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The ClimaSentAR dataset for evaluating cross-domain transferability of large language models in climate sentiment analysis

Profiles of medication literacy and associated factors among young and middle-aged hypertensive patients in China: A latent profile analysis

Improving medication literacy among individuals with hypertension has emerged as a significant public health issue. However, the heterogeneity of medication literacy and its association factors among young and middle-aged patients with hypertension has not yet been fully characterized. The current study aimed to identify distinct profiles of medication literacy in these population and to examine their associated factors, with the goal of informing targeted clinical interventions. A cross-sectional study was conducted with 544 consecutively recruited these hypertensive patients from two tertiary hospitals in China between July 15 and November 15, 2024. Researchers administered five validated instruments: a Demographic and Disease-Related Characteristics Questionnaire, the Revised Chinese Medication Literacy Scale for Hypertensive Patients (C-MLSHP-R), the Perceived Social Support Scale (PSSS), the General Self-Efficacy Scale (GSES), and the Brief Illness Perception Questionnaire (BIPQ). Latent profile analysis revealed three distinct profiles of medication literacy: low (35.3%), moderate (50.2%), and high (14.5%). Multivariate logistic regression further identified education level, per capita monthly household income, social support, self-efficacy, and illness perception as factors significantly associated with medication literacy (all p <  0.05). More specifically, lower education and lower income were associated with membership in the low literacy profile, while higher social support, higher self-efficacy, and more positive illness perception characterized the high literacy group. These results suggest preliminary stratified interventions approaches, including foundational education for patients with low-literacy patients, behavioral reinforcement for those with moderate literacy, and enhanced psychosocial support for socioeconomically disadvantaged groups. However, causal interpretations are precluded by the cross-sectional design, longitudinal studies are needed to validate these findings.

Intrinsic conformational flexibility limits stable small-molecule engagement of SKP2 in breast cancer: an integrated network and molecular dynamics study

Does language prestige hypothesis hold true across typologically distant languages? A corpus-based study comparing shining-through effect in English-Chinese bidirectional translations

While the influence of language prestige on the source language shining through has been well-documented in studies involving typologically similar languages, its impact on typologically distant language pairs remains underexplored. This article examines the interplay between language prestige and typological differences in shaping the shining-through effect, extending the analysis to Chinese-English bidirectional translations. Based on a bidirectional parallel balanced corpus, the study compares the shining-through effect in both Chinese-to-English and English-to-Chinese translations through the application of principal component analysis and flexible discriminant analysis. The findings revealed that the shining-through effect was present in both translation directions but was more pronounced in English-to-Chinese translations. Additionally, the effect varied across registers, with imaginative texts showing a stronger tendency than the informative texts. These results provide further evidence supporting the hypotheses of language prestige, typological distance and risk aversion. A comparison with findings from studies involving typologically closer language pairs further suggests that typological distance may help explain variation in the influence of language prestige on source language shining through. Specifically, the prestige effect appears to be weaker when the source and target languages are typologically more distant, although this interpretation requires direct empirical testing in future research.

A novel mouse model of cholesteryl ester storage disorder with Lipa exon 8 splicing junction mutation

Abstract Lysosomal acid lipase deficiency (LAL-D, OMIM #309,900) is caused by a deficiency of enzyme activity of the LIPA gene and is characterized by an accumulation of cholesteryl esters and triglycerides in the body. LAL-D has two disease subtypes with continuum presentation. Wolman disease is a severe type of LAL-D with typical manifestations of hepatosplenomegaly, diarrhea, vomiting, and severe hepatic disorder with short life expectancy. By contrast, cholesteryl ester storage disorder (CESD) is an attenuated disease subtype of LAL-D with abnormal serum lipids and hepatic involvement. For CESD, the most frequently observed genotype involves a splicing junction mutation of exon 8 (c.894G > A; E8SJM). In this study, we have generated a novel mouse model of CESD with Lipa E8SJM found in humans. Mice homozygous for E8SJM showed visceral manifestations such as hepatosteatosis and hepatosplenomegaly with activated macrophages, as was reported in the Lipa null mouse model for Wolman disease. Interestingly, our novel mouse model of CESD with Lipa E8SJM showed almost normal growth, while that with Lipa null alleles led to severe growth retardation. An accumulation of hepatic lipids began at 2 weeks of age in both mouse models before hepatosplenomegaly became apparent at 8 weeks. These results raise the possibility that this novel mouse model could recapitulate the clinical manifestations of CESD with Lipa E8SJM in humans.

