Search Results (9,659)

Background/Objectives: Goupi plaster (GP) is a traditional black plaster composed of a biphasic fibrous–oil matrix containing multiple bioactive compounds, and it has been widely used for the treatment of musculoskeletal disorders. Representative active compounds include sinomenine, osthole, cinnamaldehyde, and imperatorin, which exhibit anti-inflammatory and analgesic effects. However, due to its heterogeneous matrix structure and multi-component nature, the pharmaceutical delivery behavior of GP remains difficult to evaluate using conventional methods. Therefore, this study aimed to establish an integrated structure–release–permeation–pharmacokinetic evaluation framework to systematically characterize the transdermal delivery behavior of GP. Methods: GP was evaluated using multi-level analysis, including microstructural imaging (FESEM), in vitro release, ex vivo skin permeation, and in vivo dual-site microdialysis. Four representative bioactive compounds (sinomenine, osthole, cinnamaldehyde, and imperatorin) were selected as marker compounds. Release data were fitted to kinetic models, and structure–release relationships were examined using the Higuchi release constant (k_h). Skin-barrier alterations were assessed by attenuated total reflectance–Fourier transform infrared spectroscopy (ATR–FTIR) and differential scanning calorimetry (DSC). Local concentrations in subcutaneous (SC) and intra-articular (IA) compartments were measured by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) to explore potential in vitro–in vivo correlation (IVIVC). Results: FESEM revealed a fibrous–oil network structure. GP exhibited sustained, diffusion-dominated release, with k_h = 0.9908–0.9977 and Korsmeyer–Peppas (K–P) release exponents (n) = 0.61–0.66, differing from active pharmaceutical ingredient (API) controls. Fiber area fraction and fiber length density showed negative correlations with k_h (r = −0.91 to −0.99); ex vivo permeation profiles varied among compounds, and ATR–FTIR and DSC analyses showed moderate changes in skin-barrier properties. Dual-site microdialysis demonstrated sustained local exposure, and a positive relationship was observed between in vitro release and in vivo concentrations. Conclusions: This study establishes an integrated structure–release–permeation–pharmacokinetic evaluation framework for traditional black plaster systems. The observed IVIVC is descriptive rather than predictive, reflecting a trend-level association under the current experimental conditions. These findings highlight the importance of integrating in vitro release, skin permeation, and local pharmacokinetics for understanding drug delivery behavior in complex transdermal matrix systems, and provide a methodological basis for quality consistency evaluation of traditional black plaster formulations. Full article

Understanding the hydrochemical characteristics and formation mechanisms of rivers in the Huxi Catchment is essential for water resource conservation, as these rivers serve as the primary water source for Taihu Lake. A total of 14 surface water samples were collected from the rivers in Huxi catchment, and the concentrations of seven major ions—namely, ${\mathrm{N}\mathrm{a}}^{+}$, ${\mathrm{K}}^{+}$, ${\mathrm{C}\mathrm{a}}^{2+}$, ${\mathrm{M}\mathrm{g}}^{2+}$, ${\mathrm{C}\mathrm{l}}^{-}$, ${\mathrm{S}\mathrm{O}}_4^{2-}$, and ${\mathrm{H}\mathrm{C}\mathrm{O}}_3^{-}$—were determined. Positive Matrix Factorization (PMF), Absolute Principal Component Score–Multiple Linear Regression (APCS-MLR), and the Principal Component Analysis-based Endmember Mixing Model (PCA-EMM) were employed to quantify the contributions of anthropogenic activities. While APCS-MLR can only identify the impacts of human activities, PMF and PCA-EMM can further distinguish between agricultural activities and wastewater discharge. Significant positive correlations were observed between the PMF and PCA-EMM results, but PMF overestimated the contribution of anthropogenic impacts. PCA-EMM showed that the natural background accounted for 63%, while human activities contributed 37% (domestic sewage 23%, agricultural activities 14%). By integrating ion composition data from representative sources, PCA-EMM overcomes the limitations of traditional methods that lack source verification and provides robust methodological support for the source apportionment of water chemistry. Full article

