Browse Articles

Sex-specific impact of glycemic variability on long-term outcomes after acute myocardial infarction

The impact of artificial intelligence on value chain upgrading in China’s equipment manufacturing industry

Mitigating the Viscosity–Concentration Trade-Off in Imidazolium-Functionalized Viologens for Aqueous Organic Redox Flow Batteries

Leakage-free evaluation of heart disease prediction models under nested cross-validation: discrimination, calibration, and decision-curve analysis

AGTR1 rs5186 polymorphism and inflammatory biomarkers are associated with cardiovascular disease risk in an admixed Mexican population

Abstract The angiotensin II type 1 receptor ( AGTR1 ) rs5186 (A1166C) polymorphism has been associated with cardiovascular disease (CVD) risk. However, its relevance in genetically admixed populations, such as Mexicans, remains unclear. The aim of this study was to evaluate the association between the AGTR1 rs5186 polymorphism and CVD risk in a Mexican population, while integrating key inflammatory and metabolic biomarkers. A case-control study was conducted with 356 participants from Nayarit, Mexico, classified into three groups: CVD (n = 118), hypertension free of CVD (HTN, n = 119), and healthy controls (n = 119). Genotyping of rs5186 was performed via real-time PCR. Logistic regression models were adjusted for sociodemographic, clinical, and lifestyle variables, including serum amyloid A (SAA) and homocysteine levels. The A1166C genotype was independently associated with CVD risk (OR = 3.42, 95% CI: 1.16, 10.08). Elevated SAA (OR = 1.01, 95% CI: 1.01, 1.02) and homocysteine (OR = 3.20, 95% CI: 1.93, 5.33) were also significant predictors. These associations persisted after adjusting for potential confounders. This study highlights the relevance of AGTR1 rs5186 and inflammatory biomarkers in CVD susceptibility in a Mexican admixed population. Findings support the inclusion of genetic screening and inflammatory profiling in population-specific preventive strategies.

The mediating role of depression, anxiety, and sleep in the relationship between type D personality and asthma control: a cross-sectional study

Abstract Type D personality, defined by the co-occurrence of negative affectivity and social inhibition, has been associated with poorer health outcomes in chronic diseases, including asthma. However, the psychological mechanisms underlying the relationship between Type D personality and asthma control remain unclear. This cross-sectional study included 150 adult patients with asthma recruited from a tertiary outpatient clinic. Participants completed the Asthma Control Test (ACT), Type D Personality Scale (DS-14), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and Pittsburgh Sleep Quality Index (PSQI). Type D personality traits were identified in 39.3% of the sample. Compared with non–Type D individuals, patients with Type D personality had higher depressive and anxiety symptom scores and poorer sleep quality (all p  < 0.05). Mediation analyses using bootstrapping (5,000 resamples) revealed significant indirect effects of Type D personality on asthma control through depression (β = -0.088, 95% CI [-0.132, -0.047]), anxiety (β = -0.115, 95% CI [-0.158, -0.078]), and sleep quality (β = -0.057, 95% CI [-0.091, -0.029]). These indirect effects remained statistically significant after adjustment for age, sex, smoking status, and FEV1 (% predicted). These findings suggest that Type D personality is indirectly associated with poorer asthma control through increased psychological symptom burden and impaired sleep quality.

The synergistic influence of Fe3Al and graphene on the microstructure and tribological response of the composite coating deposited via laser cladding

Assessing autonomic nervous system imbalance in long COVID-19 patients through heart rate variability during tilt testing

