Search Results (1,138)

Chemotherapy-induced cognitive impairment (CICI), often referred to as “chemobrain,” is a common and sometimes persistent consequence of cancer treatment, characterized by deficits in memory, attention, executive function, and processing speed; it disproportionately affects older adults and women, suggesting a role for aging- and sex-related biological factors, including estrogen depletion. This work examines the potential of dietary phenolic compounds as multi-target modulators of mechanisms underlying CICI. A narrative synthesis of preclinical and clinical evidence was conducted, focusing on major phenolic subclasses (flavonoids, phenolic acids, stilbenes, lignans, and secoiridoids) and their effects on pathways implicated in chemotherapy-related neurotoxicity. The reviewed data indicate that phenolic compounds can influence redox balance, neuroinflammatory responses, mitochondrial function, synaptic plasticity, and estrogen-related signaling, with effects that appear to be structure-dependent; however, evidence remains heterogeneous and largely derived from experimental models rather than studies in humans. Overall, the current findings suggest that selected phenolic compounds could mitigate vulnerability to CICI, particularly in higher risk groups such as older individuals and women with low estrogen levels. These compounds represent promising and safe adjunctive strategies, although further well-designed clinical studies are needed to confirm their efficacy and clarify the underlying mechanisms. Full article

Traditional treatment strategies for Type 2 diabetes (T2D) adopt a “one-size-fits-all” approach, limiting individual effectiveness. This study presents an explainable, data-driven framework for multi-treatment and single-treatment selection of SGLT2 inhibitors (SGLT2-i) and DPP-4 inhibitors (DPP4-i) based on patient-specific health characteristics. Our approach evaluates treatment effectiveness across four outcomes—glycosylated hemoglobin (HbA1c), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and body mass index (BMI)—to enable individualized treatment recommendations. The multi-treatment model, based on multi-output regression, achieved an R² score of 0.44 and an RMSE of 5.58, identifying benefit subgroups for SGLT2-i and DPP4-i across all outcomes. Integrated with SHapley Additive exPlanations (SHAP) analysis, the model offers insights into the factors influencing treatment effects. The single-treatment selection algorithm achieved an accuracy of 0.47 and an F1 score of 0.46, showing a higher average treatment effect with SGLT2-i on all outcomes, notably in the reduction in HbA1c, LDL, and BMI and a modest increase in HDL. While DPP4-i demonstrated beneficial effects on HbA1c, LDL, and HDL, it was associated with an increase in BMI. These findings highlight the benefits of a multi-faceted, patient-centered precision medicine approach for T2D management, enabling treatment strategies that address individual health needs beyond HbA1c. Full article

Plasma-assisted nitrogen fixation has emerged as a promising strategy for sustainable nitrate production. However, the coexistence of multiple interfaces and complex multi-step reaction pathways within the plasma-liquid system often leads to the formation of mixed nitrogen species, posing a significant challenge for achieving high product selectivity. In this study, a dual-cell reactor was introduced in liquid-phase plasma (LPP) system, enabling selective product distribution. Optical emission spectroscopy revealed pronounced signals corresponding to the second positive system (SPS) of N₂ and the first negative system (FNS) of N₂⁺, indicative of strong plasma excitation and ionization processes that facilitated the formation of reactive nitrogen oxide intermediates. These species were subsequently converted into aqueous NO₂⁻ and further oxidized into NO₃⁻ only in the reaction cell where reactive species are generated. The effects of key parameters, including electrode material, treatment time, solution pH, and discharge conditions, were comprehensively evaluated. As a result, the reaction cell achieved a nitrate selectivity of 98.9%, whereas the absorption cell achieved a nitrite selectivity of 100%. Findings from EPR and scavenger analyses collectively provide a detailed mechanistic understanding of LPP-driven nitrogen fixation and highlight the importance of controlling plasma parameters to achieve highly selective production of nitrogen compounds. Full article