Introduction: This systematic review examines the association between ultraprocessed food (UPF) intake and cognitive and executive function in adults. Given the global rise in overweight and obesity and the increasing consumption of UPFs, understanding their potential impact on brain health is of growing importance. Method: A comprehensive literature search was conducted in PubMed, EBSCO, and Scopus databases following PRISMA guidelines. Fourteen studies met inclusion criteria, encompassing cross-sectional, longitudinal, and experimental designs. Risk of bias was assessed using the National Institutes of Health Quality Assessment Tool. Results: The majority of studies (78.5%) reported a significant association between higher UPF consumption and poorer cognitive outcomes, including deficits in memory, executive function, and global cognition. Longitudinal studies consistently demonstrated that increased UPF intake is linked to accelerated cognitive decline and a higher risk of mild cognitive impairment and dementia, particularly in middle-aged and older adults. In contrast, cross-sectional findings were more heterogeneous, and evidence in younger populations remains limited and inconclusive. Conclusions: Overall, the findings suggest that high UPF consumption may be a modifiable risk factor for cognitive decline. However, methodological variability and the predominance of observational studies highlight the need for further longitudinal and experimental research to clarify causal mechanisms. Full article

The electrification of floating production, storage, and offloading (FPSO) units has emerged as a strategic solution to meet the growing demand for increased oil production while reducing carbon emissions associated with onboard gas turbine generation. Power-from-shore (PFS) systems represent a promising approach to achieving this goal, with transmission technologies based on high-voltage direct current (HVDC) and high-voltage alternating current (HVAC) solutions. Although HVDC is more suitable for long-distance and high-power applications, HVAC systems offer advantages in terms of robustness, simplicity, and operational maturity. Nevertheless, the reactive power compensation requirements arising from the high capacitance of submarine cables remain a major technical challenge. This study investigates and compares several reactive power compensation topologies applied to three distinct PFS systems. The proposed methodology enables a comprehensive evaluation of both onshore and subsea reactor placement strategies under technically and technologically feasible conditions. The results demonstrate that long-distance transmission of 75 MW over 250 km was achieved exclusively through subsea compensation configurations, which maintained efficiencies above 90% and voltage and current profiles within operational limits. Conversely, onshore-only compensation proved to be the most efficient solution for shorter transmission distances. The results demonstrate that the full electrification of an FPSO is technically feasible, with voltage and current profiles remaining within acceptable operational limits. The findings also indicate that mid-cable reactor placement (at 50%) is not the most effective configuration, with superior results observed for placements at 20–80% and 40–70% of the cable length. Overall, the outcomes confirm that subsea reactor placement enables higher power transfer over longer distances, significantly extending the technical boundaries traditionally separating HVDC and HVAC solutions. These results emphasize the need for continued technological development to make subsea shunt reactor installation a viable and reliable option for future FPSO electrification projects. Full article

In recent years, sustainability reporting has taken on an increasingly important role in corporate strategy discussions. Initially a voluntary tool, it has become a benchmark for measuring and communicating the environmental, social, and economic impacts of organizations in a structured way. In light of the Corporate Sustainability Reporting Directive (CSRD), the research assumes that reporting is a lever for change and organizational innovation. To test the hypothesis, the authors chose to use what is known as a ‘natural experiment’: they observed the experience of a particular type of company, benefit corporations, which are required to publish an annual impact report. The empirical investigation was conducted using content analysis methodology to read the impact reports, websites, and social media channels of the companies, as well as case studies through semi-structured interviews. The findings of this study suggest that sustainability reporting can play a role that goes beyond transparency and compliance. In the cases analyzed, the preparation of the sustainability or impact report appears to function as an organizational coordination mechanism that mobilizes internal and external stakeholders. Through this process, companies progressively develop shared interpretations of sustainability objectives, experiment with measurement practices and introduce organizational changes affecting work organization, production processes and value-chain relationships. Full article

