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Film-based pedagogy, especially cinemeducation as a teaching methodology, has become more visible as an arts and humanities teaching format. However, many existing cinemeducation courses at medical schools use films that, while valuable in their time, may no longer reflect current societal or medical discourses. This study aimed to identify contemporary films that resonate with present-day issues in medicine, assess students' motivation to engage with cinemeducation, and explore interest in transdisciplinary learning. For the first time, three medical students participated in BaseCamp at the Locarno Film Festival, a young talents program that fosters transdisciplinary exchange. They screened festival films with medical themes and organised a 75-minute workshop with students from film, natural sciences, arts, and medicine. The workshop focused on how students from these fields can collaborate to create transdisciplinary learning environments. The students identified seven films with medical topics in the Locarno Film Festival program. Evaluations showed that the students were highly motivated to further engage with cinemeducation and promote transdisciplinary education. The exchange generated innovative approaches, including the use of video essays for cinemeducation and producing films together with art and film students. Medical students like to exchange ideas with other cinemeducation projects worldwide, which is supported by a mean Likert score of 1.3 (n=3). A transdisciplinary medical film festival and a cinemeducation symposium could be a first step. Film-basierte Pädagogik, insbesondere Cinemeducation als Lehrmethode, hat als Lehrformat der Arts und Humanities zunehmend an Sichtbarkeit gewonnen. Viele bestehende Cinemeducation-Kurse an medizinischen Fakultäten greifen jedoch auf Filme zurück, die zwar zu ihrer Zeit bedeutsam waren, aber heutige gesellschaftliche oder medizinische Diskurse nicht mehr angemessen widerspiegeln. Ziel dieser Studie war es, zeitgenössische Filme zu identifizieren, die mit aktuellen Fragestellungen in der Medizin resonieren, die Motivation von Studierenden zur Auseinandersetzung mit Cinemeducation zu untersuchen sowie das Interesse an transdisziplinärem Lernen zu explorieren. Erstmals nahmen drei Medizinstudierende am BaseCamp des Locarno Film Festivals teil, einem Nachwuchsprogramm, das transdisziplinären Austausch fördert. Sie sichteten Festivalfilme mit medizinischen Themen und organisierten einen 75-minütigen Workshop mit Film-, Naturwissenschafts-, Kunst- und Medizinstudierenden. Der Workshop konzentrierte sich darauf, wie Studierende dieser Disziplinen gemeinsam transdisziplinäre Lernumgebungen gestalten können. Die Studierenden identifizierten sieben Filme mit medizinischem Bezug im Programm des Locarno Film Festivals. Die Evaluation zeigte, dass die Studierenden hoch motiviert waren, sich weiter mit Cinemeducation auseinanderzusetzen und transdisziplinäre Lehre zu fördern. Der Austausch brachte innovative Ansätze hervor, darunter den Einsatz von Videoessays für Cinemeducation sowie die gemeinsame Filmproduktion mit Kunst- und Filmstudierenden. Medizinstudierende wünschen sich zudem einen Austausch mit anderen Cinemeducation-Projekten weltweit, was durch einen mittleren Likert-Wert von 1,3 (n=3) bestätigt wurde. Ein transdisziplinäres medizinisches Filmfestival sowie ein Cinemeducation-Symposium könnten hierfür einen ersten Schritt darstellen.

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Close relationships are central to well-being in later life, shaping both emotional and sexual experiences. Yet little is known about how relationship quality relates to successful sexual aging at the dyadic level, and whether these associations differ by gender. The recently developed Successful Sexual Aging (SSA) model involves acceptance of and adaptive adjustment to age-related changes and perceived opportunities for sexual expression. This study validated a 9-item measure of SSA (Successful Sexual Aging Scale; SSAS) in the dyadic context and addressed associations between relationship quality, operationalized as relationship satisfaction and emotional intimacy, and SSA in older couples. Data were collected from 355 heterosexual couples (Average age: 66 for female and 69 for male partners). Most couples (93%) were married, with an average relationship duration of 39 years. Dyadic structural equation modeling with the Actor-Partner Interdependence Model approach was used, controlling for age. Scalar dyadic measurement invariance of the SSAS was achieved, allowing for gender comparisons of latent SSA levels. Higher relationship quality was associated with greater acceptance of age-related sexual changes and better adaptive adjustment for both women and men. Furthermore, individuals whose partners reported higher relationship quality were characterized by better adaptation, indicating that SSA reflects not only personal but also interpersonal processes. No significant gender differences emerged. Our findings contribute to the understanding of positive sexual aging, confirm the utility of the new SSA measure in dyadic studies, and highlight the importance of relationship quality for the processes that underlie the concept of SSA.

