%, confirming the robustness of the index structure. The 2022 GNETI values indicate that Sweden has the highest performance (100.00), followed by Denmark (71.01), Canada (66.50), the United Kingdom (40.51), Germany (36.46), the United States (34.45), and Japan (11.86). The PCA results show that PC1, PC2, and PC3 explain 58.76%, 21.99%, and 9.63%, respectively, of the total variance. The cumulative variance described confirms the adequacy of the PCA approach for constructing the GSETI. In 2022, the highest GSETI values were observed in Ghana (90.39), Colombia (85.06), Brazil (70.98), India (59.00), Indonesia (52.97), Mexico (47.72), and South Africa (18.70). The findings indicate significant regional differences in the pathways of energy transition, based on variability in technological capability, renewable energy uptake and resources, energy security levels, and the fundamental structure of state energy systems.

Sci doi: 10.3390/sci8070154

Authors: Sabien G. J. Heisterkamp Tharanghi Logendran Ariane Willems Can Ozan Tan

Imaging of the lungs is traditionally based on chest X-ray as a first-line imaging method for lung aeration assessment. However, radiation exposure limits its use for patients in the pediatric intensive care unit. Lung ultrasonography (LUS) is a suitable alternative, but its interpretation is highly observer-dependent and requires sufficient experience and skill. We sought to develop a model based on deep learning to assist the clinician in the interpretation of LUS observations. In this retrospective, single-center, proof-of-concept study, all patients, age 0–18 years old admitted at the PICU of the Leiden University Medical Center (LUMC) between January and May 2022 who underwent an LUS were included. LUS video frames were analyzed using a deep learning tool; a conditional generative adversarial network (cGAN) was developed to generate segmentation masks containing clinical features from individual LUS frames. A total of 31 patients, with a median age of 2.5 months (IQR 0–11 months), were analyzed. A total of 98 LUS assessments and 506 4-s videos were collected. The median LUS score was 12 (IQR 8–17). The two best-performing frame-based segmentation models achieved mean Dice similarity coefficients of 0.97 ± 0.03 and 0.96 ± 0.03, with mean squared errors of 0.025 ± 0.025 and 0.030 ± 0.026, respectively. These findings demonstrate that a pediatric-specific cGAN can segment key LUS features from individual frames. However, the results derive from a small, single-center cohort with a frame-level rather than patient-level data split, and no formal clinical validation; independent, prospectively collected validation cohorts are required before any clinical implementation.

Sci doi: 10.3390/sci8070153

Authors: Jed Shakarji Apryl Susi Zella Berill Remle Scott Dominic Nathan Cade M. Nylund

Background: Post-COVID-19 conditions (PCCs) present a significant public health challenge due to a vast array of new or persistent health symptoms across subjects. The complex, multi-systemic nature of PCCs makes these conditions difficult to differentiate from other non-COVID-19 related medical conditions. While the Military Health System Data Repository (MDR) provides a robust supply of population-level encounter data, its high-dimensional structure poses challenges for knowledge discovery and outcome research. Objectives: The primary aim of this study was to identify novel manifestations of PCCs among active-duty service members, and model the probabilistic relationships between PCC-related diagnoses. We propose a machine learning workflow as an effective tool for knowledge discovery to statistically validate candidate PCCs from large datasets. Methods: We conducted a retrospective cohort study using MDR records from July 2018 to June 2023. From an initial pool of 311,367 eligible Active-Duty Tricare beneficiaries, we isolated 101,789 COVID-19 infections and matched them 1:1 with uninfected controls (N = 203,578 total) based on age, sex, and propensity for COVID-19. Encounter data was mapped to 392 clinical categories using the Healthcare Cost and Utilization Project (HCUP) Clinical Classification Software Refined (CCSR). Candidate PCC categories were isolated using a cross-validated lasso regression model optimized with a Tree of Parzen Estimators algorithm. A consensus Bayesian Network structure was fitted to model potential probabilistic dependency structures between identified PCCs and prior COVID-19 diagnosis. Finally, conditional Cox proportional hazards models were used to statistically validate selected novel conditions using larger cohorts drawn from the same initial eligible pool by matching cases 1:2 with controls. Results: Feature selection reduced the diagnosis set by 97.96%, isolating 8 clinical categories from the initial 392. The model confirmed known PCCs, such as respiratory symptoms and malaise, and identified two potentially novel candidate PCCs: tinnitus and personality disorders. Survival analysis validated the selection of tinnitus, showing a significant association with COVID-19 (HR: 1.17, 95% CI: 1.12–1.22). No significant association was found between COVID-19 infection and personality disorders (HR: 1.11, 95% CI: 0.97–1.26). Conclusions: This study demonstrates an effective analytical pathway for addressing the limitations of analyzing complex, high-dimensional healthcare billing data. The methodology successfully generated testable hypotheses, identifying tinnitus as a relevant sequela, and is generalizable to future research involving unknown health outcomes related to prior infection.

Sci doi: 10.3390/sci8070152

Authors: Geetha Subramaniam Ravindran Vythilingam Nida Suhail Anshoo Agarwal Gulam Saidunnisa Begum Vijaya Marakala Osama Khattak

Gluten-related diseases (GRDs), affecting approximately 1–6% of the global population, require long-term adherence to a gluten-free (GF) diet for effective disease management. Food label literacy plays a critical role in ensuring dietary safety; however, consumer attitudes, behaviors, and perceptions regarding GF food labeling remain insufficiently characterized. This study systematically reviewed and synthesized evidence on these factors across different GRDs. A comprehensive search of PubMed/MEDLINE, EMBASE, Cochrane Library, Scopus, and Web of Science was conducted for studies published between January 2000 and December 2025. Studies evaluating attitudes, beliefs, and behaviors related to GF food labeling among individuals with GRDs were included. A total of 82 studies involving 61,284 participants from 27 countries were included, with 44 studies contributing to the meta-analysis. Consistent GF label reading was reported by 79.2% of participants, while 60.3% expressed confidence in label accuracy. However, 40.9% reported dietary infractions due to misleading labeling. Label reading behavior varied across disease groups and regulatory settings. Key barriers included ambiguous wording, inconsistent cross-contamination disclosures, and lack of standardized symbols. These findings highlight important gaps in labeling practices and emphasize the need for standardized regulations and targeted educational interventions to improve dietary safety and health outcomes.