Cost-effectiveness of a mentalisation-based treatment for antisocial personality disorder in males convicted of an offence on community probation in England and Wales: Multicentre, assessor-blind randomised controlled trial

This study addresses a critical gap in the evidence base for cost-effective psychological interventions for antisocial personality disorder (ASPD) in forensic settings by presenting a full trial-based economic evaluation of mentalisation-based treatment plus probation as usual (MBT-ASPD + PAU), compared to PAU alone, for males with ASPD convicted of an offence in England and Wales. The economic evaluation adopted a societal perspective, incorporating costs to health care, the criminal justice system, and the wider societal impact of offending. Outcomes were assessed 24 months post-randomisation. The primary cost–utility analysis compared costs with quality-adjusted life years (QALYs) derived from the EQ-5D-5L, while a secondary cost-effectiveness analysis compared costs with aggression outcomes measured by the Overt Aggression Scale – Modified (OAS-M). Although missing data limited definitive conclusions regarding service use, available data suggest that participants in the PAU group spent more time in custody than those in the MBT-ASPD + PAU group. Utility scores and quality of life showed minimal variation over follow-up, with no significant differences between groups. Overall costs were lower in the MBT-ASPD + PAU group, though not statistically significant. Over the 24-month period, MBT-ASPD + PAU was associated with both reduced costs and lower aggression (as reflected in OAS-M scores), indicating that each unit reduction in aggression yielded a cost saving of approximately £93. For QALYs, MBT-ASPD + PAU appeared cost-effective compared to PAU across all values a decision-maker may be willing to pay for improvements in outcome. These findings provide preliminary evidence for the potential economic value of MBT-ASPD + PAU in a forensic ASPD population, particularly in relation to aggression reduction and the associated cost savings. Trial registration This trial is registered with ISRCTN (ISRCTN 32309003).

Assessing the accuracy of total antioxidant capacity methods using machine learning approaches

Exploring the impact path of school connectedness on student development through cultivating students’ social-emotional skills

Based on OECD’s Survey on Social and Emotional Skills (SSES) Asian data (n = 23560), this study examines the pathways through which school connectedness relates to students’ positive development using structural equation modeling. The findings reveal that school connectedness predicts student positive development through serial effect of interpersonal skills and emotion regulation skills. School connectedness is associated with social-emotional skills (student engaging with others). These two social-emotional skills both predict students’ positive development. Student-classmate relationship and perceived relationship with teachers contribute to school connectedness. Energy, stress resistance, and optimism are important social-emotional skills. The outcomes are primarily reflected in positive emotions at school and students’ school engagement. The study elucidates the internal mechanism through which the school environment predicts students’ development via a sequence of social-emotional skills, offering meaningful implications for optimizing school connectedness and cultivating student’s social-emotional skills.

Ai integrated advanced facial expression recognition using hyperscale YOLOV5 capsuled convolution neural network with swarm intelligence technique

Hierarchical Architecture of Anisotropic Alkynyl-Protected Au ₂₂ Nanoclusters Enables Accelerated Photo-/Thermo-Dual Catalysis toward Cross-Dehydrogenative Coupling

Cultural competence in dementia care: Care workers and managers perspectives on physical and social environments in Long-Term Care

This study explores how cultural identity can be preserved in dementia long-term care as resident populations become increasingly diverse. Using a deductive approach informed by the Person–Environment fit framework, the study examines care workers’ and managers’ experiences and strategies across physical and social environments. Findings show that indoor and outdoor settings, communication practices, activities, relationships, and cultural rituals all influence culturally responsive care. Competing priorities such as safety, limited resources, and varying cultural competence, shape the long-term resident’s wellbeing, environment and everyday life. The study highlights the need for interpretation services, structured cultural competence training, and sensory-based approaches to enhance residents’ well-being and inclusion.