The global aging population is rapidly increasing, prompting the United Nations to declare the "Decade of Healthy Aging" (2021-2030) to improve the quality of life for older adults. Health-related quality of life (HRQoL) is crucial in this context, and the International Classification of Functioning, Disability and Health (ICF) provides a standardized framework for its evaluation. This study aimed to apply a previously proposed method for converting SF-36 domain scores into ICF qualifiers in community-dwelling older adults attending primary care services in a middle-income country, describing the resulting classification of functioning domains using standardized ICF qualifiers. A cross-sectional study was conducted with older adults aged 60 and above who accessed primary healthcare services and had no cognitive impairment. Participants underwent HRQoL assessment using the SF-36, and ICF codes previously linked to SF-36 domains were classified using ICF qualifiers. A simple calculation method was developed to convert SF-36 scores into ICF qualifiers. The study included 52 participants, with a mean age of 71.6 ± 7.0 years, 92.3% of whom were women. The ICF framework qualified 27 codes from SF-36 domains. Moderate impairments were observed in "Bodily Pain," "General Health," and "Vitality" domains, while "Physical Functioning," "Social Functioning," and "Mental Health" domains showed mild impairments. No impairments were noted in the "Role Physical" and "Role Emotional" domains. The application of ICF qualifiers to SF-36 domain scores yielded standardized classifications of HRQoL domains across the ICF framework, allowing HRQoL outcomes to be described according to the severity levels defined by the ICF qualifier scale.

This study investigates the effects of gaming genres on behavioral and neural mechanisms of Chinese orthographic processing. A total of 1462 primary school students completed a gaming and reading experience questionnaire and an orthographic judgment task, with 45 participants undergoing fMRI scans during orthographic and font size judgment tasks. Participants were categorized into three groups: action video gamers, non-action video gamers, and non-video gamers. Behavioral analyses revealed that action video gamers outperformed the other groups in orthographic skills, when age, gender, and video gaming time were statistically controlled. Neural analyses focused on modular interactions among vision, attention, and working memory networks. During the orthographic judgment task, action video gamers exhibited significantly stronger vision-working memory modular interaction compared to the other groups. No significant group differences were observed in vision-attention modular interactions during the orthographic judgment task or in any modular interaction during the font size judgment task, suggesting cognition-specific and task-specific effects. These findings highlight the potential benefits of action video gaming on orthographic processing and its associated neural networks, particularly in integrating vision and working memory. SUMMARY: Action video gamers outperformed others in orthographic processing skills. Action video gaming was associated with better vision-working memory neural interactions. Modular interactions were cognition-specific and task-specific.

Benchmarking is essential for evaluating the capabilities of large language models (LLMs). However, existing multimodal benchmarks lack dedicated resources for traditional Chinese opera, a domain rich in cultural and visual complexity. To address this gap, we introduce the TCO-Dataset, a bilingual multimodal dataset designed to assess LLMs' ability to interpret and reason about Chinese opera images. The dataset contains 1,000 multiple-choice questions paired with high-resolution images across eight major opera genres. Each sample includes a carefully selected image, a corresponding question focused on cultural and visual understanding, and an annotated answer for evaluation. The dataset supports both Chinese and English, enabling cross-lingual model assessment. All items were reviewed through multiple rounds of expert validation to ensure consistency and accuracy. The TCO-Dataset supports diverse applications, including still-image-based visual-cultural reasoning, cultural heritage preservation, and domain-specific AI development. Initial evaluations show significant performance variation across models, underscoring the dataset's challenge and value for advancing multimodal understanding.