Sci doi: 10.3390/sci8070151

Authors: Akira Toyohara Weisheng Zhou

Can China feed itself by 2100? This study begins with a critical reexamination of the bayesTFR recovery assumption embedded in UN WPP 2024 and reconstructs population scenarios using an “empirical-base ± empirical-offset” methodology anchored at China’s 2023 official TFR of 1.01. For food security analysis, we narrow the population to three scenarios (low, medium, and policy target), and we set three scenarios each for the demand side and the supply side, producing an integrated 27-scenario analysis. The demand side comprises three trajectories: East Asian saturation type (650 kg/person/year), EU type (780 kg/person/year), and EU + α type (850 kg/person/year). The supply side comprises three trajectories: optimistic (cropland area maintains the red line, with yield reaching the technological ceiling), medium (climate change reduces yield by −10%, and cropland area breaches the red line by −5%), and pessimistic (climate change reduces yield by −20%, and cropland area breaches the red line by −10%). Based on NBS empirical data, projection results show that all 27 scenarios achieve surplus by 2100 (even the worst case retains 0.265 Gt surplus), confirming the robustness of long-term food security. However, during the medium term (2030–2050), the worst case scenario retains 26% import dependency. Even under the U.S.-type full emulation scenario (1100 kg/person/year) examined as a supplementary stress test, all nine sub-scenarios maintain surplus. The challenge for China’s food security lies not in long-term absolute shortage but in medium-term import dependency management and policy transition to the surplus era. By integrating demographic projection, agricultural-economic demand modeling, and a layered food-system accounting framework, this study offers a transferable cross-disciplinary methodology for long-term food security assessment under demographic transition, relevant beyond China to other aging, post-peak societies.

Sci doi: 10.3390/sci8070150

Authors: Sharin Valdivia Camila Riquelme Ángelo Torres-Arévalo Ivonne Brevis Osvaldo Gaete Sebastián Alarcón

Cancer stem cells (CSCs) drive therapeutic resistance and tumor relapse by exploiting redundant regulatory networks that integrate Wnt/β-catenin, Notch, and Hedgehog signaling with metabolic reprogramming, epigenetic plasticity, and tumor microenvironment crosstalk, a network architecture that renders single-pathway inhibition strategies insufficient. This review systematically examines evidence that natural compounds (curcumin, sulforaphane, resveratrol, EGCG, berberine, and quercetin) act as multitarget modulators of CSC plasticity, analyzing their molecular mechanisms of action in specific cancer models. Each compound engages distinct regulatory nodes: curcumin suppresses β-catenin nuclear translocation and STAT3 phosphorylation in lung cancer CSC models; sulforaphane represses ΔNp63α-driven stemness transcription in colorectal cancer and reduces CSC self-renewal in prostate and head and neck models; resveratrol dissociates the β-catenin–GLI-1 interaction in oral and lung CSC populations and induces Wnt/β-catenin-dependent autophagy in breast CSCs; EGCG inhibits DNMT and HDAC activity in glioblastoma and colorectal models; berberine activates AMPK-mediated suppression of mTORC1 in colorectal cancer; and quercetin suppresses PI3K/AKT/mTOR signaling while downregulating EMT transcription factors in breast and colorectal systems. We critically assess persistent methodological limitations, including bulk cell-line models, supraphysiological concentrations, and the absence of functional tumor-initiating validation, that currently prevent stronger translational conclusions. Natural compounds from Latin American biodiversity are identified as an underexplored source of CSC-active molecules. We conclude by defining the experimental standards required to reposition natural compounds as clinically relevant network-level modulators of CSC plasticity.

Sci doi: 10.3390/sci8070149

Authors: Abdullah S. Hamoud Mahmood F. Mosleh Salah Al-Zubaidi

This paper introduces an IIoT-based hybrid predictive maintenance system for industrial steam boilers, which responds to the increased demands for making intelligent and accurate decisions by leveraging data-driven analytics in complex industrial environments. The proposed approach presents comparative hybrid predictive monitoring frameworks based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models integrated with Statistical Process Control (SPC) and Cumulative Sum (CUSUM) monitoring techniques for industrial boiler monitoring; it allows accurate system behavior prediction coupled with enhanced anomaly detection across interconnected subsystems. To ensure practicability, the framework is implemented in an integrated operation technology and information technology (OT–IT) architecture with one year of real operation data from an industrial steam boiler in an oil refinery. A two-phase validation strategy is employed to overcome the gap between offline model development and application. During the initial phase, predictive models are developed and tested based on multivariate time-series data to model both the time dependence of the processes and the mechanical variables. The second phase involves the online deployment of the predictive monitoring framework through a Hardware-in-the-Loop (HiL) implementation with Programmable Logic Controller (PLC)-based and Open Platform Communications Unified Architecture (OPC UA) communication to enhance realistic system validation under emulated boiler process conditions without disrupting live plant operations. The experimental results indicate that the GRU model outperforms the LSTM, achieving good R2 (0.8956) and mean absolute percentage error (MAPE, 0.6345%), demonstrating strong predictive accuracy across key operational variables. In addition, SPC is used to set up adaptive operational thresholds based on normal industrial process behavior, and then CUSUM is applied to the prediction residuals to improve the detection of the gradual degradation of the system. Real-time validation ensures system stability, low latency, and bidirectional data transfer between the OT and IT layers, enabling continuous monitoring and real-time decision-making. The proposed solution provides a practical and scalable predictive maintenance framework in an industrial context, particularly in oil and gas operations, that helps to transition to Industry 4.0 and intelligent asset management.

Sci doi: 10.3390/sci8070148

Authors: Melissa Pérez Hincapié Victoria A. Arana Roberto García-Alzate Daisy Lozano-Arias Jorge Trilleras

Excitation–emission matrix (EEM) fluorescence spectroscopy, when combined with machine learning algorithms, has emerged as a highly promising tool for non-invasive biomedical diagnosis, demonstrating significant potential across various applications. This systematic review offers a comprehensive analysis of recent advancements in integrating EEM with chemometric techniques and machine learning models for the detection of infectious diseases, cancer, neurological, and metabolic disorders, as well as for monitoring bioactive compounds and hormonal contaminants. The review examines multivariate approaches alongside spectral preprocessing strategies, highlighting their ability to resolve overlapping signals and extract relevant information from complex biological matrices. The reviewed studies report promising high sensitivity, specificity, and accuracy values across diverse biological matrices and disease targets, supporting the scalability and versatility of this diagnostic platform. A critical evaluation of methodological approaches is also provided, identifying common pipeline-level challenges and drawing a constructive distinction between proof-of-concept studies, which establish the discriminative potential of EEM spectral data and studies aimed at clinical validation, a distinction that helps contextualize reported performance and guides future research design. Future perspectives focus on the development of open-access spectral databases, portable devices, standardized preprocessing protocols, and the integration of deep learning and explainable artificial intelligence, all of which represent concrete pathways toward the clinical translation of EEM-based diagnostics. This review confirms the value of EEM spectroscopy coupled with machine learning as a versatile, scalable, and high-impact platform for biomedical diagnostics, with significant potential for applications in public health and personalized medicine.