Gliomas represent the most prevalent type of brain tumor, with their most aggressive variant, glioblastoma multiforme, associated with high mortality rates. Due to their elevated molecular heterogeneity, accurate classification of gliomas has presented significant challenges. Therefore, considerable effort has been dedicated to identifying relevant biomarkers that improve early diagnosis and unveil new areas for treatment. Advances in high-throughput sequencing technology have enabled public resources such as The Cancer Genome Atlas (TCGA) to provide large-scale data from various cancers, allowing researchers to perform more comprehensive analysis of this disease. In this study, we introduce MOHVAE-B, a comprehensive framework designed for the integration of multi-omics data and biomarker discovery using data from TCGA. MOHVAE-B employs a supervised hierarchical variational autoencoder integrated with SHAP-based interpretability to effectively integrate high-dimensional multi-omics data and extract the most influential features driving the model’s predictions. Subsequently, Bayesian Networks (BNs) are constructed to model conditional dependencies between the selected features, providing insights into their possible relations. Applied to the TCGA glioma cohorts, MOHVAE-B achieved a near-perfect AUC of $0.9993$ and successfully identified high-impact features related to glioma classification. For glioblastoma multiforme, this included six novel candidates: LINC02172, NACA2, LINC01114, HNRNPA1P48, PPIAL4G, and LINC01558. For low-grade gliomas, the model highlighted AMER2 as a promising marker. Across both cohorts, PMP2 stood out as a particularly strong candidate for a potential role in glioma pathogenesis. The constructed BNs provided an additional layer of validation, reinforcing NACA2 as a candidate of interest in glioma biology. Full article

Wear in microscale material removal is difficult to predict because material loss can proceed through several distinct pathways, including plastic deformation, adhesion, atom-by-atom attrition, tribochemical reactions, oxidation-assisted removal, and fracture. Since these mechanisms operate under different contact and environmental conditions, no single wear law is reliable across all cases. This review examines the main quantitative wear models used in microscale material removal, from classical Archard-type and Reye-type relations to atomistic Arrhenius-type descriptions and models developed for adhesive, tribochemical, oxidation-related, and fracture-dominated wear. Attention is given to the assumptions behind these models, the regimes in which they remain useful, and the conditions under which their predictions begin to fail. The discussion also considers how material properties, tool characteristics, operating conditions, and environmental factors act alone and in combination to influence wear behavior and the reliability of different models. Across the literature, a consistent conclusion is that model selection is most reliable when it is based on the active wear mechanism and the evolving contact state. On this basis, practical guidelines are outlined for different classes of contacts, and current limitations are discussed, including poor treatment of regime transitions, difficulty in parameter identification, and the gap between atomistic models and engineering use. Future progress will depend on multi-regime modeling, better treatment of coupled effects, and improved in situ characterization under realistic operating conditions. Full article

Plastic design enables efficient structural systems by exploiting controlled inelastic deformation and force redistribution. While mature in steel structures due to stable ductility and well-defined yielding, its extension to reinforced concrete (RC) remains challenging because cracking, stiffness degradation, confinement dependency, and progressive damage govern deformation capacity and collapse mechanisms. This paper presents a state-of-the-art review of optimal plastic design methodologies for RC structures by tracing the evolution from classical plasticity theory to modern damage-informed, reliability-oriented, and sustainability-driven formulations. A systematic and structured literature review of more than 90 peer-reviewed journal articles (1990–2025) was conducted using Scopus, Web of Science, and ScienceDirect. The selected studies are classified by structural system type, plastic analysis approach, constitutive modeling strategy, and strengthening technique, including CFRP and hybrid fiber systems, optimization framework, and uncertainty treatment. The review highlights how nonlinear elasto-plastic and damage–plasticity models improve the prediction of plastic hinge development, redistribution, and failure-mode transitions, and how metaheuristic optimization, topology optimization, surrogate modeling, and machine learning are increasingly used to manage discrete design variables and computational cost. Reliability-based methods (e.g., FORM/SORM and simulation) are shown to be essential for quantifying deformation-capacity uncertainty and ensuring consistent collapse-prevention performance. A comparative assessment of nine plastic design methodologies is also provided, identifying their core assumptions, limitations, and domains of applicability within a structured evaluative framework. Remaining challenges include robust deformation-capacity prediction, reproducible calibration of damage models, and integration of life-cycle sustainability criteria within reliability-constrained plastic optimization. Future research directions are proposed toward multi-objective reliability-based design, durability-informed plastic modeling, and hybrid physics-informed AI-assisted workflows. Full article