The association between the triglyceride-glucose index-relative fat mass (TyG-RFM) and prevalent depressive symptoms remains unclear. This study aimed to examine this association and to develop an interpretable machine-learning model for screening-oriented assessment of depressive symptoms in US adults. A total of 12,600 participants from the National Health and Nutrition Examination Survey 2005-2018 were included in this study. Weighted logistic regression, restricted cubic spline (RCS), and subgroup were performed to evaluate the association between TyG-RFM and depressive symptoms. In addition, nine machine-learning models were developed and internally evaluated to estimate the individualized probability of depressive symptoms. Weighted multivariable regression analysis showed that TyG-RFM was positively associated with depressive symptoms (OR = 1.97, 95% CI: 1.10-3.53), and RCS analysis showed a similar positive relationship. In the machine-learning analysis, LightGBM showed the best overall internal performance based on five-fold cross-validated out-of-fold estimates, with an AUC of 0.747 (95% CI: 0.732-0.762), the lowest Brier score (0.070), and the greatest net benefit on decision curve analysis. SHAP analysis showed that TyG-RFM was the most influential feature in the LightGBM model for estimating the probability of depressive symptoms. TyG-RFM was positively associated with depressive symptoms and was identified as the most important feature in the best-performing LightGBM model, with the accompanying online calculator providing an exploratory approach for the assessment of depressive symptoms. However, longitudinal studies and external validation are still needed before broader clinical application.

BackgroundExisting research has largely focused on the early-career challenges and career choices of young lawyers in Western countries, yet the reasons why young lawyers in China continue in the profession despite labor remuneration being insufficient to cover work-related costs remain unclear.ObjectiveThis study aims to investigate the factors that motivate young lawyers in China to remain in this profession despite labor remuneration that does not fully cover their work-related costs.MethodsThis study employed a inductive reflexive thematic analysis of interviews with 28 young and senior lawyers in China.ResultsThe findings indicate that young lawyers' career choices are influenced not only by economic considerations but also by symbolic and lifestyle-oriented motivations. A favorable family financial background reduces dependence on immediate income, while the social prestige and professional identity associated with being a lawyer fulfill important psychological and symbolic needs. At the same time, limited case sources create greater discretionary time, enabling some young lawyers to maintain a more desirable work-life balance.ConclusionsThis study addresses a gap in international research concerning the career choice behavior of young lawyers in the Chinese context and provides insights for countries facing similar situations to optimize policies and systems for young lawyers' career development.

Lung cancer burden varies across countries undergoing different stages of urbanization, but the interacting roles of behavioral and macro-environmental factors remain difficult to quantify. Using GBD 2021 and World Bank data from 187 countries between 1991 and 2021, we combined GWRF-SHAP to examine nonlinear and spatially heterogeneous interactions between urbanization, proximal behavioral factors, and distal socio-environmental contexts. Lung cancer incidence shifted from traditionally developed regions toward rapidly urbanizing areas. Urbanization-occupational exposure and urbanization-forest cover emerged as the dominant interaction pathways, ranking first in 103 and 33 countries, respectively. Air pollution tended to amplify risk in rapidly urbanizing countries, whereas forest-related interactions generally showed buffering patterns with regional exceptions. Consequently, when formulating geographically differentiated lung cancer prevention strategies, it is essential to consider the combined impact of occupational health management, pollution control, the transition to clean energy, and environmental planning.

Maternal perception of environmental conditions can direct offspring developmental trajectories, providing adaptive flexibility across taxa. In the band-legged ground cricket Dianemobius nigrofasciatus, maternal exposure to short days induces embryonic diapause at the cellular blastoderm stage in offspring. Here, we investigate molecular mechanisms underlying this transgenerational adaptation through genome assembly (1.45 Gbp) and a time-series transcriptomic analysis of diapause and non-diapause eggs from 12 to 72 hours post-oviposition. Despite morphological similarity, diapause-destined eggs show early upregulation of ATP-dependent chromatin remodeling genes at 24 hours. ATAC-seq reveals reduced chromatin accessibility at neural and cell cycle-related genes. Time-series clustering identifies precocious shifts in RNA processing machinery (peaking at 24 versus 40 hours in non-diapause eggs), followed by metabolic regulation toward amino acid catabolism and gluconeogenesis sustaining long-term survival during developmental arrest. Our findings reveal diapause as actively coordinated molecular programming involving epigenetic, transcriptional, and metabolic remodeling, providing insights into transgenerational environmental adaptation.