Hybrid constitutive law with machine learning for sintering of advanced ceramics

Abstract Predictive simulation of sintering-induced distortion remains challenging for ceramic components subjected to gravity and mechanical constraint. Classical constitutive sintering laws reproduce free densification reliably but lack the flexibility required to accurately capture stress-driven deformation within finite-element (FE) frameworks when calibrated solely from dilatometer data. This study presents a hybrid machine-learning-assisted constitutive framework for modelling constrained sintering of an industrial ceramic material. Dilatometer densification data and a gravity-loaded beam-bending experiments were obtained for the same material system, enabling simultaneous evaluation of volumetric sintering kinetics and part-level deformation. Two independently calibrated parameter sets of an Olevsky-type constitutive law reproduce densification behaviour but underpredict gravity-driven curvature (A) when applied within FE simulations, highlighting an inherent trade-off between densification fitting and deformation prediction. To overcome this limitation, the analytical volumetric strain-rate term is replaced by an artificial neural network trained directly on experimental densification data, while analytical formulations for mean and deviatoric stress response are retained. This hybrid framework decouples densification kinetics from shear-dominated deformation, enabling modulation of the effective viscous stiffness governing beam bending without compromising physical interpretability or numerical robustness. The results establish a simple, computationally efficient, and physically interpretable pathway toward predictive modelling of constrained sintering, providing a scalable foundation for industrial process optimisation and future digital-twin development.

Petrophysical characterization and chemical treatment of oil reservoir as a tool for choosing the best improved oil recovery techniques

Abstract Enhanced oil recovery (EOR) methods are essential for maximizing oil extraction from mature reservoirs. Given the ongoing reliance on crude oil, it is essential to advance enhanced oil recovery techniques to boost reservoir production and extend their lifespan. Among the chemical EOR methods, chemical flooding is a well-established technique that can theoretically be utilized across various reservoir conditions. In this paper three novel bis (ethanethioyl) oxalamide derivatives synthesized via an eco-friendly green chemistry route using ethanol solvent at ambient temperature as chemical flooding agents. Their molecular efficacy was rationalized by quantum chemical (DFT) calculations and FTIR spectroscopy, which linked optimal interfacial activity to specific structural features. They were tested as an agent in reducing the interfacial tension (IFT) between the injected water and crude oil and also, in altering the wettability of reservoir rock. The results indicated the efficiency of the new compound (bis N) in reducing the IFT from 27 to 5 mN/m also altering the rock’s affinity for water than oil. Finally, this agent was used in chemical flooding experiments on real core plugs under reservoir conditions in terms of (temperature, pressure, and crude oil). From flooding experiments, these calculations indicate positive economics for enhanced oil recovery through this new compound where it can withstand severe reservoir conditions and achieve a recovery factor of 22.82%S or , 29.75%S or and 34.12%S or in the case of 1 g/l, 1.5 g/l and 2 g/l concentrations respectively, from the remaining oil.

Task-irrelevant background broadband noise reduces redundancy in auditory-motor adaptation in speech production

Latent profiles of social connectedness and associated factors in maintenance hemodialysis patients

Abstract To date, no study has characterized the heterogeneity of social connectedness among patients undergoing maintenance hemodialysis (MHD). This study used latent profile analysis (LPA) to identify distinct latent profiles of social connectedness in this population and to explore the factors associated with different profiles. This cross-sectional study enrolled 365 patients undergoing MHD at a tertiary hospital in Anhui Province, China, from September 2025 to March 2026. Data were collected using a demographic questionnaire, the Social Connectedness Scale-Revised (SCS-R), the Social Support Rating Scale (SSRS), and the 10-item Connor-Davidson Resilience Scale (CD-RISC-10). Data analysis was performed using SPSS 26.0 and Mplus 8.3. A total of 345 valid questionnaires were collected. The response rate was 94.52% (345/365). We identified three potential profile categories: low social connectedness ( n  = 110, 31.9%), moderate social connectedness ( n  = 146, 42.3%), and high social connectedness ( n  = 89, 25.8%). Multivariate logistic regression analysis identified educational level, employment status, dialysis shift, types of chronic diseases, resilience, and social support as factors significantly associated with these three profiles ( P  < 0.05). The findings indicate that social connectedness among MHD patients exhibits significant heterogeneity across subgroups and is associated with a variety of factors. Healthcare professionals should identify patients’ social connectedness profiles early and implement targeted interventions to improve their social connectedness.