Wastewater generated during the processing of roasted coffee, including instant coffee, remains relatively unknown in the literature. However, it is characterized by a high organic load and the presence of caffeine, phenolic compounds, and melanoidins. Its properties pose significant environmental and technological challenges, limiting the effectiveness of conventional treatment methods. The research aimed to evaluate the effectiveness of an integrated, multi-stage wastewater treatment system that reflects the process of roasted coffee extraction. The developed technological sequence included biological treatment, activated carbon sorption, membrane filtration, and disinfection using ozone and UV radiation. The experiments used synthetic wastewater containing an extract of roasted coffee beans to simulate the contaminants typically found in instant coffee production and the cleaning of processing equipment. The integrated treatment system enabled the removal of total organic carbon (82.4–95.4%), ammonium nitrogen (0–77.4%), and phosphates (0–39.9%), and a reduction in turbidity of 96.3–99.8% at pH 4.02–7.25. The results confirm the system’s high efficiency and its potential for treating complex coffee wastewater, while also highlighting the need for further research into the selection of more favorable process parameters. Full article

Heavy metal pollution remains a major global environmental challenge due to persistent ecological risks and potential threats to food safety. Microbial remediation and phytoremediation represent sustainable alternatives to conventional treatments; however, their effectiveness is strongly influenced by number of factors including nutrient availability. This review critically examines how nutritional regulation governs microbial metabolism, plant physiological responses, and rhizosphere interactions to enhance heavy metal transformation and removal. Metal bioavailability depends on type, concentration, soil pH, redox potential, and microbial processes. Interventions including fertilizers, chelating agents, inoculation with arbuscular mycorrhizal fungi and plant-growth-promoting rhizobacteria enhance phytoremediation processes through regulating plant nutrient and heavy metal uptake, while selection between ammonium/nitrate changes rhizosphere pH consequently affects plant metal uptake. Similarly, nutrients, i.e., phosphate, iron, zinc and manganese competitively affect metal uptake. Organic amendments enhance phytostabilization, especially for selenium and mercury, while enhancing chromium reduction. Sulfur-reducing bacteria precipitate metals as insoluble sulfides with 90% efficiency. In addition, soil amendments including plant-growth-promoting rhizobacteria, arbuscular mycorrhizal fungi, and metal-chelating agents can be strategically used to enhance the phytoextraction from metal from contaminated soils. We suggest that the future integration of modern approaches such as multi-omics and cisgenesis supported by artificial intelligence tools can help to accurately predict the efficiency of nutrient regulation strategies and their remediation outcomes, thereby supporting evidence-based soil management. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system disease characterized by a multitude of symptoms across various organ systems. Diagnosis has relied heavily on heterogeneous clinical symptom presentation and evolving case definitions, with treatment focused on addressing presenting symptoms due to the paucity of validated biomarkers. Meanwhile, advances have been made in understanding the underlying pathophysiology through strong epidemiologic, clinical, and basic science studies. This narrative review synthesizes recent advances that are likely to drive a shift in understanding from symptom-based classification toward a molecularly defined understanding of the disease. This shift in understanding will likely provide the foundation for future research efforts focused on targeting diagnosis and treatment more effectively. Specifically, we reference the identification of rare genetic risk variants through the HEAL2 deep learning framework, the large-scale DecodeME genome-wide association study, and dynamic epigenetic markers of disease state. In addition, the findings revealed the downstream consequences of this genetic and epigenetic priming: chronic innate immune activation, CD8+ T cell exhaustion characterized by upregulation of the exhaustion-driving transcription factors Thymocyte Selection-Associated HMG Box (TOX) and Eomesodermin (EOMES), and a cellular energy crisis centered on mitochondrial dysfunction. Furthermore, results of recent studies have revealed sex-specific transcriptomic and proteomic signatures of maladaptive recovery. We also highlight the role of machine learning and artificial intelligence integrations in translating high-dimensional multi-omics data into actionable biological insights, including the identification of monocyte subsets via Positive Unlabeled Learning, circulating cell-free RNA diagnostic signatures, and integrated multi-modal disease models such as BioMapAI. The combination of these findings, which highlight multiple identifiable mechanisms of molecular activity, support the feasibility of molecular subtyping, precision diagnostics, and targeted therapeutic strategies for ME/CFS. Full article