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Depression is a prevalent mental health disorder and a leading cause of disability worldwide, creating substantial personal and societal burdens. Digital mental health interventions have emerged as accessible and scalable solutions, with artificial intelligence (AI)-driven chatbots increasingly applied to deliver therapeutic content, monitor symptoms, and provide personalized support. However, limited evidence exists on how chatbot interaction features influence treatment adherence and clinical outcomes in depression. This systematic review aimed to evaluate the clinical effectiveness of AI-driven chatbots for depression and to examine the associations between chatbot characteristics, treatment outcomes, and user adherence. A systematic review and meta-analysis were conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, searching 6 databases (Web of Science, Scopus, PubMed, IEEE Xplore, Embase, and APA PsycINFO) for randomized controlled trials (RCTs) published before May 30, 2025. Eligible studies involved individuals with depression or depressive symptoms receiving AI-driven chatbot, conversational agent, or virtual therapist interventions, with outcomes assessed using the Patient Health Questionnaire-9 (PHQ-9). Data extraction included chatbot type, interaction features, adherence, and standardized mean differences (SMDs) for symptom change. Risk of bias was assessed using the Cochrane Risk of Bias tool version 2 (RoB 2). Random-effects meta-analyses were performed with the Hartung-Knapp-Sidik-Jonkman adjustment. This review was preregistered on the Open Science Framework. A total of 11 RCTs involving 2220 participants (1091 in the intervention and 1129 in the control groups) were included. Using a random-effects model with Hartung-Knapp-Sidik-Jonkman adjustment, AI-driven chatbots showed a small-to-moderate reduction in depressive symptoms compared with control conditions, but the effect was not statistically significant (SMD=-0.46, 95% CI -1.02 to 0.10; P=.01; 95% prediction interval -1.50 to 0.58). Subgroup analyses of adherence did not show significant differences across the reported chatbot-type subgroups. In contrast, exploratory analyses of interaction features revealed more consistent patterns for adherence. Emotional responsiveness, structured feedback strategies, and interaction frequency were associated with higher adherence in high-scoring subgroups, whereas dialogue depth, self-disclosure encouragement, and user agency level showed weaker or inconsistent associations. For clinical outcomes, associations with interaction features were less consistent and more heterogeneous. This systematic review provides an interaction-focused synthesis of AI-driven chatbot interventions for depression, examining how interaction features relate to clinical outcomes and user adherence. Although overall effects were not statistically significant, emotional responsiveness, structured feedback, and interaction frequency were consistently associated with higher adherence. Engagement and outcomes may be influenced by distinct mechanisms. Limitations include the small number of RCTs, heterogeneity, reliance on study-reported descriptions, and potential publication bias. These findings highlight the importance of interaction design in developing scalable digital mental health interventions.

Physical inactivity and sleep disturbances can affect health and quality of life in people with inflammatory bowel disease (IBD). This study aimed to describe physical activity (PA) and sleep characteristics in adults with IBD in Puerto Rico. Forty-eight participants (24 women, 24 men; mean age, 42.1 ± 14.1 years), 73% with Crohn's disease (CD) and 27% with ulcerative colitis (UC), completed anthropometric measures and wore an accelerometer on the non-dominant wrist for 7 days to assess PA and sleep characteristics. The Pittsburgh Sleep Quality Index and the Inflammatory Bowel Disease Questionnaire were also used in a subsample(n = 43). On average, patients with IBD met PA recommendations (≥150 min/week) but had short sleep duration. Patients with CD were significantly younger than patients with UC, but no differences were found in PA, body mass index (BMI), or sleep duration. Men were taller and heavier than women, but BMI, PA, and sleep characteristics were similar, except that men had more sleep interruptions. Physical activity peaked in the afternoon and evening. No significant correlations were found between PA and sleep quality. However, sleep duration was inversely correlated with BMI, and PA was positively correlated with BMI. Patients with IBD were generally overweight and physically active and had short sleep duration. Monitoring PA, sleep, and BMI may inform targeted health interventions in this population.

