Early clinical manifestations of Alzheimer's disease (AD) include an apparent decline in memory and executive function. Executive function is closely related to activities of daily living (ADL) and is important for maintaining an independent, high-quality lifestyle.

This study aimed to explore the executive control ability and neuromechanisms of AD patients through stop-signal task (SST) elicited event-related potentials (ERPs) and their relationship with ADL.

Thirty-six patients with AD and 36 sex and age matched healthy controls (HCs) were recruited. Electroencephalography (EEG) data recorded during the SST was compared between groups, and SST-related indicators were determined to assess executive control ability in AD patients. The relationship between ADL and SST-related indicators was explored. We performed Receiver Operating Characteristic (ROC) analysis on SST- and EEG-related indices.

Differences in the following indices were found between the two groups: Go accuracy (P< 0.001), Go omissions (P< 0.001), Go errors (P< 0.001), Go error reaction time (RT) (P< 0.001), failed stop RT (P = 0.021), all accuracies (P = 0.005), mean amplitude of N300 (P = 0.043), peak amplitude of N300 (P = 0.043), and peak latency of N300 (P< 0.001). And Go accuracy (r = -0.603, P = 0.005) and all accuracy (r = -0.624, P = 0.003) in the AD group were negatively partially correlated with ADL. These SST- and EEG-related indicators had an Area Under the Curve of 0.771 and 0.831, both of which could be used to jointly diagnose AD (both P< 0.001).

This study suggests that the worse the executive function of AD patients, the more serious the ADL impairment. AD patients have electrical abnormalities associated with executive control. Different SST- and EEG-related indicators can be used to diagnose AD. This provides a new avenue for further elucidation of the pathological mechanisms of AD.

Obesity stands as a formidable 21st-century public health crisis, with its capacity to aggravate depressive symptoms gaining increasing clinical attention. Traditional treatment models often treat these two conditions separately. However, recent research evidence suggests a complex network linking obesity and depressive symptoms across metabolism, behavior, and mental health, with dietary patterns proposed as a key upstream modulator of both metabolic and psychological pathways. The specific mechanisms by which diet influences obesity and depressive symptoms remain unclear. Therefore, this narrative review focuses on analyzing molecular connections between diet, obesity, and depressive symptoms, including adipose tissue inflammation, the gut-brain axis, the hypothalamus-pituitary-adrenal axis, insulin and brain-derived neurotrophic factor levels, and neuroplasticity. We discuss the possible pathways and effects of different diet therapies in regulating metabolism and simultaneously impacting mental health, including calorie restriction diet, intermittent fasting, ketogenic diet, low glycemic index diet, plant-based diet, Mediterranean diet, Dietary Approaches to Stop Hypertension, among others. This review aims to provide a scientific basis for precision nutrition and personalized, sustainable diet therapies in clinical practice, promoting awareness and improving treatment strategies for depressive symptoms in obese patients.

Mental and behavioural disorders account for a large and growing share of the global disease burden, yet evidence on hazard-specific mental health risks and adaptation remains limited. We aimed to systematically evaluate disease-specific hospitalization risks and attributable burdens following multiple climate hazards in China, and examined whether socioeconomic conditions, demographic change, and historical hazard experience modified these risks.

We conducted a retrospective observational study in 955 counties in China (2016-2023) using 392,924 daily disease-specific hospitalizations for mental and behavioural disorders from the Chinese Multiple County (CMC) Hospital Network. Storm, flood, and tropical cyclone events were linked to hospitalizations, and propensity score matching aided difference-in-differences (PSM-DID) models were used to estimate post-hazard excess relative risks across eight diagnostic categories. We then estimated hazard-attributable mental health hospitalizations and used counterfactual decomposition analysis to quantify the contribution of population aging and growth to hazard-attributable burden.

Schizophrenia and delusion, mood disorders, and stress-related disorders showed stronger associations with climate hazards. Floods were associated with broader and longer-lasting increases in mental health hospitalizations, with elevated admissions persisting for 6-8 weeks after events. Averaged across 2016-2023, storms, floods, and tropical cyclones collectively generated approximately 117,717 (93,667, 147,158) additional mental and behavioural hospitalizations per year in China, 41.05% of which occurred in children, adolescents, and older adults. Population aging and growth accounted for 1.20-5.81% of hazard-attributable hospitalizations, depending on hazard type. Socioeconomically developed counties (quartile 4) experienced 16.18-24.96% lower hazard-attributable hospitalization risks than less developed counties (quartiles 1-3). Counties with the highest historical hazard frequency (quartile 4) showed 11.30-14.29% lower impacts from storms and tropical cyclones than counties with lower historical frequency (quartiles 1-3), whereas no such adaptive advantage was observed for floods. This adaptation observation was more pronounced in socioeconomically developed settings.

