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Prostate cancer (PCa) is a highly heterogeneous malignancy with complex genetic underpinnings. This study integrates multi-omics data to prioritize candidate susceptibility genes and evaluate their functional and clinical significance in PCa pathogenesis. We integrated PCa GWAS summary statistics with GTEx v8 expression quantitative trait locus reference panels to perform cross-tissue and single-tissue transcriptome-wide association studies. Candidate signals were refined using conditional analysis, MAGMA and fastBAT gene-level tests, Summary data-based Mendelian randomization, and Bayesian colocalization. Tumor-context cis-eQTL evidence from TCGA-PRAD was incorporated to prioritize regulatory signals retained in prostate cancer tissues. Prioritized candidates were further assessed using transcriptomic datasets, Human Protein Atlas immunohistochemistry, single-cell RNA-seq analysis, histological grading, preoperative PSA, and established genomic risk signatures. Gene network and pathway enrichment analyses were performed to explore potential biological context. The integrative genetic analyses identified 23 consensus candidate genes supported by multiple association frameworks. SMR and Bayesian colocalization further narrowed the candidate list, and tumor-context cis-eQTL analysis in TCGA-PRAD retained MLPH as the final prioritized candidate. The lead variant rs7582964 was significantly associated with MLPH expression in PRAD tumor tissues. MLPH was upregulated in PCa tissues compared with normal prostate tissues in TCGA-PRAD and showed concordant expression patterns in an independent GEO cohort and Human Protein Atlas immunohistochemistry data. Single-cell transcriptomic analysis localized MLPH expression mainly to epithelial cells, with the strongest signal observed in tumor epithelial cells. Clinically, MLPH expression was associated with histological differentiation, with reduced expression in the most poorly differentiated tumors. Lower MLPH expression also correlated with higher preoperative PSA and higher Decipher-like genomic risk scores. Functional analyses linked MLPH to vesicle-mediated transport, exocytosis, and hormone-related signaling pathways. This integrative genomic analysis prioritizes MLPH as a candidate susceptibility gene for PCa and links its regulatory signal to tumor-context expression, protein-level evidence, cellular localization, and clinically relevant molecular features. These findings support a potential role for MLPH in PCa biology, particularly in relation to vesicle trafficking and tumor differentiation.

Resilience theory emphasizes that adaptive capacity must be understood in relation to adversity and the resources available for adaptation. However, public health research primarily interprets psychological resilience measures as indicators of individual adaptive capacity, with limited integration of sociological, historical, and developmental context. Such narrow interpretations of resilience may be especially consequential for young African American women, who experience disproportionately high exposure to stressors shaped by socioecological structures. This perspective proposes a socioecological framework to refine the interpretation of psychological resilience in public health research involving young African American women. Drawing on multisystem resilience theory, African-centered psychology, Indigenous health scholarship, community-engaged research, and prior empirical findings, this perspective proposes a socioecological framework that situates psychological resilience within patterned structural stress environments, culturally grounded resource systems, and life-course developmental contexts. Reframing the interpretation of resilience in public health research can strengthen methodological rigor and reduce oversimplified conclusions. Without this shift, narrow interpretations may promote detrimental narratives and lead to decontextualized interventions that are ineffective and unsustainable. Situating psychological resilience within multilevel systems of stress exposure and reciprocal resources supports culturally grounded research and informs the development of effective, multidimensional public health interventions.