Rising antibiotic resistance necessitates alternatives such as silver nanoparticles (AgNPs), which exhibit bactericidal activity via multi-target mechanisms (e.g., membrane disruption, ROS production). While resistance to chemically synthesized AgNPs exists, the potential for resistance to green-synthesized AgNPs, such as those from reishi mushroom, is unknown. This study compared S. aureus resistance development against both AgNP types using experimental evolution by analyzing genomic and morphological changes. Additionally, this work evaluated potential cross-resistance responses to ionic silver and investigated how adaptation to green-synthesized AgNPs affects sensitivity to chemically synthesized AgNPs (and vice versa). Rapid resistance, along with cross-resistance to silver ions, emerged in bacteria following 14 days of sublethal exposure to silver nanoparticles, regardless of whether they were chemically or biologically synthesized. While green-synthesized AgNPs demonstrated a substantial resistance to chemical variants (p < 0.05), the reverse effect was not as strong, and resistant populations showed distinct morphological adaptations. Genomic analysis highlighted convergent hard selective sweeps, identifying common mutations across both chemical and green AgNP-treated populations, with limited unique mutations found for either. These findings enhance our understanding of bacterial resistance mechanisms to nanomaterials, contributing to the development of safer, eco-friendly, and high-efficacy treatments against multidrug-resistant infection. Full article

In this paper we aim to conduct inference on the “lift” effect generated by an online advertisement display: specifically we want to analyze if the presence of the brand ad among the advertisements on the page increases the overall number of consumer clicks on that page. A distinctive feature of online advertising is that the ad displays are highly targeted—the advertising platform evaluates the (unconditional) probability of each consumer clicking on a given ad, which leads to a higher probability of displaying the ads that have a higher a priori estimated probability of click. As a result, inferring thecausal effect of the ad display on the page clicks by a given consumer from typical observational data is difficult. To address this we propose a multi-step estimator that focuses on the tails of the consumer distribution to estimate the true causal effect of an ad display. This “identification at infinity” approach alleviates the need for independent experimental randomization but results in nonstandard asymptotic theory, motivating our novel inference method. To validate our results, we use a set of large-scale randomized controlled experiments that Microsoft has run on its advertising platform. Our dataset has a large number of observations and a large number of variables and we employ LASSO to perform variable selection. Providing a basis for comparison with our estimates, we use a study conducted by Microsoft with approximately 9.3 million search sessions focusing on consumer click behavior across search result pages of a major search engine. Randomized experiments indicate that displaying a brand advertisement increases the probability of visiting the advertiser’s website by about 2.27 percentage points relative to a baseline visit rate of roughly 78 percent. Our non-experimental estimates exhibit broadly similar patterns to those obtained from randomized controlled trials, suggesting that the proposed observational estimator can recover qualitatively comparable treatment effects in large-scale advertising data. Full article

The synergistic gelation between xanthan gum (XG) and locust bean gum (LBG) is a classic phenomenon widely adopted for quality control of XG functionality; yet the regulatory roles of XG’s side chain groups—particularly glucuronic acid, whose function remains unexplored—have not been systematically elucidated. In this study, three complementary modification strategies including enzymatic hydrolysis, oxalic acid treatment, and dilute alkali treatment were for the first time combined to precisely modulate the contents of pyruvate, acetyl, and glucuronic acid in XG side chains, constructing a series of XG samples with well-defined gradient structures. Enzymatic hydrolysis and oxalic acid treatment reduced the pyruvate content from 5.80% to 1.05% and 1.42%, respectively, while dilute alkali treatment selectively decreased the acetyl content from 3.97% to 2.58%. The effects were systematically investigated through multi-scale characterization including rheology, texture analysis, scanning electron microscopy and thermogravimetric analysis, combined with correlation analysis. The results revealed that glucuronic acid, together with pyruvate, synergistically enhanced gel network stability through electrostatic interactions and hydrogen bonding. In contrast, acetyl groups acted as negative regulators via steric hindrance, inhibiting hydrogen-bond crosslinking. This study clarifies the distinct functional roles of key XG side chain groups, with the first systematic demonstration of glucuronic acid’s contribution, and provides a theoretical basis for the structure-oriented precise design of XG-based functional gels. Full article

Gram-positive infections are associated with significant morbidity and healthcare burden, often requiring prolonged intravenous therapy. Dalbavancin, a long-acting lipoglycopeptide, has emerged as a promising option beyond its approved indication for acute bacterial skin and skin structure infections (ABSSSI). A systematic review was conducted according to the PRISMA guidelines (PROSPERO: CRD420261296328). MEDLINE, Embase, CENTRAL, and Web of Science were searched from inception. Randomized controlled trials (RCTs) and observational studies evaluating dalbavancin in adult patients with Gram-positive infections were included. Outcomes of interest were clinical effectiveness, safety, and healthcare resource utilization. Risk of Bias was assessed using RoB 2 and the Newcastle–Ottawa Scale. Twenty-one studies were included. Randomized trials confirmed non-inferior efficacy of dalbavancin compared with standard therapy in ABSSSI. Observational studies demonstrated high clinical success rates across a range of infections, including osteo-articular infections, bloodstream infections, and infective endocarditis (IE), particularly in acute settings. Lower effectiveness was observed in biofilm-related infections without adequate source control. Dalbavancin was frequently used as sequential or consolidation therapy in complex patients. Its use was consistently associated with reduced length of hospital stay, facilitation of outpatient management, and potential cost savings. The safety profile was favorable, including in prolonged or multi-dose regimens. In conclusion, dalbavancin represents an effective and well-tolerated option for Gram-positive infections, with expanding evidence supporting its use in complex and off-label settings. Its pharmacokinetic profile enables simplified treatment strategies and improved healthcare resource utilization, although appropriate patient selection and source control remain essential. Full article

