Posttraumatic stress disorder (PTSD) is a critical occupational health concern among health care workers (HCWs). Quantifying global prevalence and identifying risk factors is critical for guiding intervention and policy strategies. A systematic review and meta-analysis was performed following PRISMA guidelines (PROSPERO CRD42024587810). A literature search of PubMed, Embase, PsycINFO, Web of Science, and Cochrane Library databases was performed from database inception to March 11, 2025. Observational studies reporting PTSD prevalence and odds ratios (ORs) for PTSD risk factors among HCWs were included. The primary outcome was p ooled prevalence of PTSD and ORs for risk factors among HCWs. A total of 308 studies from 60 countries were included, comprising 371,211 HCWs. The pooled PTSD prevalence was 27.2% (95% CI, 25.3%–29.2%). Higher prevalence was observed among female (31.5%), nurses (28.6%), HCWs in low- and middle-income countries (30.0%), and those in Africa (40.8%). Prevalence increased from 20.2% (95% CI, 14.4%–26.0%) before COVID-19 to 27.8% (95% CI, 25.8%–29.8%) after its onset, with meta-regression showing a significant upward trend over time (β = 9.94*10 -4 , P = 0.012). The strongest risk factors for PTSD included a history of mental disorder (OR, 2.08; 95% CI, 1.54–2.80), nursing occupation (OR, 1.60; 95% CI, 1.41–1.82), and symptomatic family or friends (OR, 1.53; 95% CI, 1.22–1.90). These findings indicate a substantial psychological burden among HCWs and identify subgroups with higher vulnerability across settings.

Speech-based depression detection has become a hot research topic. Personalized information such as personality and speaking style may cause overlap of speech features among individuals with varying depression levels, raising the risk of model misclassification. A potential strategy is to specifically account for the effect of personalized information during modeling to leverage its intrinsic depression cues. Accordingly, we proposed the Adaptive Embedding Personalized Information Model (AEPIM), which comprises three modules: the Personalized Information Extraction Module (PIEM), the Depression Information Extraction Module (DIEM), and the Self-Adaptive Fusion Module (SAFM). PIEM employs contrastive learning to extract personalized information from longitudinal data. DIEM and SAFM are then trained jointly to learn more discriminative depression representations. To validate AEPIM’s effectiveness, we constructed a longitudinal dataset containing two rounds of data, which is rare in this field. Such data are crucial for analyzing personalized information, supporting the establishment of accurate relationships between speech features and depression levels, thereby assisting in individualized depression diagnosis. Experimental results demonstrate that AEPIM outperforms existing methods, reducing RMSE and MAE by at least 13.8% and 13.0% in Round 1, and by 7.5% and 5.2% in Round 2, respectively. Out-of-domain generalization was assessed on two cross-sectional datasets, indicating its effectiveness on external data. These improvements suggest that AEPIM holds significant potential for practical applications, such as long-term monitoring of depression. The code is available at https://github.com/yuanjq2023-stack/AEPIM.

Brain science research in China is at an unprecedented moment of opportunity, but existing data silos represent a systemic obstacle that impedes research and limits both scientific innovation and clinical translation. We believe the path ahead must be towards creating a fully standardized national brain health ecosystem. The pillars of this ecosystem are the standardized acquisition of multimodal data, a governance pipeline to translate raw data into secure, shareable knowledge, and large prospective cohorts as important tools for translational research. These components can help ensure that data are no longer just accumulated, but knowledge is actually found, and can enable a strategic shift in brain disease care from reactive, treatment-focused care to proactive, prevention-focused brain health.

The scarcity of normative polysomnographic (PSG) parameters in typically developing (TD) children and adolescents, as well as those with neuropsychiatric disorders, limits sleep research and clinical management. This study aimed to characterize PSG parameters in TD participants from infancy to 18 years and compare them with those observed in pediatric neuropsychiatric disorders. Data were extracted from 135 publications in the Scopus database citing the AASM 2007 scoring manual and subsequent versions, involving 7015 TD children and 3421 neuropsychiatric participants. Among TD children, total sleep time (TST) was 459.44 min, and sleep efficiency (SE) was 87.87%; the proportions of N1, N2, and SWS were 4.94%, 46.73%, and 27.14%, respectively. With increasing age, sleep onset latency (SOL) and wake after sleep onset (WASO) decreased, while SWS and REM increased. SE and N2 were positively correlated with age. Neuropsychiatric children, particularly those with epilepsy, Down syndrome, and autism, showed significant differences in SE. These findings provide a comprehensive profile of PSG parameters in TD children, useful for the differential diagnosis and clinical management of neuropsychiatric disorders.