Prevalence and symptoms of primary dysmenorrhea among adolescent girls in India: a school-based cross-sectional study

Abstract The objective of this study was to estimate the prevalence of primary dysmenorrhea and associated symptoms among adolescent girls. This school based cross-sectional study was conducted among 5,000 adolescent girls from 55 randomly selected schools. The data were collected by using Dysmenorrhea questionnaire and numerical pain rating scale along with demographic details. The data were analysed using Jamovi -open statistical software version 2.6.44. A bivariate logistic regression model was computed and variables whose p-value was < 0.05 in the bivariate logistic regression analysis were further included in the multiple logistic regression analysis. The prevalence of primary dysmenorrhea was found among 3441 (68.82%), 1686 (49.01%) had mild pain, 1496 (43.4%) had moderate pain, 259 (7.52%) had severe pain. Multiple logistic regression revealed, factors such as mothers with and without dysmenorrhea, having menstruation more than once in a month, menstruation once in two months, lethargy and tiredness day before menstruation, lethargy and tiredness day after, irritability on the day before menstruation, and constipation on the first day of menstruation were associated with primary dysmenorrhea. The prevalence of dysmenorrhea is high among adolescent girls, which highlights the need for early diagnosis and development and implementation of interventions to promote the school health and wellbeing.

Transparent film and electrospun PVA-HA nanofibers incorporated MSNs loaded TET/ERY for wound healing: characterization and in vitro study

DFT and docking analysis of stability, conformational dynamics, and interaction behavior in linear C6 triketo and diketo scaffolds

Abstract Linear polycarbonyl motifs constitute flexible chemical frameworks with relevance to conformational analysis, molecular stability, and interaction modeling. Here, we report a combined density functional theory (DFT) and molecular docking study of the structural stability, conformational preferences, and interaction behavior of linear C₆ triketo and diketo scaffolds. Equilibrium geometries, intramolecular hydrogen-bonding patterns, and relative Gibbs free energies were evaluated at the B3LYP/6–311 +  + G(d,p) level. Symmetric diketone isomers were identified as the most stable conformers, whereas arrangements containing adjacent carbonyl groups exhibited pronounced conformational flexibility accompanied by energetic destabilization of up to ~ 18 kcal mol⁻ 1 . Across the scaffold series, a consistent instability window of ~ 12–16 kcal mol⁻ 1 was observed, associated with characteristic geometric distortions. To qualitatively probe interaction behavior, representative conformers spanning the stability range were subjected to molecular docking. Docking simulations were carried out against three key non-small cell lung cancer (NSCLC)-related targets, epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), to evaluate the biological relevance of the studied conformers and to relate conformational stability to binding behavior in a disease-relevant context. Energetically favorable conformers displayed well-defined and reproducible interaction poses, while less stable structures showed increased orientational variability consistent with their flexible potential energy surfaces.

Development and validation of a multidimensional physical function-focused frailty at risk index for early detection of functional decline in community-dwelling older adults

Reducing write amplification of DM-SMR disks by shingle-aware persistent cache

A water and land resources multi-objective optimal allocation model based on ecological, economic and social sustainability using NSGA-III

Development and validation of a nomogram for estimating frailty probability in patients on maintenance hemodialysis

Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations

Abstract Clinical decision-making increasingly relies on deep neural networks (DNNs), yet their deployment in practice requires transparent and interpretable predictions. Explainable artificial intelligence (xAI) methods can identify input regions relevant to a model’s decision, but their clinical interpretability remains limited. In this study, we investigated whether inductive transfer learning (TL) can reinforce domain-specific feature separation in xECGArch, a two-branch convolutional neural network for atrial fibrillation (AF) detection from electrocardiograms (ECGs). Each branch was pre-trained on a task aligned with its designated feature domain, P wave detection for morphology and RR interval variability prediction for rhythm, then fine-tuned on binary AF classification using an iterative layer freezing schedule. Deep Taylor decomposition (DTD) was applied to analyze explanations across all configurations. Fine-tuning accuracy ranged from 85.70% to 95.23%, remaining comparable to the original xECGArch architecture and previous TL-based approaches. However, DTD analysis demonstrated that morphology pre-training directed relevance toward P waves, whereas rhythm pre-training concentrated explanations on R peaks, with domain specificity increasing as more layers were frozen. These findings suggest that inductive TL can encourage domain-specific feature attribution in DNNs, improving the alignment of post-hoc explanations with clinically meaningful ECG regions.