Sci doi: 10.3390/sci8070147

Authors: Antonio Rafael Rodríguez Abraham

The resurgence of trade policy interventions in the global economy has renewed interest in the long-run macroeconomic implications of commercial barriers. While previous research has largely focused on the short-term effects of tariff reforms and trade liberalization, relatively less attention has been paid to the persistence of trade policy regimes over time. This study addresses this gap by analysing the relationship between trade policy persistence—proxied by the trajectory of the Nominal Average Tariff (NAT)—and Peru’s real GDP during the period 1980–2025. Using a Johansen cointegration framework combined with a Vector Error Correction Model (VECM), the study evaluates both the existence of a long-run equilibrium relationship and the dynamics of adjustment following deviations from that equilibrium. The econometric evidence confirms the existence of a stable long-run relationship between the NAT and aggregate GDP. The normalized cointegrating vector suggests that higher and persistent levels of tariff protection are associated with lower levels of real GDP in the long run. The estimated error-correction mechanism further indicates that deviations from equilibrium are gradually corrected through adjustments in the trajectory of real GDP, whereas the tariff equation does not exhibit a statistically significant adjustment process at conventional levels. This asymmetric structure suggests that trade policy persistence operates as a relatively stable macroeconomic condition, while aggregate GDP gradually adjusts to long-run disequilibria. By framing tariffs not only as policy instruments but also as indicators of persistent policy orientations, the study contributes to the trade and growth literature from a persistence-based perspective. The findings additionally highlight the potential relevance of policy consistency and predictability in small open economies characterized by high external dependence and prolonged processes of trade liberalization.

Sci doi: 10.3390/sci8070146

Authors: Spoorthi Kadikol Math Nagaraj N. Katagi Ashwini Bhat Manjunath Shettar Rajashekhar V. Choudhari

The present study investigates heat transfer characteristics in the thermally developing region of a porous tube under the local thermal nonequilibrium (LTNE) model. The influence of magnetohydrodynamic (MHD) flow on an electrically conducting fluid flowing through a porous medium under a transverse magnetic field is examined. Under the LTNE framework, two separate energy equations are employed to describe the temperature fields of the fluid and solid phases. The coupled governing equations are solved numerically using the Keller-box method. The results indicate that increasing the interphase heat transfer parameter strengthens thermal coupling between the fluid and solid phases, thereby reducing temperature differences and promoting local thermal equilibrium. In contrast, an increase in the Prandtl number reduces thermal diffusion, leading to larger temperature gradients and greater disparity between the two phases. Furthermore, the magnetic field suppresses both the velocity and temperature distributions through the Lorentz force. An increase in permeability reduces the velocity profiles due to the combined effects of the MHD and Prandtl numbers while increasing the temperature profiles. Increasing the interphase heat transfer rate drives the system from the LTNE to the LTE phase. The study confirms that LTNE effects play a significant role in thermal transfer processes in porous media and are relevant for various industrial heat transfer applications.

Sci doi: 10.3390/sci8070145

Authors: Naila Nasirova Gregor Kaljula Elina Leis Darja Lavogina

Since its introduction in 1976, the Bradford assay has served as a gold standard for protein quantification across a wide range of applications. While its limitations—including protein-to-protein variation in dye binding, challenges in selecting a representative calibration standard, and susceptibility to matrix interferences—are recognized, the relevant information remains scattered throughout the literature, with little quantitative guidance available for assay optimization. Here, we review interfering compounds reported in the literature during nearly 50 years and report a systematic characterization of a panel of potential interfering compounds, evaluating the effects of 29 different substances in the presence or absence of the protein analytes. Our findings revealed that 12 of the tested compounds induce significant artefacts in the Bradford assay, with minimal interfering concentrations varying widely across compounds. Detergents were confirmed as the most problematic interference; furthermore, two novel groups of interfering compounds were identified, represented by the transfection reagents and oligoarginine peptides with molecular weight below 3 kDa. Importantly, the resulting artefacts were also observed in complex biological matrices. While these compounds also affected the Lowry assay, the magnitude of the artefacts was substantially lower than that observed in the Bradford assay. This study will provide a valuable resource for researchers working in proteomics and related fields, offering practical insights for improving the reliability of Bradford-based protein quantification.

Sci doi: 10.3390/sci8070144

Authors: Yelena Brazhnikova Lyudmila Ignatova Natalya Vedyashkina Saule Kenzhebayeva Ekaterina Moskvina Susana Muradova Alla Goncharova Tatyana Karpenyuk Madina Alexyuk Andrey Bogoyavlenskiy Aizhamal Usmanova Nariman Abilman Ilya Digel

The aim of this study is to develop and comprehensively evaluate the efficacy of an innovative formulation of a biological preparation consisting of a bacterial consortium (Serratia proteamaculans B5, Pseudomonas putida D7 and Lysinibacillus sp. S1), embedded in a pullulan polysaccharide matrix, as an agent for inducing systemic resistance in barley (Hordeum vulgare L.) to phytopathogenic stress caused by Fusarium graminearum. To optimize the product’s protective efficacy and minimize the pesticide load on the agroecosystem, a reduced dose of Fundazol (50% of the standard rate) was incorporated into the formulation. The constituent strains exhibited high indole-3-acetic acid production (53.29–69.2 μg·mL−1) and strong antagonistic activity against phytopathogenic fungi, with inhibition zones reaching up to 32.5 mm. Pot and field trials were conducted to comprehensively assess the effect of the biological product on the stress tolerance of barley plants. Pre-sowing seed treatment reduced proline accumulation (by up to 2.3-fold), maintained photosynthetic pigment levels, and increased field germination to 79%. Under infectious field conditions, treatment with the biopreparation contributed to the stabilization of yield structure parameters (treated plants exhibited increases in height and biomass of 9–21%) and the improvement of grain quality indicators. Overall, the results obtained demonstrate the potential of the developed biopreparation as a component of comprehensive protection strategies and as an inducer of plant priming mechanisms.

Sci doi: 10.3390/sci8070143

Authors: Gonzalo Arias-Álvarez Rodrigo Campos-León Alexander Bravo-Ovarett Francisco Guede-Rojas José Manuel Gómez-Pulido Waldo Osorio-Torres Benjamín Parada-Norambuena Claudio Carvajal-Parodi

Fibromyalgia is a chronic pain condition associated with reduced quality of life, psychological symptoms, cognitive impairment, and low adherence to exercise-based interventions. Immersive virtual reality (IVR) has gained attention as a rehabilitation approach; however, the relationship between user experience, adherence, and clinical outcomes remains unclear. This post hoc exploratory secondary analysis was derived from a randomized controlled clinical trial (NCT07605143) and included 16 women with fibromyalgia who completed a six-week IVR-based rehabilitation program. Associations between user experience, adherence, and clinical outcomes were explored among participants exposed to IVR. Quality of life was assessed using the Fibromyalgia Impact Questionnaire-Revised (FIQ-R), psychological symptoms using the Depression Anxiety Stress Scales (DASS-21), and user experience using the Player Experience of Need Satisfaction (PENS) questionnaire. Within-group changes were observed in FIQ-R scores (p = 0.001; d = 1.08) and DASS-21 stress levels (p = 0.025). Participants demonstrated favorable adherence and positive user experience. A significant correlation was identified between intuitive control and changes in depression scores (ρ = 0.63, p = 0.008). This exploratory analysis identified favorable user experience and adherence among women participating in an IVR-based rehabilitation program. However, due to the absence of a concurrent control group, no conclusions regarding treatment efficacy can be drawn. These findings should be considered hypothesis-generating.