Engineering optimization and hyperparameter tuning in machine learning represent distinct classes of complex optimization problems: the former is characterized by numerous constraints and multimodal landscapes, while the latter demands identifying optimal configurations from a vast combinatorial space. This paper proposes a novel algorithm based on the Dung Beetle Optimizer (DBO), termed Multiple-Strategies DBO (MSDBO), which integrates three targeted improvement strategies: (1) an adaptive ball-rolling and spawning strategy that dynamically balances exploration and exploitation, (2) an optimal boundary control strategy that constrains search agents within feasible regions, and (3) a foraging enhancement strategy that strengthens local search to escape premature convergence. To comprehensively evaluate MSDBO, we benchmark it against the original DBO, two enhanced DBO variants, and eight well-established metaheuristic algorithms on the CEC2017 suite (30, 50, and 100 dimensions) and the CEC2022 suite (10 and 20 dimensions). The Friedman mean rank test validates the statistically superior performance of MSDBO, and the Wilcoxon rank-sum test confirms its significant superiority at the 0.05 significance level. Ablation studies on 23 classical benchmark functions verify that each integrated strategy contributes critically to the overall performance gain. For real-world engineering applications, MSDBO is applied to three constrained design problems-speed reducer design, pressure vessel design, and step-cone pulley design-and consistently yields lower-cost solutions than 11 competing algorithms. To further validate its efficacy in machine learning hyperparameter optimization, we develop an MSDBO-tuned Kernel Extreme Learning Machine (MSDBO-KELM) model for corporate bankruptcy prediction. On this task, MSDBO-KELM achieves a classification accuracy of 82% and a specificity of 85%, outperforming all comparison algorithms across four metrics:accuracy, Matthews correlation coefficient, sensitivity, and specificity. These results collectively demonstrate that MSDBO is an efficient, robust, and broadly applicable optimizer for both constrained engineering design and data-driven prediction tasks. The source code is publicly available at https://github.com/Dedai-Wei/MSDBO.git.

No data have been collected regarding the quality of life after live-attenuated influenza vaccine (LAIV). Our study investigates LAIV's impact on the quality of life of children. This prospective, questionnaire-based study targeted children aged 4-15 y. Guardians responded to daily surveys for 16 d, starting the day before vaccination. The questionnaire included the EuroQoL 5-dimension Youth 3-Level (EQ-5D-Y-3L) tool and the EuroQoL Visual Analogue Scale (EQ-VAS) monitoring fluctuations of health-related quality of life (HRQL) and adverse events following immunization (AEFIs). Time series analysis and area under the curve (AUC) calculation were used to investigate the scores' changes. Final analysis involved 92 patients who responded to at least half of the questionnaire's iterations, including both day 7 and 14 after vaccination. One or more AEFIs were reported for 78 children (73.91%), with generally mild symptoms. In 30 cases, a significant impact on daily activities was described, and one serious AEFI was reported consisting of severe headache and pharyngodynia with fever, leading to emergency room access. EQ-VAS score dropped on the first 2 d after vaccination and recovered thereafter, reaching (and even exceeding) the pre-vaccination value on day 10. The EQ-5D-Y-3L score also declined, however, only on day 1 and recovered faster, reaching the pre-vaccination value already on day 5, undulating around the baseline level until day 10. A net loss of 0.01 quality-adjusted life days was identified (AUC: -0.0138). Our analysis showed a positive LAIV safety profile. Data from larger populations are needed to support clinical recommendations, promoting vaccine acceptance and fighting hesitancy.

Rapid population ageing presents one of the defining global health challenges of the twenty-first century. While digital technologies are increasingly used to support older adults, their deployment often remains fragmented, inequitable, and insufficiently guided by ethical and legal frameworks. This paper proposes an integrated policy agenda for healthy longevity, based on the interdisciplinary work of the Einstein Circle Longevity - Healthy Ageing Assisted by Digital Technologies. Drawing on literature, expert deliberation, and practical exemplars, we developed a conceptual framework encompassing seven interrelated domains-medical, technical, practical, interactive, psycho-social, ethical, and legal. The framework highlights the need to reframe health and ageing policy around functional ability and healthspan, not merely disease outcomes, and to embed equity and participation throughout the design and governance of digital health systems. Digital technologies can extend preventive and personalised care, foster independence, and enhance participation across the life course. Yet their benefits remain unevenly distributed because of disparities in digital access, usability, and representation in data and design. Policies must ensure that technologies respect human rights, protect autonomy, and strengthen - rather than replace - social relationships and professional care. Realising the potential of digital health for healthy longevity requires investment in interoperable infrastructure, participatory design, and ethical and legal safeguards. Cross-sector collaboration and transparent governance are essential to ensure that technology adds not only years to life, but life to years.