Biogenic aluminium nanoparticles synthesized from Terminalia chebula seed extract: characterization and exploration of their antiproliferative, antimicrobial and biosensing potential

AI-based color vision screening and educational counseling for high school students: An experimental study

Color vision deficiency (CVD) can negatively affect students’ learning experiences and career choices; however, combined screening and educational counseling remain challenging in school settings, particularly in low-resource contexts. Conventional paper-based color vision tests are time-consuming, difficult to standardize, and rarely linked to individualized educational or career guidance. This study aimed to design, implement, and validate an Android-based mobile application that integrates CVD screening with artificial intelligence (AI)–assisted educational and career counseling for high school students. A cross-sectional study was conducted with 527 high school students in Danang, Viet Nam. The application incorporated a digital Ishihara-based screening module and an AI-assisted counseling component. Screening outcomes were compared with conventional Ishihara testing to evaluate agreement, while students’ perceptions of usability, usefulness, and counseling relevance were assessed using a validated questionnaire. Statistical analyses included descriptive statistics, independent-samples t-tests, reliability analysis using Cronbach’s alpha, and exploratory factor analysis. Seventeen students (3.23%) were identified as having CVD, including red-green, protan, deutan, and achromatopsia (complete CVD). The mobile application demonstrated high concordance with conventional screening results. No statistically significant differences were observed between students with and without CVD in overall system evaluation scores (p > 0.05). The questionnaire showed good internal consistency (Cronbach’s alpha = 0.857) and a clear multi-factor structure reflecting students’ acceptance of AI-assisted educational and career counseling. These findings indicate that the proposed mHealth application offers a feasible and scalable approach for school-based CVD screening combined with personalized educational and career guidance. The system may serve as a supportive decision-making tool for students and counselors, while further validation under diverse real-world conditions and across different mobile devices is recommended.

Effects of ice-pack application on hand skin temperature and subjective thermal sensation and comfort in healthy young men: a randomised, parallel-group controlled trial

Characteristics and causal paths of refined oil depot accidents in China: A statistical and network-based analysis

Refined oil depots store large quantities of flammable and explosive substances and involve frequent storage and transfer operations, making them prone to accidents with severe consequences, including casualties, and economic losses. To improve accident prevention in refined oil depots, this study first collected, verified, and cleaned accident records, resulting in a dataset of 470 refined oil depot accidents in China. Accident characteristics were analyzed in terms of accident type, unit, operating status, and causes. Finally, based on accident type characteristics, oil spill-fire and explosion accidents were selected as the focus, a directed accident cause network was constructed by analyzing the influence relationships among accident causes, and UCINET was used to analysis critical causes and paths. The results indicate that accidents occur most frequently in storage units (52.1%) and during loading and unloading operations (67.2%). Oil spill is the most common accident type (36.2%), and operational errors are a prominent cause (20.4%). Based on the propensity of oil spill accidents to trigger fire and explosion accidents, network analysis was conducted to identify critical paths and causes. The results indicate that operational errors, protection defects, and defects of equipment are critical causes, and four critical pathways were identified: inadequate training → operational errors → accidents; inadequate training → operational errors → equipment defects → accidents; inadequate procedures → operational errors → accidents; inadequate procedures → operational errors → equipment defects → accidents. These findings provide support for differentiated safety management and targeted accident prevention strategies in refined oil depots.