Background: Chemoimmunotherapy has improved survival compared with chemotherapy alone in phase III trials of advanced oesophago-gastric (OG) cancers; however, real-world UK data under National Institute for Health and Care Excellence (NICE) eligibility criteria remain limited. This study evaluated the effectiveness and safety of first-line pembrolizumab- or nivolumab-based chemoimmunotherapy in routine clinical practice. Methods: We conducted a retrospective, multi-centre cohort study of patients with unresectable or metastatic oesophageal, gastro-oesophageal junction, or gastric cancers treated with first-line chemoimmunotherapy at two UK tertiary centres between April 2021 and July 2024. Clinical, laboratory, radiological, and toxicity data were collected. Radiological outcomes were based on retrospective review of reports issued by consultant radiologists during routine clinical care. Overall survival (OS) and progression-free survival (PFS) were analysed using the Kaplan–Meier method, with exploratory analyses of prognostic factors. Results: Seventy-six patients were included (59.2% ≥ 65 years; 81.6% adenocarcinoma; 71.1% metastatic). At a median follow-up of 11 months, 46 deaths had occurred. Median OS was 16.0 months (95% CI: 11.0–20.9), and median PFS was 8.0 months (95% CI: 6.8–9.2). Disease control occurred in 80.3% of patients and was associated with improved OS compared with progressive disease (17.0 vs. 4.0 months; p < 0.001). Survival outcomes did not differ significantly by tumour site, histology, or immunotherapy agent. Immunotherapy-related adverse events occurred in 31 patients (40.8%), with grade ≥ 3 toxicities in 13.2% and two treatment-related deaths. Exploratory analyses suggested potential associations between survival and baseline lymphocyte count and neutrophil–lymphocyte ratio, although these did not reach statistical significance. Conclusions: In this real-world UK multi-centre cohort, first-line chemoimmunotherapy demonstrated survival outcomes comparable to pivotal clinical trials, with manageable toxicity. These findings support the use of chemoimmunotherapy in routine practice. Prospective collaborative studies incorporating robust biomarker evaluation are warranted to optimise patient selection and better define predictors of response and toxicity. Full article

Polysulfone (PSF) nanofiltration membranes incorporating oxidized multi-walled carbon nanotubes (o–MWCNTs) and terbium oxide (Tb₂O₃) nanoparticles were fabricated via the non-solvent-induced phase inversion technique. The effect of Tb₂O₃ loading (0, 1, 3, and 5% w/w) on membrane morphology, hydrophilicity, water permeability, dye rejection, and antibiofouling performance was systematically investigated. Membrane structure was characterized by FTIR spectroscopy, SEM, EDX, XRD, and water contact angle measurements. The results confirmed the successful incorporation of Tb₂O₃ within the membrane matrix, and morphological analysis revealed a relatively dense membrane structure without macrovoid formation. Filtration experiments conducted in a dead-end cell under pressures of 1–4 bar demonstrated a maximum water flux of 53 L m⁻² h⁻¹, with dye rejection exceeding 99.9% for both methylene blue (MB) and Congo red (CR) at 4 bar. Antibiofouling performance, evaluated by colony-forming unit analysis, revealed bacterial growth reductions of 59% against Gram-negative Escherichia coli and 89% against Gram-positive Candida albicans, attributed to the dark-active generation of reactive oxygen species by Tb₂O₃, eliminating the need for UV irradiation. These results demonstrate that the synergistic integration of o–MWCNTs and Tb₂O₃ effectively addresses the permeability-selectivity trade-off and mitigates biofouling limitations associated with pristine PSF membranes, thereby offering a promising multifunctional platform for sustainable industrial wastewater treatment. Full article