Objective: Deep Brain Stimulation (DBS) has been consolidated as a valid therapeutic option for advanced Parkinson’s disease (PD). The identification of specific targets can be achieved through different methods, including conventional direct and indirect methods. The aim of our multicentric study is to provide a comparison between these traditional methods and artificial intelligence (AI) in the ascertainment of the ideal targets. Materials and Methods: A total of eight patients, who received bilateral STN (subthalamic nucleus) DBS implantation between 2022 and 2023 were analyzed. Target coordinates were calculated based on the Schaltenbrand and Wahren atlases and the AI using the RebrAIn system during the planning phase; intraoperatively, the targets were either confirmed or adjusted according to microelectrode recordings (MERs). The differences in the three Cartesian axes of stereotactic coordinates (X, Y, and Z) according to these methods were evaluated and compared through non-parametric ANOVA Friedman test. Results: The results revealed significant agreement in the lateral–lateral coordinates (X, X′, X″), indicating stability in target determination along this axis across the methods. However, more substantial discrepancies were observed in the antero-posterior and cranio-caudal coordinates, suggesting lower consistency between the examined methodologies. Conclusions: Our preliminary study results suggest that, despite the challenges posed by interindividual anatomical variability and the limitations of imaging techniques, artificial intelligence has shown comparable values on the lateral–lateral X coordinates. The accuracy of predictive targeting using machine learning models needs to be validated by further studies, but the preliminary results appear to indicate a potential promising role for artificial intelligence in integrating the preoperative workflow. Full article

Dysregulated iron handling—including catalytic iron and ferroptosis, hepcidin–ferroportin signaling, ferritin dynamics, and neutrophil gelatinase-associated lipocalin (NGAL)-mediated siderophore transport—has been implicated in the initiation and propagation of acute kidney injury (AKI) across ischemia–reperfusion, sepsis, and nephrotoxic contexts. To provide a SANRA-aligned narrative synthesis of mechanistic and translational evidence on iron biology in AKI, clarifying biomarker readiness and therapeutic prospects while explicitly separating preclinical from human findings. PubMed, Scopus, and Web of Science (1 January 2000 to 30 September 2025), plus appraised grey literature (guidelines/registries) using predefined criteria (authority, update recency, and methodological transparency). Narrative review with comprehensive database searches, single-reviewer screening/extraction (acknowledged as a limitation), and qualitative synthesis. Evidence is organized by pathway (catalytic iron/ferroptosis, transferrin (Tf)/transferrin receptor (/TfR), ferritin/ferritin heavy chain (FtH), hepcidin–ferroportin and NGAL) and translational domain (biomarkers and therapeutics). Statements are tagged as [Preclinical] or [Human]. [Preclinical] Robust signals support roles for catalytic iron and ferroptosis, protection by iron chelation, hepcidin modulation, heme oxygenase 1 (HO-1)/FtH induction, and apotransferrin/NGAL-based strategies. [Human] Biomarkers such as NGAL show clinical utility for kidney injury detection, whereas catalytic iron assays (labile plasma iron [LPI]/bleomycin-detectable iron [BDI]) remain investigational with limited standardization. Observational links between iron-regulatory pathways and AKI risk exist, but interventional trials are sparse; dose, timing, and safety of iron-targeted strategies in defined AKI settings remain to be established. Iron-handling pathways are compelling targets for AKI prevention/mitigation, yet high-quality human trials are limited. Priorities include standardized catalytic-iron assays, biomarker-guided enrichment, and pragmatic trials of tractable interventions (e.g., peri-contrast or cardiopulmonary bypass settings). Until such evidence accumulates, recommendations beyond standard care should be avoided. Full article