Associations of perceived benefits, cost burden, and sociodemographic factors with cervical cancer screening by regular physician status among Korean women

In silico identification and biophysical characterization of candidate antimicrobial peptides from the Indian marine microbiome targeting multidrug-resistant ESKAPE pathogens

The global health crisis of antimicrobial resistance necessitates the discovery of new antibacterial agents. Underexplored marine microbiomes, particularly from the biodiverse Indian coast, represent a rich potential source of antimicrobial peptides (AMPs). Targeting the urgent threat of multidrug-resistant ESKAPE pathogens, the present study aimed to computationally identify novel, membrane-active AMPs from these unique metagenomic datasets, with a focus on inhibiting Gram-negative bacteria. In this study, we computationally mined Indian marine high-resolution shotgun metagenomic datasets through quality filtering, de novo assembly, and small open reading frame prediction. An ensemble of six machine learning-based AMP prediction tools identified over 51,000 high-confidence candidate AMPs. Subsequent filtering based on physicochemical properties and AlphaFold3-predicted structures prioritized ten peptides with favourable membrane-active characteristics. Two lead candidates, c_AMP_1 and c_AMP_2, were subjected to all-atom molecular dynamics simulations within Gram-negative membrane mimetic models of Pseudomonas aeruginosa , Acinetobacter baumannii , and Klebsiella pneumoniae . Our simulations indicated distinct membrane interaction modes: c_AMP_1 adopted a stable, surface-associated α-helical orientation, while c_AMP_2 displayed a more flexible, membrane-inserting orientation in the simulations. Analysis of the MD simulations revealed distinct predicted peptide-membrane interaction profiles, characterized by specific hydrogen bonding patterns, peptide tilt angles, and membrane thinning, which collectively suggest differing biophysical interaction modes. Taken together, our work suggests the Indian marine microbiome as a promising reservoir for novel AMP candidates and suggests that an integrated computational pipeline – combining machine learning, structural biology, and biophysical simulation – may help prioritize candidate peptides for future experimental validation against critical pathogens.

Acoustic emission-based intelligent identification and dual early warning for coal fatigue failure under multistage cyclic loading

“We’ll have to see how it works”: An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research

Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other stakeholders together. By understanding AI as ‘sociotechnical’ where the social and the technical nature of the work and the models are inseparable, we explore the AI development workflow and how stakeholders navigate the challenges and tensions of sharing and generating knowledge across disciplines. We conducted an inductive thematic analysis of 13 semi-structured interviews with participants in early stages of AI-in-healthcare research consortia in the UK. Our findings identify that participants needed to adapt both the tools used for sharing and the information communicated according to their audience, particularly when working with those with a clinical or patient perspective. We identify the novelty of participating in AI research, how AI knowledge is shared, and the inclusion of clinician and patient stakeholder perspectives as key areas within collaborative AI practices in healthcare. These findings highlight that bringing AI into the mix can introduce new obstacles to interdisciplinary work.

Feasibility of a noninvasive photoplethysmography-based wearable chest patch for cardiac output assessment during exercise compared with thermodilution right heart catheterization

Abstract Hemodynamic monitoring with cardiac output (CO) assessment is usually based on invasive methods that are associated with potential complications. This study compared CO measurements during exercise obtained from a noninvasive photoplethysmography (PPG)-based chest patch monitor with those obtained from invasive thermodilution-based right heart catheterization in patients with heart failure with a preserved ejection fraction (HFpEF). CO was assessed at rest, during passive leg raises, and during 20 Watt (W) of bicycle exercise. Nine patients (67% female) with a mean age of 74.1 ± 12.1 years provided a total of 27 paired measurements. Bland–Altman analysis showed a bias of -0.72 L/min (95% CI − 1.16 to − 0.29) with 95% limits of agreement of − 2.88 to 1.43 L/min and a percentage error of 33%. The CO values obtained by thermodilution and the PPG patch were strongly correlated (r = 0.774; p  < 0.001) with a concordance of r = 0.88 ( p  = 0.002) for the rest-to-20 W change. In this single-center feasibility study, a noninvasive PPG-based chest patch was feasible during exercise right heart catheterization and tracked exercise-induced changes in cardiac output well. Wearable monitoring may offer a low-risk, complementary approach to hemodynamic assessment, but these findings require confirmation in a larger validation cohort.