Maize is one of the most important food crops in the world, and foliar diseases can lead to significant yield losses if identification is not performed on time. Experts conducting manual inspections find it less effective and more subjective. Deep learning-based approaches utilizing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been demonstrated as a viable approach to automate disease diagnosis. Thus, while CNNs fail to capture wider context due to their local feature focus and ViTs need larger datasets and tend to miss finer-grained details. To overcome these limitations, we present EDISP a hybrid framework that connects Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for local feature extraction, as well as global contextual learning. The EDISP framework brings together the strengths of CNNs and ViTs to overcome their individual weaknesses. It is trained on a dataset that includes both controlled-environment and real-field maize leaf images, which helps it handle different environmental conditions. The data undergoes thorough preprocessing, including normalization, augmentation, and stratified splitting into training, validation, and test sets to support generalization. The CNN focuses on detailed local disease features, while the ViT captures broader contextual information across the maize leaf surfaces. The proposed EDISP model significantly outperforms standalone CNN and ViT Models in multiple performance metrics, achieving an overall classification accuracy of 99.40%, precision of 99.43%, recall of 99.38%, and an F1-score of 99.40%. Experimental results demonstrate that EDISP excels in identifying maize leaf diseases, including Common Rust, Gray Leaf Spot, Northern Leaf Blight, and Healthy leaves, with minimal false positives and negatives. External validation with an independent dataset further highlights the model's robustness and ability to generalize to real-world conditions. The EDISP hybrid architecture, integrating CNNs and ViTs, provides a stronger method for accurate, automated maize leaf disease detection. Its robust performance, consistent results on controlled and field datasets shows robustness in diverse environments. However, EDISP's effectiveness may be limited by image quality, lighting, or disease types not seen in training. These results highlight the promise of hybrid deep learning in precision agriculture and offer a scalable solution for disease detection, supporting farmers without expert diagnostic resources.

To explore first-hand expert perspectives on the development and use of federated repositories for health data sharing, with a focus on their role in secondary use of health data and the European Health Data Space framework. A qualitative study was conducted using semi-structured interviews with 19 experts directly involved in developing federated health data repositories. Interview findings were thematically analysed to identify perceived benefits, limitations, and suitable use contexts for federated data-sharing models. Federated networks were perceived as effective in contexts where they can support new opportunities for research data sharing, engage new data providers, and address trust-related barriers. However, participants also identified important limitations. These included high resource demands, complex governance requirements, privacy risks, and reduced suitability for certain types of research. Federated repositories may offer valuable governance and technical solutions for health data sharing, especially where trust and provider engagement are central concerns. Their benefits are context-dependent, and they are not suitable for all research scenarios. The findings contribute to debates on data governance for secondary use and the future role of federated infrastructures under the European Health Data Space.

The sustained progression of chronic obstructive pulmonary disease (COPD) may not be independently driven by a single process such as chronic inflammation, oxidative stress, or cell death, but rather originates from a cross-amplification network among "mitochondrial dysfunction-oxidative stress-regulated cell death." In the context of mitochondrial damage, excessive generation of reactive oxygen species (ROS), damage and release of mitochondrial DNA (mtDNA), and dysregulation of mitochondrial quality control (MQC) collectively promote airway epithelial injury, sustained inflammation, alveolar destruction, and tissue remodeling. Furthermore, regulated cell death modalities such as apoptosis, necroptosis, pyroptosis, and ferroptosis are not isolated from each other but are coupled under a shared context of mitochondrial stress, exhibiting different dominant patterns across various cell types and disease stages. Adopting an integrated perspective encompassing mitochondrial dysfunction, amplified oxidative stress, and the regulated cell death (RCD) cross-network, this article synthesizes current research regarding COPD-related mechanisms, with a focus on mitochondrial damage markers, RCD activity indicators, mechanism-oriented patient stratification, and potential therapeutic strategies targeting mitochondrial homeostasis and cell death pathways. This framework facilitates the transition of COPD understanding from the traditional chronic inflammation model to a more stratified and translationally promising mitochondrial-cell death network model.