Fully automated digital technologies, such as artificial intelligence (AI) and apps, provide a particularly promising way to promote and support mental health and well-being in university students due to their accessibility, scalability, and cost-effectiveness, among other factors. Nevertheless, they are currently impeded by suboptimal engagement and high dropout rates, limiting their effectiveness to promote and support mental health and well-being. This study aimed to understand university students' experiences of engaging with AI and apps to promote and support mental health and well-being. University students who did not currently experience a mental health condition were recruited to ensure a nonclinical sample. Qualitative semistructured interviews were adopted and focused on students' experiences of engagement with AI and apps for their mental health and well-being. These interviews were conducted in April and May 2023 and lasted 30 minutes, 2 seconds (SD 9 min, 49 s), on average. Interviews were transcribed verbatim and analyzed using a reflexive thematic analysis. A total of 21 interviews were conducted, and 4 main themes and 4 subthemes were constructed. The first main theme refers to the "need" to engage with AI and apps for mental health and well-being. Specifically, this theme describes how nonclinical students would primarily use these technologies as a support strategy when their mental health and well-being deteriorate, and their preexisting mental health and well-being strategies are insufficient. The second theme refers to AI and apps as both a barrier and solution to stigma; while students are less inclined to access mental health apps due to stigma, they also consider apps to be less intrusive compared with other forms of support. The third theme considers a lack of trust in AI and apps. This lack of trust primarily exists due to skepticism about the capabilities of AI and apps supporting and promoting mental health and well-being, and skepticism about their ability to safeguard mental health and well-being. The final theme describes how usage is dependent on unique AI and app characteristics. Students may engage more in AI and apps when humanity, warmth, and care are considered less crucial, and when a lack of judgment and pressure is considered imperative. Overall, nonclinical university students were more likely to engage with AI and apps when they experienced a decline in their mental health and well-being. Thus, it could be more beneficial to adopt apps as a support strategy rather than as a promotional strategy in a nonclinical sample. Furthermore, future policy and practice should implement strategies to safeguard mental health and well-being and provide open and honest communication about the capabilities of AI and apps in order to build trust and enhance engagement with digital technologies for mental health support.

This study examines the family and social consequences associated with using Artificial Intelligence (AI) to manage workplace conflicts within hybrid workplaces. Hybrid workplaces are a new workplace environment, where technology is used in emerging area of inquiry concerning the degree to which AI related stressors bleed across boundaries into family life. Research has primarily focused on either the technological, or organizational aspects of algorithmic management. As such, there exists a dearth of theoretical understanding as to how workplace systems utilizing AI drive employee psychological security and subsequently their family dynamics. Thus, our objective was to understand how transparency regarding AI, fairness in algorithms used by AI, and digital surveillance employed by employers affects hybrid employee's psychological security and ultimately their family dynamics within the UAE. We utilized an explanatory sequential design to utilize both quantitative survey data collected from 420 hybrid workers, along with qualitative thematic analysis of scenario based vignette responses. Our results indicate a stark disconnect; while participants had moderate confidence in AI systems resolving conflicts; they were extremely concerned about issues of fairness, privacy and lack of algorithmic transparency. Most importantly, 63% of respondents indicated that work-related stress experienced due to AI mediated workplace surveillance and evaluation of performance had a negative effect on communication and emotional stability with family members at home. Therefore, we have proposed the Social Algorithmic Justice Framework (SAJF) to identify the pathways from algorithmic transparency to family resiliency with the influence of social support. Ultimately, this study will provide a theoretically supported framework for integrating workplace AI with socially sustainable values so as to ensure that workplace AI integration enhances family harmony rather than diminishes it.