Sci doi: 10.3390/sci8060142

Authors: Pierpaolo Di Bitonto Michele Magarelli Angelo Mariano Pierfrancesco Novielli Valentina Piantadosi Valeria Poscente Emilia Pucci Sandro Pullo Donato Romano Francesco Salzano Remo Pareschi Sabina Tangaro Claudia Zoani

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings.

Sci doi: 10.3390/sci8060141

Authors: Giuseppe Penna Igor Jelaska Gaetano Raiola Giovanni Esposito

The ability to generate and sustain high punch intensity is a key determinant of boxing performance. However, the relationship between mechanical output and physiological responses during intermittent high-intensity efforts remains insufficiently understood. This pilot study explored associations between mechanical punch intensity and physiological responses in elite and amateur boxers, considering sex and competitive level. Twenty boxers (10 elite and 10 amateurs; 5 males and 5 females per group) were assessed using the inertial sensor-derived Intensity Score to quantify mechanical punch intensity, alongside physiological measurements of heart rate (HR) and oxygen uptake (VO2) during a Repeated Punch Ability Test (RPAT). Data were analyzed using repeated-measures ANOVA and Spearman correlations (p < 0.05). A significant main effect of round was observed for all variables (p < 0.001), with a progressive decline in mechanical punch intensity and stabilization of physiological responses after the first round. Elite boxers showed descriptively higher mechanical punch intensity values than amateurs, particularly in later rounds, but subgroup comparisons should be considered preliminary. Correlation analyses indicated a positive association between mechanical punch intensity and VO2 that appeared to increase from the first to the third round (ρ = 0.398–0.563; p < 0.05), whereas the relationship with HR was weak or negative. No significant correlations were found between HR and VO2. Overall, the findings suggest preliminary patterns in which fatigue and competitive level may be associated with mechanical punch intensity, while VO2 may become more closely related to mechanical output under fatigue. These results should be interpreted as exploratory and non-causal.

Sci doi: 10.3390/sci8060140

Authors: Yuttanun Pansong Chitnarong Sirisathitkul Natdanai Saipan Chiraphon Sutham Pongsakorn Wattanasit Wannasan Noonsuk Kaoru Ueda

Fine-paste earthenware held symbolic significance in Hindu and Buddhist rituals and domestic use in Southeast Asia. Despite the influx of Chinese glazed ceramics from the ninth century onward, these locally produced vessels continued to circulate widely until the fourteenth century along maritime trade routes extending from northern Sumatra and Java to the southern Philippines and the Thai–Malay Peninsula. Integrated petrographic, Field Emission Scanning Electron Microscopy (FESEM), and Energy Dispersive X-ray Spectroscopy (EDS) analyses were employed to compare fine-paste earthenware from the Kok Moh production center in Songkhla Province, Thailand, and the Kota Cina consumption site in northern Sumatra, Indonesia. Petrographic observations indicate broadly similar mineralogical compositions in samples from both sites, consistent with the use of kaolin-rich clay materials. FESEM reveals that Kok Moh samples exhibit relatively dense and homogeneous microstructures with more continuous matrices, whereas Kota Cina specimens display coarser textures, more distinct mineral inclusions, and less consolidated matrices. EDS elemental mapping further demonstrates a more uniform distribution of major elements in the Kok Moh samples. Although both groups share broadly similar silica–alumina compositions, the observed microstructural differences suggest variations in clay preparation and firing practices rather than major differences in raw material selection. Comparison with published data from Nakhon Si Thammarat supports an association with kaolin-rich clay resources in southern Thailand. In contrast, the examined ceramics differ from fine-paste wares reported from northeastern Thailand, Myanmar, and India. These findings suggest that maritime Southeast Asian fine-paste ware developed as a localized technological tradition shaped by regional resources, production practices, and maritime exchange networks.

Sci doi: 10.3390/sci8060139

Authors: Giuseppe Giardullo Manuele Taleb Gaetano Raiola Ruggero Andrisano Ruggieri Giuseppe Di Lascio Rosario Ceruso

Youth soccer performance is influenced by multiple factors, including age, body size, and physical capacities, but the relative contribution of these variables to aerobic and explosive performance remains unclear. Understanding these relationships can improve the interpretation of field tests and support individualized training prescription. This study was designed to examine the association of age category, body mass, and height with physical performance in youth soccer players by jointly considering aerobic and explosive capacities, in order to support the interpretation of field tests within training prescription. Forty-five male players (15 U16, 15 U17, 15 U19) from the same club were assessed across two standardised on-field testing sessions, including the 45–15 test (estimated maximal aerobic speed, MAS) and vertical jump tests (squat jump, SJ; countermovement jump, CMJ; countermovement jump with free arms, CMJ_FH). Performance variables (SJ, CMJ, CMJ_FH, MAS) were treated as outcomes, while category, body mass, and height were included as predictors. A multivariate analysis was performed, followed by univariate analyses for each indicator. Results showed a significant multivariate effect of age category on overall performance (p < 0.001; η2p = 0.482), whereas height and body mass were not significant (p > 0.05). In univariate analyses, age category was associated with all variables: SJ (p = 0.005; adj. R2 = 0.160), CMJ (p < 0.001; adj. R2 = 0.287), CMJ_FH (p = 0.004; adj. R2 = 0.173), and MAS (p < 0.001; adj. R2 = 0.352). Performance increased progressively from U16 to U17 to U19, with larger between-category differences in aerobic capacity. In conclusion, age category was more strongly associated with the performance profile than height and body mass when considered jointly; these findings should be interpreted in light of the observational design and the lack of biological maturation measures.

Sci doi: 10.3390/sci8060138

Authors: Jainara Lima Menezes Felipe J. Aidar José Uilien de Oliveira Jymmys Lopes dos Santos Anderson Carlos Marçal Nuno Domingos Garrido Pantelis T. Nikolaidis

Paralympic Powerlifting is a maximal strength sport that requires high training intensity and efficient recovery to maintain performance and prevent injury. Intense training can induce oxidative stress, potentially compromising performance and physiological adaptation. Caffeine (CA) is a widely used ergogenic aid with potential to enhance muscle strength and reduce oxidative damage. This study investigated the acute effects of CA supplementation (9 mg/kg) on static strength indicators and oxidative stress markers in 14 male Paralympic powerlifting athletes. The protocol included two conditions (CA and placebo), with athletes performing 5 sets of 5 repetitions at 80% of 1 RM in the adapted bench press. Strength variables (maximal isometric force, 1-s peak force, and force variability) and biomarkers (TBARS/MDA, total antioxidant capacity, sulfhydryl groups, and uric acid) were analyzed. CA supplementation significantly increased maximal isometric strength, improved 1-s peak force, and reduced variability, suggesting enhanced motor unit recruitment. However, CA also led to increased oxidative stress, as evidenced by elevated TBARS levels, reduced antioxidant capacity, decreased sulfhydryl groups, and higher uric acid concentrations post-exercise. In conclusion, while CA acutely improves neuromuscular performance, it does not attenuate—and may even exacerbate—exercise-induced oxidative stress. Therefore, its use in strength sports should be approached with caution.