Acute caffeine ingestion is widely used to enhance exercise performance, but most evidence comes from male or mixed-sex cohorts. Whether menstrual-cycle phase or hormonal contraceptive status modifies caffeine's ergogenic effect in women remains unclear. Six databases were searched for randomized controlled trials examining acute caffeine ingestion and objective exercise performance outcomes in women. Methodological quality and risk of bias were assessed using a modified PEDro scale and RoB 2. Three-level meta-analyses accounted for dependent effect sizes within studies. The primary moderator was reproductive-hormonal status, classified as early follicular, late follicular/peri-ovulatory, luteal/mid-luteal, or hormonal contraceptive/oral contraceptive use. Exploratory analyses examined dose, exercise type, phase verification, timing, and habitual caffeine intake. Twenty studies contributed 144 primary effect sizes. Acute caffeine ingestion improved exercise performance in women overall (Hedges' g = 0.37, 95% CI 0.24 to 0.50), with moderate heterogeneity and a prediction interval crossing the null. Positive estimates were observed across all reproductive-hormonal strata, but there was no clear evidence of between-stratum moderation. The overall effect was robust to outlier exclusion and alternative correlation assumptions. Exploratory analyses suggested that exercise-task phenotype and phase-verification method may partly explain variability in observed effects, whereas dose, timing, and habitual caffeine intake did not support phase-specific prescriptions. Risk-of-bias concerns, sparse subgroup data, and low-to-very-low GRADE certainty limited individualized inference. Acute caffeine ingestion produces a small average ergogenic effect in women, but exploratory findings suggest that its expression may vary by exercise task within different reproductive-hormonal contexts. Current evidence therefore supports a context-sensitive interpretation rather than rigid menstrual-phase- or contraceptive-specific prescriptions. However, given the low-to-very-low certainty of evidence, these findings should be interpreted cautiously and considered hypothesis-generating. Inconsistent phase verification remains a key methodological limitation that may obscure biologically meaningful patterns. Future female-specific trials should combine rigorous hormonal verification, detailed contraceptive profiling, prespecified exercise phenotypes, and mechanistic assessment to refine individualized caffeine guidance.. OSF https://osf.io/5y69g/, identifier 5y69g.

Malnutrition and treatment-related catabolism undermine outcomes in oncology. This study aimed to characterize nutritional modalities, outcomes, and contextual effectiveness in cancer management. Following a predefined scope, a comprehensive search was conducted across three academic databases to identify relevant literature. Eligible study designs for inclusion were randomized controlled trials, pilot/feasibility studies, and matched cohort studies in adults with cancer that reported patient/clinical outcomes. Data were charted on population, intervention components, comparators, outcomes, feasibility, and further synthesized narratively. Fifteen studies conducted in countries such as Canada, Japan, Mexico, Spain, and India were included. The interventions were clustered as follows: (i) dietitian-led counselling with quantified energy/protein targets; (ii) oral nutrition supplements such as whey protein, ω-3/EPA-enriched formulas, and adapted foods; (iii) dietary patterns (modified Atkins); and (iv) multimodal prehabilitation including nutrition plus exercise/psychosocial, and sometimes anti-inflammatory medication. During chemotherapy, eicosapentaenoic acid (EPA)-enriched oral nutritional supplementation (ONS) improved appetite, intake, and treatment tolerability, whereas survival remained unchanged. Cachexia programs were feasible and safe, but adherence to supplements lagged behind exercise and medication. Culturally appropriate foods were also found to enhance acceptability and quality of life. Nutritional therapy is a feasible and clinically meaningful component of cancer care. Impact varied by context and fidelity; however, harmonized outcomes, adherence optimization, and cost-effectiveness data are needed to inform commissioning, scale-up, and evaluation strategies.

Immune checkpoint inhibitors have transformed cancer therapy with the programmed cell death protein 1-programmed death-ligand 1 axis, demonstrating substantial efficacy by targeting adaptive immunity across multiple malignancies. However, the limited clinical responses observed in a considerable proportion of patients highlights the need for more effective engagement of innate immune mechanisms. In this context, the cluster of differentiation 47-signal regulatory protein α (CD47-SIRPα) axis has emerged as a next-generation immune checkpoint that regulates phagocytosis. CD47 is a ubiquitously expressed transmembrane glycoprotein containing an N-terminal extracellular immunoglobulin variable-like domain and is frequently overexpressed in both solid tumors and hematological malignancies. By binding to SIRPα on macrophages, CD47 transmits a canonical "don't eat me" signal that suppresses phagocytosis and enables tumor immune evasion. Beyond this canonical role, CD47 interacts with ligands such as thrombospondin-1 and integrins to regulate tumor cell migration, metabolic adaptation, and immune balance within the tumor microenvironment. Importantly, CD47 links innate immune clearance with antigen presentation and downstream adaptive immune activation, positioning it as an actionable node for next-generation therapeutic designs. Although the first-generation CD47 blockade has revealed challenges related to hematologic toxicity, antigen sink effects, and limited monotherapy durability, these challenges have also accelerated the development of more selective, controllable, and context-responsive therapeutic strategies. In this Review, we elucidate the structural features, molecular mechanisms, pathological functions, therapeutic strategies, translational challenges, and emerging solutions of CD47, with the aim of providing a theoretical basis for overcoming current therapeutic limitations and advancing more precise cancer immunomodulatory strategies.