Background:Response inhibition is a fundamental component of executive control and a transdiagnostic mechanism underpinning numerous mental disorders. Transcranial direct current stimulation (tDCS) has been investigated as a potential neuromodulatory intervention to enhance response inhibition; however, findings remain inconsistent due to methodological heterogeneity in stimulation parameters and outcome measures. Aims:To quantitatively evaluate the effects of tDCS on response inhibition as measured by the stop-signal task (SST) and to explore potential moderators influencing tDCS efficacy. Methods:This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We systematically searched PubMed/MEDLINE, Web of Science, PsycINFO, Embase and Scopus up to 1 April 2026. Quality assessment was conducted utilising the Cochrane risk of bias 2 tool, with random-effects meta-analysis, moderator and subgroup analyses and sensitivity analysis for statistical evaluation. Results:The search yielded 2982 articles, of which 35 were considered eligible for inclusion, including 1768 participants. The risk of bias 2 assessment indicated acceptable methodological quality, with the majority of studies rated as low risk of bias or as having some concerns. The overall effect of tDCS on response inhibition was modest yet statistically significant (Hedges' g = -0.22, 95% confidence interval (CI) -0.34 to -0.11, p < 0.001). Subgroup analysis indicated a small-to-moderate effect of anodal tDCS (Hedges' g = -0.38, 95% CI -0.51 to -0.26, p < 0.001), especially when targeting the right inferior frontal gyrus, right dorsolateral prefrontal cortex or motor-related cortex; additionally, online tDCS demonstrated greater efficacy than offline tDCS. Cathodal tDCS demonstrated a small but statistically significant detrimental effect on response inhibition (Hedges' g = 0.19, 95% CI 0.03-0.35, p = 0.023). Conclusions:Our findings support the potential of anodal tDCS as a neuromodulatory approach for enhancing response inhibition. The polarity-, target- and timing-specific effects highlight key methodological considerations for optimising tDCS protocols in research on response inhibition impairments. PROSPERO Registration Number:CRD42024565038.

Background: Benzodiazepines are widely prescribed but carry risks of abuse, misuse, and dependence. Current evidence on the global epidemiology and risk factors for these behaviours is heterogeneous. We aimed to synthesise global evidence on the prevalence and risk factors for benzodiazepine abuse, misuse, and dependence. Methods: In this systematic review and meta-analysis, we searched PubMed, Embase, PsycINFO, Web of Science, and the Cochrane Library from inception to Aug 16, 2025. We included observational studies reporting prevalence of benzodiazepine abuse, misuse, or dependence and/or associated risk factors. Case reports and reviews were excluded. Two investigators independently extracted data. Study quality was assessed using standard checklists. Pooled prevalence estimates with 95% CIs were calculated using random-effects models. The study is registered with PROSPERO (CRD420251055384). Findings: Of 12,446 records identified, 19 studies involving 251,503 participants were included. In the general population, pooled prevalence was 0.6% (95% CI 0.3–0.9%) for abuse, 1.8% (1.0–2.6%) for misuse, and 0.8% (0.1–8.2%) for dependence. Prevalence was substantially higher in psychiatric populations: 6.0% (1.9–10.2%) for abuse, 37.7% (25.4–50.1%) for misuse, and 36.5% (22.4–52.7%) for dependence. Subgroup analyses revealed higher pooled prevalence of dependence in studies from low- and middle-income countries (55.6%, p<0.001) and in studies using non-DSM diagnostic criteria (42.2%, p=0.021). Key risk factors included unemployment/low income, polysubstance use, psychiatric conditions, and history of substance use disorders. Heterogeneity (I²) was substantial across analyses. Interpretation: Benzodiazepine misuse and dependence are highly prevalent, particularly among psychiatric patients. The findings underscore an urgent public health need for targeted interventions, standardised diagnostic approaches, and enhanced prescription monitoring, especially in low-resource settings.