Sci doi: 10.3390/sci8060137

Authors: Adisak Bootwong Rattabal Khunphonoi Aphirak Loykaew Shin Sato Mathana Wongaree

TiO2/activated carbon (TiO2/AC) was coated on mesh using the dip-coating method employing polyvinyl pyrrolidone (PVP) as a copolymer via a simple mixing method. The obtained catalyst was used to treat real wastewater from a building discharge with a horizontal photoreactor with a continuous flow system. The synthesis of 20%wt TiO2/AC-coated mesh was performed with a fixed TiO2:AC ratio of 1:0.05 wt/wt, as confirmed by employing various characterization techniques, which resulted in a composite TiO2/AC characterized by XRD, FE-SEM, and EDS, confirming the uniform distribution of TiO2/AC nanoparticles coated on mesh substrates. The influence of vital parameters on the best conditions of the photoreactor design, including flow rate (8.0 L/min), light intensity position (5.0 cm), and the number of mesh layers (20 mesh layers), was systematically examined during photocatalytic oxidation. The treatment efficiency of domestic building wastewater was evaluated using a TiO2/AC coated on mesh under visible light irradiation for 120 min. The recirculating batch photoreactor was operated at a continuous flow rate of 8.0 L/min, corresponding to a total treated wastewater volume of 960 L. In real wastewater treatment, the optimized TiO2/AC-coated mesh exhibited the highest activity, achieving approximately 68% BOD removal and 65% COD removal. This study indicates that TiO2/AC-coated mesh can be a good candidate for building wastewater treatment systems using photocatalytic activity under visible light irradiation. However, the TiO2/AC coating exhibited limited reusability, with BOD degradation decreasing to 53% after three cycles. Future work must develop a more stable binder to improve its durability and reusability.

Sci doi: 10.3390/sci8060136

Authors: Andréia de Cássia Rodrigues Soares Alarcon Giovana Kátia Viana Nucci Elaine Silva de Pádua Melo Marta Aratuza Pereira Ancel Regiane Santana da Conceição Ferreira Cabanha Rita de Cássia Avellaneda Guimarães Karine de Cássia Freitas Valter Aragao do Nascimento

Lithium carbonate is a cornerstone therapy for bipolar disorder, typically administered long-term, which necessitates strict control of elemental impurities beyond the quantification of the active ingredient. While previous studies focused on lithium concentration and dosing accuracy, this study characterized the unique inorganic signatures and evaluated the toxicological implications of reference, similar, and generic lithium carbonate formulations marketed in Brazil. Seven commercial brands were analyzed by inductively coupled plasma optical emission spectrometry (ICP OES). Elemental concentrations (mg/kg) ranged as follows: As (0.50–0.62), Pb (0.39–0.57), Se (0.80–1.01), Cr (detected in one similar formulation at 0.18), Fe (<LOD–0.86), Mg (8.10–14.65), K (1.18–4.2), Mn (0.072–0.40), and P (24.3–74.4), while Cd, Cu, and Zn were below detection limits. Statistical analysis (p < 0.05) demonstrated significant inter-manufacturer differences, indicating that pharmaceutical equivalence does not imply inorganic identity. Despite this variability, all formulations complied with ICH Q3D (R2), USP <232>, and Brazilian Pharmacopoeia limits. Under maintenance doses of 600–1200 mg/day, daily exposure remained well below Permitted Daily Exposure thresholds; the cumulative Hazard Index was <0.02, and Incremental Lifetime Cancer Risk (5.46 to 6.80 × 10−6) was within safe levels. These findings confirm that while distinct elemental signatures exist, the medications are toxicologically safe for chronic therapy.

Sci doi: 10.3390/sci8060135

Authors: LinShu Liu Karley K. Mahalak Adrienne B. Narrowe Jenni Firrman

A stable gut microbial community (SGMC) created by the culture of fecal samples in a chemostat-based gut model is a physiologically relevant, reproducible tool for ecological research on the gut microbiome. It is also a subject of continuous improvement for the model to better mimic the in vivo system. In this research, compositions of fecal homogenates and the derived SGMC were analyzed based on shotgun metagenomic sequencing. It was found that taxa in the fecal inocula could be divided into resilient or sensitive types according to their response to environmental factors; taxa were sorted into three subsets—shared, emerged, and lost—by how they were represented in the established communities. The first two subsets consisted of resilient species, constituting an SGMC, which was suitable for long-term, ecological research. In contrast, species of the lost subset were sensitive to the new environmental conditions and were missing from the SGMC. However, these lost species may carry host-dependent information that is useful or even critical for drug and functional foods development or nutrition research. The differentiation between the three subsets reveal the community shift and metabolic profile along with SGMC formation and could serve as a diagnosis for understanding the extent to which the SGMC mimics, or differs from, the actual ecosystem in vivo or the donor’s fecal samples, which may be useful in work to upgrade in vitro models.

Sci doi: 10.3390/sci8060134

Authors: Leidy Johana Tobar-Miranda Angela María Tobar-Miranda Nicolas Martínez-Mera Mario Fernando Muñoz-Velez Howard Ramírez-Malule Andrea Carolina Acosta-Tirado Jose Herminsul Mina-Hernandez

A controlled composting biodegradation system was implemented to evaluate a wood–plastic composite (WPC) composed of wood fibers and recycled HDPE (rHDPE), in accordance with ASTM D5338, by measuring CO2 capture over 45 days. This evaluation was complemented with mechanical and physicochemical characterization, including stereomicroscopy/SEM, mass loss, water absorption, contact angle, tensile strength, FTIR, TGA, and DSC. The results showed 6.12% biodegradation, classifying the material as neither biodegradable nor compostable. SEM analysis revealed increased surface roughness, cracks, and microbial-like structures, together with a 10% decrease in contact angle. The mechanical properties declined by 33% (tensile strength), despite only 1.26% mass loss, which was attributed to weakening of the matrix–fiber interfacial adhesion due to water absorption. TGA, DSC, and FTIR supported the interpretation that degradation occurred preferentially in the wood fibers.

Sci doi: 10.3390/sci8060133

Authors: Sohan Kumar Y Madhav Sonkusare Niranjan N Prabhu Krishna Kumar P Nagaraja Shetty

Fibre-reinforced polymer composites incorporating synthetic reinforcements such as glass and carbon fibres are widely used due to their superior mechanical performance. However, their energy-intensive production and end-of-life disposal contribute to an increased carbon footprint and significant environmental burden. Natural fibre-reinforced composites have emerged as promising low impact alternatives, but variability in their mechanical performance and the lack of controlled comparative studies limit their structural application. This study presents a controlled experimental comparison of alkaline-treated unidirectional flax/epoxy and hemp/epoxy composites fabricated using the vacuum-assisted resin infusion moulding (VARIM) process. Alkali treatment was employed to enhance the fibre–matrix interfacial bonding. Mechanical characterization was conducted through tensile, flexural, impact, interlaminar shear strength (ILSS), and Vickers microhardness testing in accordance with relevant ASTM and ISO standards. The flax/epoxy composites exhibited superior in-plane mechanical performance including, 9.1% higher tensile modulus, 13.8% higher flexural strength and 20.5% higher flexural modulus compared to hemp/epoxy composites. A significant improvement was observed in impact performance, with hemp composites showing 87.4% higher impact strength, indicating enhanced resistance to dynamic loading. Conversely, hemp/epoxy composites demonstrated a 10.6% higher ILSS, suggesting improved interfacial shear resistance and fibre interlocking. These findings confirm that the fibre type significantly influences composite performance, with flax fibres providing superior stiffness and strength, while hemp fibres offer better interlaminar shear behaviour and impact strength. Scanning Electron Microscopy (SEM) fractographic analysis was additionally conducted on fracture surfaces to characterize failure mechanisms and fibre–matrix interfacial morphology. The present study provides a reliable comparative framework for material selection and demonstrates the potential of flax- and hemp-based composites as sustainable alternatives for lightweight structural applications. This study supports the development of sustainable composite materials and contributes to the United Nations Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 11 (Sustainable Cities and Communities).