Radiotherapy (RT) remains a major treatment for solid tumors, but durable tumor control is frequently limited by adaptive DNA repair, altered cell-death thresholds, cancer stemness, metabolic plasticity, and immune escape. RNA modifications have recently emerged as rapid post-transcriptional regulators that enable tumor, stromal, and immune cells to remodel these programs after irradiation. This Mini Review emphasizes three major themes. First, N6-methyladenosine (m6A) is the best-characterized RNA modification in RT response, with METTL3/METTL14, FTO, ALKBH5, YTH-domain readers, and IGF2BP proteins regulating DNA repair, apoptosis, ferroptosis, stemness, metabolism, and immune checkpoints in a highly context-dependent manner. Second, non-m6A modifications, including 5-methylcytosine (m5C), N4-acetylcytidine (ac4C), 7-methylguanosine (m7G), and A-to-I RNA editing, are increasingly linked to homologous recombination, metabolic adaptation, innate immune sensing, and immune evasion, although their RT-specific evidence remains limited and should be viewed as emerging rather than established. Third, therapeutic targeting of RNA-modifying enzymes may improve radiosensitization only when guided by tumor type, cellular context, RT dose and fractionation schedule, predictive biomarkers, and normal-tissue safety. Accordingly, we organize current evidence around RNA-modification machinery, tumor-intrinsic mechanisms of radioresistance, immune microenvironment remodeling, and barriers to clinical translation. We further highlight the need to move beyond single-axis models toward dynamic, spatial, and clinically validated analyses of RNA modification networks during fractionated RT.

Emerging evidence suggests that human flourishing is not guaranteed, even in economically developed nations, with substantial disparities reported across populations and contexts. This highlights an urgent need for scalable, context-sensitive approaches that support flourishing and holistic wellbeing. Physical activity represents one such pathway, with growing evidence linking participation to psychological, social, and physical dimensions of flourishing. However, many strategies aimed at increasing physical activity overlook the complexity of behaviour change and the socio-cultural factors shaping movement experiences. As a result, physical activity is often framed as a prescriptive health behaviour rather than a meaningful, intrinsically rewarding experience. This paper presents a conceptual framework that integrates meaningful physical activity, physical literacy, and human flourishing to inform policy, practice, and research aimed at fostering lifelong engagement in movement and wellbeing. A Delphi study was conducted with an international panel of experts working across the fields of meaningful physical activity, physical literacy, and human flourishing (N = 8; 75% female). Through iterative rounds of consultation, including focus group workshops and structured evaluation processes, participants provided feedback to refine a preliminary conceptual framework and establish consensus regarding its clarity, relevance, and practical utility. The Delphi process resulted in a refined framework characterised by enhanced conceptual coherence, stronger alignment with contemporary theory and policy, and greater clarity regarding the interrelationships among meaningful physical activity, physical literacy, and human flourishing. Expert feedback contributed to a more explicit articulation of the mechanisms through which meaningful movement experiences and physical literacy development may support flourishing across the lifespan. The resulting framework provides a theoretically grounded and practice-oriented model for understanding how movement experiences may contribute to human flourishing. By emphasising the quality, meaning, and embodied nature of physical activity, the framework offers guidance for policymakers, public health practitioners, educators, and community organisations seeking to promote sustainable, lifelong engagement in movement and support holistic wellbeing.