Storms, floods, and tropical cyclones imposed substantial and inequitably distributed mental health burdens. Climate adaptation policies should prioritize vulnerable populations and less developed regions, leveraging both socioeconomic development and hazard preparedness to promote mental health equity and sustainability under escalating climate threats.

National Natural Science Foundation of China, National Key Research & Development Program of Ministry of Science and Technology of China, Beijing Municipal Ecology and Environment Bureau.

Mental health disorders are a major public health concern in Tanzania, where most people in need of care do not receive treatment and mental health services remain severely under-resourced. Despite a growing body of country-level research, no prior review has mapped the evidence on structural determinants of mental health conditions and policy recommendations within the WHO Comprehensive Mental Health Action Plan 2013-2030.

This scoping review aimed to identify structural and health system factors associated with mental health conditions in Tanzania and map policy recommendations reported in the included studies against the objectives of the WHO Comprehensive Mental Health Action Plan 2013-2030.

Following the Joanna Briggs Institute framework for scoping reviews, five electronic databases (Embase, MEDLINE, PsycINFO, Web of Science, and Scopus) were searched for peer-reviewed empirical studies published in English up to June 2025. Screening and full-text assessment were conducted independently by multiple reviewers using Covidence. Data were extracted using a standardized extraction form and synthesized through deductive thematic analysis organized within the domains of the WHO Comprehensive Mental Health Plan.

In this review, 74 studies met the inclusion criteria. Depression and anxiety were the most frequently studied mental health conditions. The main structural and health system factors associated with mental health conditions were poverty, food insecurity, and health-system weaknesses. Policy recommendations primarily emphasized service integration and community-level promotion, whereas governance reform, sustainable financing, and culturally adapted measurement tools were less frequently addressed.

The findings highlight persistent gaps in leadership, governance, financing, and mental health information systems. Addressing these gaps requires coordinated, system-oriented strategies aligned with all levels of the WHO framework, particularly stronger attention to governance and financing, which were less frequently addressed in the recommendations from the included studies.

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.

Menopause is a major psychoneuroendocrine transition which can impact emotional functioning and mental health. Although emotion regulation (ER) is fundamental for mental health, intrinsic neural connectivity supporting ER across the menopausal transition remains unexplored. Addressing this gap, this study provides the first examination of intrinsic effective connectivity within an ER-related network across menopausal stages. Resting-state fMRI data were acquired from 76 healthy premenopausal (n = 32), perimenopausal (n = 19), and postmenopausal (n = 25) women. Effective connectivity within a predefined ER network was examined using spectral dynamic causal modeling. Further, we assessed how intrinsic connectivity predicts self-reported ER ability within each group. While self-reported ER ability did not differ across groups, resting-state effective connectivity within the ER network varied in a stage-specific manner, with the most heterogeneous effects observed between pre- and perimenopause, suggesting a non-monotonic pattern of between-group differences. Perimenopause was characterized by distinct frontal interaction patterns, reflecting a stage-specific redistribution of network organization rather than a gradual intermediate between pre- and postmenopausal connectivity profiles. Differences regarding postmenopause were restricted to greater weighting of temporo-parietal network components. Connectivity-ER ability associations revealed stage-specific predictive profiles, with distributed fronto-temporal connectivity predicting ER ability in premenopause, frontal-restricted connectivity in perimenopause, and a single frontal connection with reversed predictive direction in postmenopause. Our findings demonstrate that comparable levels of trait-based ER ability are associated with divergent intrinsic network configurations rather than a uniform architecture. Identifying perimenopause as distinct stage of intrinsic network organization advances hormone-sensitive models of intrinsic connectivity and provides a framework for understanding how baseline network organization may adapt during psychoneuroendocrine transitions in women.

Individuals who survive acute respiratory distress syndrome (ARDS) often face prolonged cognitive impairments. Comparable consequences have been observed in chronic hypoxic conditions, like COPD and obstructive sleep apnea syndrome, which have been linked to reduced gray matter volume. We hypothesized that ARDS patients display similar cerebral findings, but those treated with extracorporeal membrane oxygenation (ECMO) show greater structural brain alterations than those treated conservatively, reflecting the severity of hypoxemia.

Eighteen ARDS survivors, seven conservatively treated (ARDS-conv), and eleven treated with ECMO (ARDS-ECMO) were studied and compared with healthy controls. Structural magnetic resonance imaging (MRI) was analyzed using voxel-based morphometry (VBM). Total intracranial volume (TIV), gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volumes were quantified and compared between groups.

ARDS patients had lower TIV than healthy controls (1516.50 mL [95% CI 1380.85-1618.72] vs. 1664.07 mL [95% CI 1558.41-1776.06], P=0.008). While intracranial volumes of GM, WM, and CSF were not different between ARDS-conv and healthy controls, CSF volume of ARDS-ECMO was lower than in healthy controls, while GM and WM were comparable. Compared to ARDS-ECMO, ARDS-conv had lower GM volumes (ARDS-conv 37.9% [95% CI 36.3-39.2] vs. ARDS-ECMO 41.4% [95% CI 39.9-43.8]; P=0.036), WM and CSF were comparable. VBM also revealed distinct clusters of reduced GM in ARDS-conv patients compared to healthy controls (TFCE, FWE-corrected P<0.05).