Sci doi: 10.3390/sci8060132

Authors: Nikolay Lyubomirskiy Aleksandr Bakhtin Alexey Gusev Tamara Bakhtina German Bilenko Valentina Volchenkova Ivan Tyunyukov Wolfgang Linert

The article presents the results of experimental studies on the possibility of predicting the efficiency of CO2 mineralization using metallurgical wastes (MWs) from the perspective of chemical thermodynamics and on identifying, accordingly, promising MWs for the production of construction materials and products. The study examined MWs from major Russian iron and steel producers, namely: blast furnace, electric steelmaking, ferroalloy, converter steelmaking slag, as well as nepheline slag, a by-product of nepheline ore processing for alumina. The CO2 binding capacity of MWs was determined using experimental samples fabricated by semi-dry pressing of MW powders, followed by curing them in a gas atmosphere with an CO2 concentration of 80% vol. It was found that the investigated MWs are capable of absorbing and binding CO2, thereby improving their physical and mechanical properties. Experimental samples made from nepheline slag bind 11.3 to 12.0 wt.% of CO2; samples from steelmaking slags: up to 9 wt.% or more; and samples from blast furnace dump slag: approximately 5.5 wt.% At the same time, the compressive strength of samples from steelmaking slags exceeds 100 MPa, that of samples from nepheline slag approaches 80 MPa, and that of samples from blast furnace dump slag exceeds 50 MPa. It has been established that predicting the efficiency of CO2 mineralization by metallurgical wastes based solely on chemical thermodynamics is not entirely accurate. To develop a preliminary forecasting model for the carbonate hardening potential of various MWs, further studies are needed to identify additional key factors influencing the carbonate hardening process of MWs.

Sci doi: 10.3390/sci8060131

Authors: José Uilien de Oliveira Felipe J. Aidar Jessica Denielle Matos dos Santos Greice Itamaro Heiden Ricardo Luiz Cavalcanti de Albuquerque-Júnior Jymmys Lopes dos Santos Pantelis T. Nikolaidis

Resistance training (RT) can help with injury recovery and the healing process. Still, high-intensity exercise can cause ischemia and reperfusion, resulting in exacerbated production of reactive oxygen species and oxidative stress. This study aimed to evaluate the effects of RT with progressive loads on markers of tissue damage and oxidative stress in rats subjected to skin lesions. Forty male Wistar rats were used, divided into four groups (n = 10): Control (CG): no intervention; Sedentary Injury (SHAM): subjected to injury, no training; Training + Injury 1 (G1): injury after one week of training; Training + Injury 2 (G2): injury followed by training. The protocol consisted of climbing a vertical ladder three times a week, 48 h apart, using progressive loads (50%, 65%, and 80%). After euthanasia, markers of tissue damage (CK, LDH, ALT, AST), oxidative stress (MDA/TBARS, SH, uric acid), and histological analysis of collagen deposition in the injured tissue were assessed. Groups G1 and G2 showed a significant increase (p < 0.0001) in CK, LDH, ALT, and AST levels compared to GC and SHAM. Oxidative stress markers, such as MDA and SH, were also elevated in the G1 and G2 groups (p < 0.0001). Uric acid concentrations increased significantly in the exercised groups compared to the controls (p < 0.0001). Histology revealed an inflammatory infiltrate and disorganized collagen fibers in the SHAM group, while G1 and G2 showed tissue with greater cellular maturity and organization. Although RT induced muscle damage and an increase in pro-oxidant markers, it also favored cellular organization and scar tissue quality.

Sci doi: 10.3390/sci8060130

Authors: Érika Leite Ferraz Libório Karine de Cássia Freitas Arnildo Pott Rosângela dos Santos Ferreira Aline Carla Inada Priscila Aiko Hiane Juliana Rodrigues Donadon Valter Aragão do Nascimento Rita de Cássia Avellaneda Guimarães

The tumor microenvironment of breast cancer presents high complexity and resistance to conventional therapies. Ferroptosis, a programed cell death that is dependent on iron and characterized by lipid peroxidation, arises as a promising therapeutic goal. This scoping review mapped evidence on the exogenous use of iron and selenium, in conventional or nano-particulated forms, in the modulation of ferroptosis as therapeutic strategy for breast cancer treatment, identifying knowledge gaps and opportunities for future research. We performed a scoping review and the methodology followed the guidelines of the Joanna Briggs Institute (JBI) and PRISMA-ScR. We made a systematic search in five data bases (Embase, Lilacs, PubMed (MEDLINE), Scopus, and Web of Science) between the years 2012 and 2025. Among 2.723 identified publications, we selected 48 studies. The results revealed predominance of nanoplatforms of iron (97.9%), focused on the Fenton reaction. The modulation of selenium for inactivation of GPX4 was shown to be effective, though still little-explored (n = 1). We evidenced that the induction of ferroptosis potentializes tumor immunogenicity and the effectiveness of combined therapies. We conclude that the field is under development; thus, the diversification of metabolic targets and trials of chronic toxicity are fundamental steps for future clinical research.

Sci doi: 10.3390/sci8060129

Authors: Ziad M. Hawamdeh Tasneem N. Alhosanie Ali H. Otom Amira S. Serhan Mustafa I. Saadeh Ahmed M. Jouda Rawan S. Mousa Dania F. Naser Majd Z. Hawamdeh