Background/Objectives: Early detection through minimally invasive approaches is critical for timely patient stratification and optimal therapeutic decision-making in colorectal cancer (CRC). Liquid biopsy, based on the analysis of tumor-derived components in blood and other body fluids, has emerged as a promising strategy to overcome current limitations in CRC diagnosis and follow-up. This review evaluates the current landscape of liquid biopsy clinical trials in CRC, focusing on predictive biomarker detection, prognostic assessment, and disease monitoring. Methods: ClinicalTrials.gov was searched using the terms “colorectal cancer” and “liquid biopsy” yielding 153 registered trials. After manual screening, 44 trials were excluded for not using liquid biopsy for CRC management, leaving 109 trials for analysis. Of these, 25 were completed, and 13 had publicly available results related to liquid biopsy. Results: The included trials were conducted across 27 countries on four continents. Overall, 119 biomolecules assessments and 167 different endpoints were reported across 109 clinical trials. Because individual trials could evaluate multiple biomolecules and endpoints, counts exceed the total number of trials. Cell-free DNA (cfDNA) was evaluated in 92/109 trials (84%) and accounting for 77% of all biomolecule assessments. Circulatingtumor cells (CTCs) were analyzed in 9/109 trials (8%, representing 8% of all the biomolecules analyzed), and microRNAs (miRNAs) in 8/109 (7%, representing 7% of all the biomolecules analyzed). Treatment sensitivity was the most common endpoint (57/109, 52% of the clinical trials; representing 34% of all the 167 different endpoints analyzed), followed by disease progression (28/109, 26%; representing 17% of all the different endpoints analyzed) and diagnostic applications (21/109, 19%; representing 12% of all the different endpoints analyzed). Among the 25 completed studies, 10/25 (40%) were interventional and 15/25 (60%) observational, spanning 14 countries. The majority of completed trials (21/25, 84%) used cfDNA. Interventional studies were predominantly phase II (5/10), with fewer phase III trials (2/10), primarily evaluating treatment response, particularly in relation to EGFR inhibitors and RAS/BRAF mutation status. Four observational studies (4/15) investigated emerging biomarkers, including long noncoding RNAs and miRNAs. Conclusions: Current clinical trials highlight cfDNA as the dominant and most clinically advanced liquid biopsy biomarker in CRC, primarily used for treatment guidance and disease monitoring. In contrast, CTCs and RNA-based biomarkers remain underrepresented. The limited number of randomized late-phase trials, heterogeneity in study design, and technical challenges associated with emerging biomarkers underscore the need for standardized methodologies and robust validation before routine clinical implementation. Full article

Traditional cardiovascular assessment has historically focused on the isolated evaluation of either atrial or ventricular structure and function. However, the left atrioventricular coupling index (LACI) represents a paradigm shift by moving beyond single-chamber metrics to quantify the dynamic interaction between the left atrium and left ventricle. Defined as the ratio of left atrial end-diastolic volume to left ventricular end-diastolic volume, LACI integrates structural and functional aspects of cardiac performance. This comprehensive review examines the physiological basis of how the left atrium and ventricle operate as an integrated hemodynamic unit. We detail current measurement methodologies, including two- and three-dimensional echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging, which serves as the reference standard. Furthermore, the review explores the pathophysiological mechanisms driving atrioventricular uncoupling, specifically mechanical dysfunction, electromechanical desynchrony, and hemodynamic alterations. Extensive clinical evidence demonstrates LACI’s robust independent prognostic value across diverse cardiovascular conditions, such as heart failure, myocardial infarction, cardiomyopathies, and atrial fibrillation. Observational data suggest that LACI provides a promising prognostic value beyond established risk assessment tools by combining the assessment of both chambers’ interdependence. Finally, we outline future directions for clinical translation, highlighting the necessity for standardized measurement protocols, the integration of artificial intelligence, and the potential of LACI as a target for personalized therapeutic strategies. Full article

Trackways preserve sequential records of animal locomotion and provide some of the most direct evidence of locomotor behaviour in the vertebrate fossil record. Alternating short–long pace lengths have historically been used to infer gait irregularities such as limping or lateral limb preference, but these interpretations typically lack statistical validation, treating mean asymmetry as sufficient grounds for behavioural conclusions without first establishing whether observed differences exceed normal locomotor variability. This study introduces a quantitative framework that addresses this gap by applying Welch’s t-test to pace and stride length measurements, establishing statistical confirmation of asymmetry as a prerequisite for behavioural interpretation. The framework is demonstrated on nine dinosaurian trackways drawn from published data. While all had previously been interpreted as asymmetric, seven exhibited statistically significant pace asymmetry (p < 0.05) and two did not exceed the range of normal variation. Stride length showed no significant asymmetry in any trackway, confirming that pace-level metrics are more sensitive to limb bias than stride-based measures. This framework provides an objective, reproducible standard for evaluating asymmetry claims—a necessary and feasible methodological advance for vertebrate ichnology. Full article