ARDS survivors exhibit structural brain changes suggestive of hypoxia-related injury. ECMO treatment may mitigate gray matter loss, supporting early initiation of ECMO in severe ARDS to reduce long-term neurological sequelae. Extracorporeal membrane oxygenation may prevent preterm cerebral atrophy in survivors of acute respiratory distress syndrome: a pilot trial.

Artificial intelligence (AI) demonstrates potential throughout the cancer care continuum, with evidence supporting its application in medical imaging for detection, staging, treatment planning, and prognostic evaluation. However, clinical translation is hindered by challenges, data curation and annotation, model interpretability, generalizability, and integration into workflows. To address these barriers and provide guidance, a national multidisciplinary expert panel in China developed this consensus. A modified Delphi approach was employed to achieve expert consensus, involving 81 specialists in radiology, nuclear medicine, oncology, and imaging AI from university hospitals across China. These experts completed a survey containing 30 core statements addressing AI applications in clinical cancer imaging, spanning cancer screening, diagnosis, staging, treatment planning, response assessment, prognostic prediction, data governance, and implementation. Consensus was defined as a mean score ≥ 7 on a 9-point Likert scale, with ≥ 80% of experts scoring ≥ 7. All 30 statements fulfilled these thresholds, with mean scores ranging from 8.06 to 8.58 and the proportion of experts scoring ≥ 7 ranging from 86% to 98%. This expert consensus summarizes key AI application scenarios in cancer imaging and delivers recommendations on data acquisition and annotation, model development and validation, interpretability, multicenter generalizability, privacy-preserving collaboration, clinical workflow integration, and post-deployment monitoring, while contextualizing these statements across major clinical application domains and key implementation challenges in practice. It further identifies priority research directions, including the integration of multimodal and multi-omics data, longitudinal modeling of treatment response, and prospective validation in clinical settings, to support the safe, effective implementation of AI technologies in cancer imaging. KEY POINTS: Question AI translation in oncologic imaging remains constrained by limitations in rigorous validation, actionable interpretability, standardization, governance, and workflow integration. Findings Eighty-one Chinese experts reached consensus on 30 clinically practical statements covering AI applications from early detection to deployment. Critical relevance statement Recommendations highlight expert-supervised labeling, multicenter validation, subgroup evaluation, interpretable outputs, privacy-secured collaboration, integrated workflows, and post-implementation surveillance.

Evaluate whether circulating blood biomarker profiles identify shared and disease-specific risks of non-communicable diseases (NCDs).

We considered 42,666 participants from Taizhou, China. After exclusions, discovery (n = 14,478; recruited 2011.9-2014.1) and temporal validation (n = 25,018; recruited 2018.7-2021.11) cohorts were defined. We integrated 54 blood biomarkers and 26 questionnaire/physical indicators. Predictors were selected after Cox pre-screening based on concordant inclusion across stepwise regression, regularized regression, and Boruta random forest.

The final score included 15 biomarkers plus age, smoking, hypertension, and vegetable intake. In the temporal validation cohort, high-risk individuals (25.0% of participants) accounted for 53.9% of incident major NCD cases, with a 6.29-fold (4.83-8.19) higher risk than the low-risk group. Similar gradients were observed for all-cause mortality. Compared with disease-specific scores, the combined score effectively stratified both composite and individual outcomes and revealed shared risks: 56.1% of disease-specific high-risk individuals were also high risk for other NCDs.

A score integrating blood biomarkers with epidemiological and physical measures achieved clear risk stratification in the temporal validation cohort and may support priority population identification for major NCDs.

Somalia faces a dual crisis of high chronic disease burden and extreme climate vulnerability. This study examines the predictors of health-seeking behavior (HSB) for chronic diseases among Somali households. Using data from the 2020 Somali Demographic and Health Survey (SDHS), survey-weighted probit model was applied to a sample of 69,998 individuals to analyze predictors of regular treatment-seeking behavior. Approximately 6.6% of the population reported a chronic disease, with 48.8% of those individuals seeking regular treatment. Wealth (AME = 0.094, p < 0.001) and urban residence (AME = 0.078, p < 0.001) were the strongest predictors of HSB. Secondary education increased HSB by 6.2 percentage points (p = 0.012). Conversely, climate-induced livestock loss significantly hindered HSB, reducing the probability of treatment-seeking by 3.4 percentage points (p = 0.035). Internet access was not a significant predictor. Climate shocks create an economic barrier that prevents vulnerable households from managing chronic conditions. Policy interventions should focus on integrating NCD care into climate adaptation strategies and reducing the rural-urban health gap.