Background: Predicting rehabilitation outcomes at admission supports tailored therapy plans and efficient use of resources for patients undergoing intensive inpatient rehabilitation, including those with stroke, orthopedic, and other neurological conditions. Nonetheless, current machine learning (ML) methods face limitations, including the ceiling effect in absolute functional gain measures, the uniform treatment of diverse patient groups, and reliance on black-box models that lack clinical transparency. Methods: This retrospective observational study analyzed a fully anonymized, publicly available dataset of 3419 patients admitted to the intensive rehabilitation unit at IRCCS San Raffaele Hospital, Rome, Italy, from 2015 to 2018. To mitigate the ceiling effect, a normalized Barthel Index gain metric was developed. K-means clustering (K = 2, trained solely on the training set) identified patient admission profiles based on functionality, which were then used as predictive features. Eight machine learning classifiers were tested across three groups (All Patients, Orthopedic, Neurological). SHAP-based explainability was employed at four levels: global, diagnostic group, patient functional profile, and individual. Finally, clinical decision rules and bedside stratification profiles were derived and validated with an internal held-out test set (n = 684). Results: Normalization significantly increased the correlation between admission BI and gain (r = 0.130 to r = 0.520), supporting the presence of a ceiling-related limitation in absolute gain metrics. Two distinct functional admission profiles with statistically significant group differences were identified—High-Burden (38% below-median recovery) and Moderate-Burden (21%)—with cluster membership the third most important predictor (13.9% SHAP importance). The highest AUC-ROC values were 0.831 for all patients (XGBoost), 0.864 for neurological patients (Gradient Boosting), and 0.839 for orthopedic patients (Gradient Boosting). Multilevel SHAP analysis showed age as the primary predictor for neurological patients (mean |SHAP| = 0.360) but the third for orthopedic patients (0.350), highlighting clinical relevance. Validation using SHAP values from the Gradient Boosting model showed a Spearman correlation of ρ = 0.925 (p = 1.13 × 10−30), with eight of the top ten features overlapping, indicating that these patterns are not model-specific but reflect the underlying data. Risk zone stratification found 80.7% of patients in high-confidence zones (accuracy > 80%). The clinical decision rules achieved 70.8% accuracy with full transparency, and the elderly (≥75 years) combined with a low BI (<25) profile showed an 89.6% model accuracy with only 10.4% recovery above the median. Conclusions: This explainable, profile-informed ML pipeline addresses key methodological limitations in predicting rehabilitation outcomes. It also provides a foundation for integrating models into clinical practice, pending prospective, external validation of the results. Before clinical implementation, validation across multicenter cohorts is essential.

Sci doi: 10.3390/sci8060128

Authors: Umanath R. Poojary Ananda Hegde Sriharsha Hegde

Mechanical components subjected to dynamic loading require materials that combine adequate strength with effective vibration-damping capability. Austempered ductile iron (ADI) is a promising candidate for such applications because of its ausferritic matrix, which provides a useful combination of strength, toughness, wear resistance, and energy dissipation. However, the damping behaviour of manganese-alloyed ADI and its dependence on austempering parameters have not been sufficiently clarified. In this study, ductile iron containing 0.3, 0.6, and 0.9 wt% Mn was austempered at 320, 370, and 420 °C for 1, 1.5, and 2 h using a full-factorial experimental design. The damping response was evaluated through impact hammer-based experimental modal analysis and correlated with hardness, ausferritic morphology, and the volume fraction of carbon-enriched/high-carbon austenite. The results showed that manganese content and austempering temperature significantly influenced the loss factor, whereas austempering time had only a minor effect within the selected range. The highest damping performance was obtained for the alloy containing 0.6 wt% Mn austempered at 370 °C, where a favourable balance was achieved between stabilized high-carbon austenite, refined ausferritic morphology, ferrite/austenite interface density, and controlled matrix hardness. At 320 °C, limited austenite stabilization restricted damping improvement, while at 420 °C, ausferritic coarsening reduced the effective interface-related energy dissipation. ANOVA confirmed manganese content and austempering temperature as the dominant factors, contributing approximately 59% and 39%, respectively, to the variation in loss factor. The regression model showed strong predictive capability within the investigated process window. Overall, the study demonstrates that damping behaviour in manganese-alloyed ADI can be effectively tailored through controlled alloy chemistry and austempering temperature, supporting its potential use in vibration-sensitive engineering components.

Sci doi: 10.3390/sci8060127

Authors: Hiedar Alyami Mohammed Al-Hariri

Background: This study evaluated the effects of combined exercise (CE) alone and CE combined with Nigella sativa (NS) supplementation on musculoskeletal performance and blood fructosamine levels in male patients with Type 2 diabetes mellitus (T2DM). Methods: Ninety male patients were randomly allocated to one of three groups in a 1:1:1 ratio: a non-exercise comparator (Diabetes), a Diabetes + CE group, or a Diabetes + CE + NS group (n = 30 per group). NS was administered orally (2 g/day) for four weeks. Functional performance outcomes included the six-minute walk test, timed up-and-go test, handgrip strength, and sit-to-stand repetitions. Glycemic control was assessed using blood fructosamine at baseline and after four weeks. Results: Both intervention groups showed significant improvements in all functional outcomes and significant reductions in BMI and fructosamine compared with the non-exercise comparator group (p < 0.05). Post-intervention blood fructosamine was significantly lower in the CE + NS group than in the CE group (p = 0.002). Conclusions: CE significantly improved musculoskeletal performance and short-term glycemic control. The addition of NS appeared to confer additional benefits, particularly on glycemic control and upper- and lower-limb strength, although results should be interpreted with consideration of the short intervention duration, the male-only sample, and reliance on BMI as the body composition measure.

Sci doi: 10.3390/sci8060126

Authors: Erick Galicia Vargas Alfredo González Ortega Jesús Aguayo Alquicira Mario Ponce Silva Susana Estefany de León Aldaco

This study evaluates the technical feasibility of an autonomous photovoltaic pumping system for agricultural use in isolated communities, using a representative region of the Mixteca Poblana, Mexico, as a case study. A reference sizing methodology reported in the literature was adopted for the sizing of isolated systems, and subsequently enhanced through a structured methodological extension, applied in the final stage of the design, focused on the technical validation and commercial selection of system components. The base framework incorporates site characterization and crop selection criteria. Subsequent stages define the hydraulic and electrical design requirements for the extension of the methodology, such as the calculation of water demand, the determination of pump power, and the estimation of energy requirements. These parameters enable the integrated correlation between hydraulic demand and electrical system constraints in the selection of the main system components, including the pump, photovoltaic array, battery storage system, water storage tank, and inverter. The technical robustness of the combined approach was validated through a simulation performed using specialized solar pumping software, confirming the operational feasibility and replication potential in rural communities with similar conditions.

Sci doi: 10.3390/sci8060125

Authors: Jean Paul Restucci-Orozco Mario Fernando Muñoz-Velez Niny Andrea Arteaga-Pedraza Carlos David Grande-Tovar Carlos Humberto Valencia-Llano Jose Herminsul Mina-Hernandez

Critical-sized bone defects remain a major reconstructive challenge, and the Masquelet technique has become an important option in traumatic, infectious, and post-resection settings. This review examines the role of acrylic bone cement in this technique, emphasizing its evolution from a temporary spacer to an active biomaterial that shapes the induced membrane and the local regenerative microenvironment. The article discusses the surgical and biological basis of the technique, the composition and handling characteristics of polymethyl methacrylate (PMMA) bone cement, the rationale for antibiotic-loaded spacers, and the influence of spacer-related variables such as formulation, surface properties, and geometry on membrane quality. It also addresses emerging strategies, including bioactive PMMA modifications, multifunctional cements, and degradable alternatives aimed at improving osteogenesis, angiogenesis, and infection control. Current evidence, derived mainly from in vitro studies and animal models, suggests that the spacer may play a role beyond space maintenance by participating in induced membrane formation and influencing biological signaling related to bone repair. In contrast, the clinical evidence primarily supports the reproducible use of PMMA spacers for dead-space management, infection control, and bone reconstruction. However, important gaps still remain in the translational validation of these biological properties and in the standardization of spacer formulations and antibiotic protocols.