Physics-informed deep learning models have received increasing attention in medical image segmentation and particularly in brain tumor analysis, owing to their ability to incorporate mechanistic prior knowledge into data-driven architectures. Although numerous studies have presented empirical observations using physics-informed constraints, considerably less attention has been paid to why, when, and under what conditions such prior knowledge meaningfully contributes to segmentation behavior. This study presents a theoretical and methodological analysis of physics-informed segmentation frameworks from an inductive bias perspective. Rather than proposing new models or numerical benchmarks, we examine how reaction–diffusion-based priors influence model behavior across different spatial scales, tumor growth assumptions, and imaging scenarios. We argue that physics-informed constraints function not as universal improvers but as context-dependent regularization mechanisms whose effectiveness depends on the alignment between biological assumptions and imaging characteristics. By reframing physics-informed segmentation as a problem of inductive bias compatibility rather than numerical optimization, this study clarifies the conceptual role of mechanistic priors and provides guidance for their principled use in future segmentation studies. To operationalize this perspective, we introduce two central constructs: a dimensionless scale ratio R that delimits the spatial regime in which reaction–diffusion priors remain valid, and an alignment function A that captures the compatibility between encoded biological assumptions and the underlying data-generating process. We formalize the conditions under which physics-informed regularization is expected to be beneficial, neutral, or potentially harmful, and propose a reporting framework for the transparent evaluation of physics-informed approaches. Full article

Background: Artificial intelligence (AI) is increasingly incorporated into nutrition research and practice; however, the extent of its clinical integration and impact on health outcomes remains unclear. This systematic review evaluated how AI-based systems have been implemented in human nutritional interventions and summarized reported outcomes. Methods: PubMed, Scopus, Google Scholar, SpringerLink, JMIR, and MDPI were searched from January 2020 to March 2025 (search completed in March 2025). Randomized controlled trials and prospective or retrospective cohort studies published in English or Spanish were included if they evaluated AI-driven nutritional interventions in human populations and reported health-related outcomes. Risk of bias was assessed using RoB 2 and ROBINS-I. A qualitative synthesis was performed. Results: Sixteen studies involving 10,863 participants were included. Most were randomized controlled trials targeting metabolic disorders, particularly type 2 diabetes and obesity. Eleven studies evaluated metabolic outcomes, including HbA1c, body weight, fat mass, lipid levels, and insulin resistance indices. Six studies assessed gastrointestinal symptom severity scores, and two examined quality-of-life or patient-reported outcomes. Several trials reported short-term improvements favoring AI-supported interventions in glycemic control, weight reduction, and symptom severity. However, effects were heterogeneous and often observed within multimodal programs, limiting attribution of outcomes solely to the AI component. Conclusions: AI integration in nutrition remains in an early phase of clinical implementation. Although preliminary findings suggest potential benefits, interpretation should be cautious given methodological heterogeneity and moderate-to-high risk of bias across studies. Larger, rigorously designed investigations are required to determine sustained clinical effectiveness. Full article

