Social isolation is a significant factor contributing to mental health issues among the older adults, particularly in societies experiencing rapid population aging. Notably, digital technology offers an effective means to address this issue. However, little is known that how access to internet affects the mental health of the older adults. Based on the China Longitudinal Aging Social Survey (2018-2020) data, a national representative data, this study explored the impact of internet access on the older adults mental health by broadening their social network. The results show that internet access significantly reduces the risk of depression of the older adults. This mitigating effect is attributed to the way digital technology enhances the ties of the older adults with family and friends, which in turn helps prevent social isolation and leads to improved mental health. Furthermore, the study finds that community-based service infrastructure and family-level filial support can significantly amplify the effect of internet access on mental health. The effects are stronger for urban residents and "young-old" cohort (aged 60-80). These findings demonstrate that fostering digital inclusion among the older adults is an effective strategy for enhancing their psychological wellbeing within aging societies.

Objective: To assess the internal consistency and interpretation of estimates reported in a 30-year cohort study of alcohol consumption and cardiovascular outcomes. Design: Critical commentary with independent recalculation of odds ratios from published aggregate data. Setting: A Swedish occupational cohort study with 30 years of follow-up. Subjects: Published data for 973 male automotive workers. Main outcome measures: Myocardial infarction, stroke, all-cause mortality and survival-time estimates. Results: The published abstract attributes an odds ratio of 0.57 to myocardial infarction, whereas the reported counts yield an odds ratio of 1.41; the 0.57 estimate relates to all-cause mortality. Further concerns include survival-time terminology, numerical inconsistencies across tables and figures, competing-risk methods, multiplicity and residual confounding, including sick-quitter bias. Conclusion: The published record requires correction and reconciliation with the underlying data and statistical analyses. The findings support only exploratory associations and do not establish cardioprotection or a survival benefit from alcohol consumption.

Artificial intelligence (AI) is increasingly introduced into radiological practice to support image interpretation, workflow optimization, and diagnostic decision-making. However, successful clinical adoption depends not only on technical performance but also on radiologists' trust in these systems. This study investigates radiologists' perceptions of AI in clinical practice and examines how these perceptions relate to trust in AI-assisted diagnostic decision-making. We conducted a cross-sectional online survey among practicing radiologists in Sweden. The survey comprised eight thematic sections addressing AI knowledge and training, attitudes toward AI, responsibility attribution, information and transparency, evaluation and risk perception, professional development, and trust in AI system functionality. Survey responses were analyzed using descriptive statistics, and open-ended responses were analyzed inductively. Fifty-seven radiologists across a range of experience levels and clinical settings participated in the study. While most reported generally positive attitudes towards AI, routine clinical use and formal training were limited. Trust was primarily linked to diagnostic accuracy, empirical validation, transparency, responsibility, and continued human oversight, while concerns focused on data representativeness, system robustness in complex cases, over-reliance on AI, and insufficient transparency. Participants largely assigned responsibility for AI-assisted decisions to AI developers, radiologists, and healthcare organizations rather than to the AI systems themselves. Trust in clinical AI develops through the alignment of technological reliability, professional preparedness, and transparent governance structures. Barriers to trust emerge when education, accountability frameworks, and technological maturity do not sufficiently support clinicians' ability to critically engage with AI-assisted radiology tools in healthcare. Strengthening trust therefore requires clearer definitions of the roles, responsibilities, and competencies expected of radiologists working with AI in clinical practice.

The use of artificial intelligence (AI) in diagnosis, prognosis, triage, and clinical decision-making has intensified concern that patients may be reduced to data profiles, risk scores, and probabilistic classifications. Current ethical approaches emphasize accuracy, privacy, bias, explainability, and human oversight but do not fully address the authority granted to algorithmic representations. This conceptual article develops the principle of non-totalization through a constructive dialogue between Emmanuel Levinas and Pope Leo XIV's Magnifica Humanitas. Drawing on Levinas's accounts of totality, the face, vulnerability, responsibility, and the third, together with the encyclical's theological anthropology of dignity, communion, vulnerable embodiment, accountability, participation, and data justice, the article argues that even an accurate and explainable AI representation becomes ethically problematic when treated as a morally exhaustive account of the patient. Non-totalization therefore requires representational humility, meaningful patient voice, contestability and remedy, attributable human responsibility, and data justice. Three illustrative scenarios involving palliative care prediction, rural triage, and a mental-health chatbot show how statistically useful outputs can foreclose attention to patients' goals, circumstances, relationships, and spiritual or existential concerns. In the Philippine context, loob and kapwa serve as contextual interlocutors that illuminate relational interiority and shared personhood without being equated with Levinasian alterity or treated as universal descriptions of Filipino patients. The framework further attends to unequal access, multilingual communication, digital literacy, workforce limitations, religious plurality, and unequal control over health data. The article concludes that AI may inform clinical judgment without acquiring morally exhaustive authority over the person to whom healthcare remains answerable.

Soil heavy metal(loid) contamination threatens food security and public health, but conventional monitoring alone cannot provide the spatial coverage required for effective risk assessment and management. This PRISMA-compliant review synthesizes 245 eligible reports published between 2015 and 2025 on machine learning and remote sensing for soil contamination prediction and risk assessment. Across 536 eligible conventional R[2] estimates from 200 reports, the field-wide mean was 0.722 at the estimate level and 0.749 at the report level. Algorithm-family differences were descriptive and confounded by target element, sample size, input data, study context, validation design, and reporting quality. No report used spatial block cross-validation; 99 (40.4%) used random train-test splitting, 38 (15.5%) used random k-fold cross-validation, and 72 (29.4%) inadequately documented partition construction. Restricting analyses to documented protocols reduced the estimate-level mean R[2] to 0.707. Interpretability remained limited: 82 reports (33.5%) used no method, 46 (18.8%) provided insufficient documentation, and 19 (7.8%) used SHAP, which should be interpreted as a model attribution method rather than a causal inference method. Only 43 reports (17.6%) included health or ecological risk assessment, and 11 (4.5%) explicitly applied EPA HQ/HI frameworks. A six-item Minimum Reporting Standard is proposed to strengthen validation, transparency, interpretation, and risk integration.

The increasing use of I-124 PET for differentiated thyroid cancer (DTC) has raised concerns about radiation exposure in pediatric patients because of their radiosensitivity. This study estimated organ-absorbed doses and the lifetime attributable risk (LAR) of secondary cancer following I-124 PET using Monte Carlo simulations with pediatric voxel phantoms representing 10- and 15-year-old males and females. Organ doses were calculated for oral and intravenous administration under different thyroid uptake conditions, and LAR was estimated using the Biological Effects of Ionizing Radiation (BEIR) VII model. The thyroid absorbed dose was slightly higher after intravenous administration, whereas the total-body dose depended mainly on age. Younger patients showed nearly twice the LAR of older patients. Females had a higher solid cancer risk, whereas leukemia risk was greater in males. Thyroid uptake and administration route had minimal effect on LAR. These findings support individualized I-124 PET protocols to maximize diagnostic benefit while minimizing long-term radiation risk in pediatric DTC.