Sci doi: 10.3390/sci8060124

Authors: Paul Bakaki Michel Belyk Marcello Trovati Nik Bessis

Computational research is becoming increasingly dependent on code, data, workflows, software environments and model configurations that must be preserved and understood before findings can be reproduced. The FAIR Guiding Principles have significantly improved data stewardship, but they do not by themselves provide an executable, explainable and evidence-linked mechanism for verifying computational claims. This article presents eFAIR-X, an implementation-oriented and AI-enabled extension of FAIR for interdisciplinary computational reproducibility. The framework connects publications, claims, datasets, code, workflows, environments and verification evidence through a semantic research knowledge graph. It also defines a Dashboard for Reproducibility (DfR) that reports bounded, auditable and calibratable indicators for artefact availability, metadata completeness, workflow executability, output agreement, contribution-evidence coverage, relevance longevity and originality risk. In response to the need for stronger technical precision, the model separates three issues that are often combined: FAIR principle extension, FAIR assessment and operational reproducibility verification. A browser-based proof-of-concept prototype has now been implemented and exercised using structured JSON study files to demonstrate the dashboard, knowledge-graph view, evidence table, claim-evidence mapping and validation panel. The proposed metrics are explicitly treated as provisional operational indicators that require calibration through benchmark experiments, expert agreement analysis, case-based evaluation and sensitivity testing before they can be used as decision-support evidence. The paper further specifies local and global explainability mechanisms, human contestability, knowledge-graph node and edge semantics, metadata requirements and dashboard evidence drill-downs. eFAIR-Xis therefore positioned not as a replacement for FAIR, FAIR4RS or FAIRification frameworks, but as a complementary verification-centred infrastructure for making computational reproducibility more measurable, inspectable and actionable.

Sci doi: 10.3390/sci8060123

Authors: Yuliya A. Litvinenko Larissa R. Sassykova Albina R. Sassykova Azamat T. Konysbayev Renata R. Aitbayeva Tleutai S. Abildin Fatima M. Kanapiyeva Nurbubi K. Zhakirova Aisulu K. Zhussupova Subramanian Sendilvelan Kathirvelu Bhaskar Kannayiram Gomathi Ruimao Hua

This study investigated the macro- and microelement profiles and bioactive substances (BAS) in the herbaceous species of the genus Rheum collected from two villages near Almaty, Kazakhstan, and the accumulation of heavy metals (HMs) in wild plants of Eastern Kazakhstan. Representative zonal species from the steppes and desert–steppes (Eastern Kazakhstan) were analyzed—totaling one hundred samples across 18 species from six families. According to the research, different plant species have a selective ability to accumulate heavy metals, even when growing in the same type of soil. Long-term observations reveal dominant patterns of HM (Cu, Zn, Mn, Co, Pb, Cd) occurrence in dry-steppe vegetation. It was found that Cu and Co demonstrate moderate bioadsorption with minimal accumulation, while Zn, Mn, and Pb show strong biological enrichment. Cadmium falls under elements with pronounced biological retention. Copper and cobalt are classified as moderately absorbed with limited buildup. Native plants in this region contain substantially greater amounts of lead relative to cobalt, and reflect cadmium–zinc geochemical specificity: Cd 3.85, Zn 0.15, Mn 0.10, Pb 0.09, Co 0.07, Cu 0.04, per Clark’s concentration index. Samples of collected Rheum tataricum L. in the village of Miyaly were found to have higher levels of flavonoids, phenolic compounds, and organic acids, while raw materials from the village of Bakbakty showed increased accumulation of anthracene derivatives and tannins. Flavonoids constituted the largest proportion of the studied groups of biologically active substances in both samples, confirming the high biological and pharmacological value of the plant material studied. The results obtained during the research can be successfully applied to the development of a strategy for the conservation of plant biodiversity in the studied areas and the sustainable management of plant resources.

Sci doi: 10.3390/sci8060122

Authors: Joaquim Jose Carvalho Proença Carlos Enrique Bermudes Mendoza Rosita Elvira Alcantara Poma Nelly Gisella Quispe Quispe Carmen Ramos Vera

Artificial intelligence (AI) has become a central driver of transformation in innovation management, reshaping how organizations design strategies, develop offerings, and generate knowledge. This study examines how innovation management has evolved from the pre-ChatGPT era—characterized by analytics, automation, and decision support—to the post-ChatGPT period, marked by the widespread adoption of generative AI (GenAI) and human–AI collaboration. Using a structured literature review of Scopus-indexed studies published between 2020 and 2025, the paper identifies the following six dominant thematic dimensions of AI-enabled innovation management: strategic and business model innovation, product and service innovation, sustainability-oriented innovation, organizational agility and capabilities, human-centric innovation, and knowledge, learning, and research. The findings reveal a conceptual shift from efficiency-driven applications toward more creative, strategic, and collaborative uses of AI, with generative models acting as co-creators rather than mere analytical tools. The study contributes by synthesizing the fragmented literature into an integrative framework that captures this transition and by highlighting emerging research gaps, particularly in sustainability and human-centered innovation. Practical implications for managers and policymakers are discussed.

Sci doi: 10.3390/sci8060121

Authors: Jean Paul Restucci-Orozco Luisa Fernanda Pacheco-Muñoz Carlos David Grande-Tovar Carlos Humberto Valencia-Llano Niny Andrea Arteaga-Pedraza Mario Fernando Muñoz-Velez Jose Luis Castillo-Garcia Jenny Alexandra Lugo-Peña Arturo Jose Aragón Juan Camilo Madrid-Paz Gustavo Urrego-Grueso Jose Herminsul Mina-Hernandez

A porcine model with a stabilized segmental femoral defect was used, in which commercial or experimental bone cements were implanted following the principles of the Masquelet technique. After 45 days, considered long enough for induced membrane maturation, the samples were analyzed by optical microscopy (H&E, Masson’s trichrome, and Gomori staining) and scanning electron microscopy (SEM). Histologically, both formulations induced membranes with fibrovascular tissue organization; however, the membranes associated with the experimental cement exhibited qualitatively distinct patterns of stromal organization and cell distribution compared with those of the commercial cement group. SEM analysis revealed qualitative differences in the material–tissue interaction, with the experimental cement showing a distinct distribution pattern of amorphous and fibrillar material on the surface and within the interpearl spaces, whereas the commercial cement exhibited a more focal interaction, predominantly associated with structural irregularities. Overall, these observations indicate that differences in the formulation and microstructure of bone cements may influence how tissue organizes and interacts with the material and may be associated with qualitative differences in tissue organization and material–tissue interaction within the induced membrane. These results highlight the relevance of the spacer type in the histological characteristics of the induced membrane.