Impulsive alpha (α)-stable noise, characterized by heavy tails and intense outliers, is a key ingredient in simulating financial, medical, seismic, and digital communication technologies. It poses versatile challenges to conventional machine learning (ML) algorithms in predicting noise parameters for multidisciplinary artificial intelligence (AI)-embedded devices. In this study, we adopted a two-phase methodology to investigate the complexity and performance of supervised ML algorithms while classifying impulsive noise parameters. We generated synthetic datasets of α-stable noise distributions for experimentation in a controlled environment. It was followed by experimental evaluation to derive the complexity and performance of ML classifiers—k-nearest neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and Random Forest (RF). Moreover, we employed a very high channel noise level of −15 dB in the test datasets to ensure that the derived analysis applies to real-world devices. The results demonstrate the high performance of DT and RF in structured binary classification of the α regime and the sign of skewness, while incurring satisfactory computational costs. However, SVM and kNN are comparatively more robust for multi-class classification, albeit with higher memory and training costs. On the contrary, NB fails to address the skewed and impulsive behavior of α-stable noise. We observed that even the most effective classifiers struggle to achieve perfect accuracy in multi-class classification. Overall, the experimental results reveal significant trade-off relationships between the complexity and performance of ML classifiers. Conclusively, simple models are well-suited for coarse-grained tasks, such as α-approximation and sign-of-skewness classification. In contrast, sophisticated models can be deployed to predict noise parameters to some extent. Our study provides a clear set of trade-offs for future applied AI devices that address adversarial and impulsive noise. Full article

Optoelectronic motion capture systems provide objective and high-resolution measurements of upper limb kinematics. The hand-to-mouth movement is closely related to motor development in children. The “Rab Hand-to-Mouth protocol” (BTS Bioengineering) is widely used; however, its seated configuration constrains elbow posture and may limit the ecological validity of the movement. In this study, we propose a methodological adaptation of the protocol in a standing position to allow a more physiological elbow configuration and to increase the dynamic range of elbow and shoulder motion. The objective was to characterize kinematic patterns of the hand-to-mouth movement in typically developing children aged 4 to 9 years using this adapted setup. This study was designed as a descriptive analysis and does not aim to provide formal validation of the standing protocol against the original seated configuration. An observational study that included 40 children was conducted. Motion data were acquired using eight optoelectronic cameras (sampling frequency: 250 Hz) and 17 reflective markers placed on the trunk and upper limbs. Kinematic patterns and spatiotemporal parameters were computed using dedicated motion analysis software. No significant differences were observed between dominant and non-dominant limbs in spatiotemporal parameters, whereas kinematic differences were minimal and limited to trunk rotation, as identified by Statistical Parametric Mapping (SPM). Some isolated statistically significant associations with age were identified in specific spatiotemporal variables; however, these variables showed low coefficients of determination (R²), indicating limited explanatory power of age. Overall, kinematic parameters did not exhibit consistent age-related patterns. These findings provide preliminary descriptive data for hand-to-mouth kinematics in a standing condition, which may contribute to the future development of assessment protocols. However, the limited sample size and the absence of pathological populations restrict the direct generalization of these findings. Future studies should evaluate the applicability of this approach in clinical cohorts and explore its integration into sensor-based and data-driven models for movement analysis. Full article

This study evaluates deterministic flood fatality models using a harmonised dataset of river and flash flood events in Europe (1980–2024). The objective is to quantify differences across data sources and critically assess the applicability of commonly used prediction models for hydrological floods, with particular emphasis on flash floods, which remain poorly represented in existing methodologies. The analysis integrates large-scale databases on flood fatalities (HANZE, EM-DAT) with detailed event-based studies containing hazard and other indicators, enabling a combined evaluation from different sources. Three model groups are assessed by comparing predicted and observed fatalities: Damage–Fatality, Depth–Fatality, and Depth–Velocity–Fatality approaches. Results confirm discrepancy between exposure and mortality: river floods dominate in terms of affected population (87%) and economic losses (71%), whereas flash floods account for nearly half of all fatalities despite affecting only 13% of people. All evaluated models show significant limitations for prediction of flash floods fatalities; single-parameter approaches perform poorly, while multi-parameter models remain highly sensitive to uncertain hydraulic inputs. The study demonstrates that current methods are not transferable to flash flood conditions and highlights the need for integrated, multi-variable approaches supported by consistent and high-quality datasets. The main contributions of the study are the first systematic validation of widely used models against historical river and flash flood events, revealing their uncertainties, and a comprehensive assessment of their robustness and sensitivity to key input indicators. Full article