Short-form video platforms have become a central psychological environment in university life, yet their mental health significance cannot be explained by total screen time alone. This hypothesis-and-theory article develops the Motivation-Affordance-Capacity-Outcome framework (MACO) to explain why similar short-form video duration may produce protective, neutral, problematic, or clinically meaningful outcomes among university students. MACO is revised here as a shorter, more testable, and platform-specific framework. Its distinctive contribution rests on three mechanisms: session-in-context analysis, motivational drift, and algorithmic feedback loops. The framework argues that entry motives are translated by short-form-video affordances into engagement modes; that self-regulatory capacity and baseline vulnerability shape whether use remains flexible; and that algorithmic feedback can stabilize either adaptive or maladaptive patterns over repeated sessions. The article clarifies how MACO differs from I-PACE, the active-passive model, compensatory Internet use theory, and differential susceptibility approaches by generating comparative predictions about short-form video versus long-form video, text forums, traditional television, and general social media. It also specifies falsifiable propositions, disconfirmation criteria, and operational indicators for constructs such as motivational drift, socially saturated loneliness, perceived algorithmic agency, and motivational alignment. Particular attention is given to Chinese and East Asian university contexts as theoretically important boundary conditions rather than assumed universal settings. The framework supports interventions that move beyond generic screen-time reduction toward motive-specific diagnosis, digital mindfulness, sleep-protective friction, credibility support, platform design changes, and culturally responsive mental health education.

Epigenetic modifications, including DNA methylation, have long been associated with developmental programming, as well as aging and disease states. However, our understanding of cell-specific epigenomic landscapes remains limited, especially in the context of brain aging and neurodegeneration. In cases of late-onset brain disorders, such as Alzheimer's disease, progressive supranuclear palsy, Parkinson's disease, and frontotemporal dementia, unraveling cell-specific epigenomic contributions is particularly necessary to better understand the molecular contributors to early disease states, which may help enhance diagnostic and therapeutic measures. While whole brain tissue and neuronal cell-type-specific methylomic contributions have been extensively studied, those of glia, including astrocytes, remain poorly elucidated. Given the key role of DNA methylation in guiding neurodevelopmental timing and gliogenic onset, it is likely that these modifications alter astrocyte functionality with age and disease. Here, we briefly review astrocyte development in the context of DNA methylation and highlight key instances where methylomic changes contribute to astrocyte maturation and functionality. We also point to evidence showing extensive transcriptomic and functional changes associated with aged and diseased astrocytes and explore the relevance of DNA methylation in these conditions. Ultimately, elucidating molecular drivers of disease states in astrocytes will allow for a better understanding of cell-specific contributions and pave the way for future research directed at cell-specific therapeutics.

Burnout syndrome represents a critical issue in occupational health, particularly in high-demand contexts such as mining, where physical, environmental, and psychosocial risks converge and affect workers' wellbeing and job performance. In this context, the study objective is to analyze the association between psychosocial risk factors and burnout syndrome among mining workers in Moquegua, Peru. A quantitative, analytical cross-sectional study with a non-experimental design was conducted. The study population consisted of 65 workers from a mining unit in Moquegua, Peru. Given the complete accessibility of the target population, a census approach was adopted, and all eligible workers were included in the study (N = 65). Validated instruments were used, including the SUSESO/ISTAS21 questionnaire for psychosocial risks and the Maslach Burnout Inventory. Data analysis involved descriptive and correlational statistics, multiple linear regression, and machine learning techniques. The findings revealed significant associations between psychosocial factors particularly social support, leadership, and work-family conflict (double presence) and burnout. All analyzed factors demonstrated significant associations capacity, with double presence emerging as the most influential predictor. Furthermore, machine learning analyses identified relevant burnout-related patterns within the analyzed dataset, highlighting their effectiveness in identifying burnout-related patterns. Burnout in mining is a multifactorial phenomenon influenced by organizational and psychosocial conditions. The results support the use of machine learning as an useful tool for identifying psychosocial risk patterns that may support prevention strategies, contributing to improved occupational health strategies in high-risk industrial settings.