Global homelessness is a rapidly increasing humanitarian crisis, affecting 120 million individuals worldwide. However, the prevalence of mental disorders in homeless young people globally is unknown; our study aimed to address this gap. In this systematic review and meta-analysis, we searched PubMed, Embase, Scopus and Web of Science from January 1990 to 16 October 2024 for research articles in any language that reported any mental disorders for the homeless under the mean age of 24 years by reliable diagnostic methods. Studies with a response rate <50%, a sample size <50 and those that did not report the original data were excluded. Subgroup analyses and meta-regression were conducted to examine the potential factors influencing heterogeneity of prevalence. The study was registered with PROSPERO (CRD42024570399). Of the 33,600 reports identified, 45 from nine countries (73.3% from the USA) were included in this systematic review and meta-analyses. The studies encompassed 25,320 participants: 50.5% male and 48.3% female. The random-effects meta-analysis of mental disorders indicated that substance use disorders had the highest pooled point prevalence among homeless children, adolescents and youth (29.2% (95% CI 17.7–42.3%)), followed by conduct disorder (24.4% (95% CI 8.3–45.4%)), depressive disorders (21.3% (95% CI 16.7–26.4%)), anxiety disorders (16.3% (95% CI 9.9–23.9%)), posttraumatic stress disorder (14.8% (95% CI 10.8–19.3%)), bipolar and related disorders (13.0% (95% CI 4.8–24.2%)), ADHD (12.9% (95% CI 7.5–19.5%)) and schizophrenia spectrum disorders (5.4% (95% CI 1.8–10.6%)). The lifetime prevalence ranged from 12.5% (anxiety disorders) to 71.5% (conduct disorder). Subgroup and meta-regression analyses revealed key gaps: small samples, few post 2020 studies and major underrepresentation of Asian and African populations. Individuals aged 18–24 were at higher clinical risk. Conduct, anxiety and substance use disorders were more common in males; depressive disorders were more common in females. Overall, homeless children, adolescents and youth face markedly elevated risks for mental disorders, especially substance use and conduct disorders. Urgent, integrative tertiary prevention interventions and further research are needed to support this vulnerable group. This meta-analysis by Luo et al. shows high rates of mental disorders among homeless children and youth—especially substance use and conduct disorders—revealing major global data gaps and an urgent need for targeted mental health support.

Emerging evidence links gut dysbiosis to sleep disturbances, yet a comprehensive synthesis defining specific and shared microbiota alterations across sleep disorders is lacking. In this systematic review and meta-analysis, we searched PubMed, Cochrane Library, Web of Science, Embase, and PsycINFO for articles published up to Sep 20, 2025. Data from 53 studies were included in the quantitative analysis. The alpha diversity was decreased in individuals with sleep disturbances compared to controls (Shannon index standardized mean difference [SMD] = -0.24, 95% CI: -0.34 to -0.15; Chao1 index SMD = -0.29, 95% CI: -0.40 to -0.17; observed species SMD = -0.27, 95% CI: -0.40 to -0.14). However, this finding was sensitive to publication bias: after trim-and-fill adjustment, the association with the Shannon index became non-significant. Significant beta diversity shifts were observed for rapid eye movement sleep behavior disorder (RBD) and abnormal sleep duration. While evidence for disease-specific microbial alteration was limited, a shared pattern of microbiota alteration was suggested across insomnia, obstructive sleep apnea, and RBD, characterized by reduced relative abundance of anti-inflammatory, butyrate-producing bacteria (e.g., Faecalibacterium, Lachnospira) and increased abundance of Collinsella. In summary, this meta-analysis suggests a shared gut microbiota alteration in sleep disorders, though these findings are exploratory and should be interpreted cautiously given the limited confounding control.

OBJECTIVE:Chronic insomnia disorder is common and burdensome, and current treatments remain limited. We conducted a multicenter, randomized, double-blind, placebo-controlled trial to determine whether a fecal microbiota transplantation (FMT)-based treatment protocol improves sleep outcomes in adults with chronic insomnia disorder. METHODS:Participants were randomly assigned 1:1 to receive short-course antibiotic pretreatment followed by donor microbiota capsules (n = 40) or placebo capsules without antibiotic pretreatment (n = 40). Within each group, participants were further randomized to receive synbiotic supplementation or matched placebo for prespecified exploratory subgroup analyses. The primary outcome was polysomnography-measured sleep efficiency (SE) at 1 month after treatment. Secondary outcomes included other polysomnographic parameters, patient-reported sleep outcomes, and safety; microbiota analyses were exploratory. RESULTS:Compared with placebo, the FMT-based treatment protocol improved SE (adjusted between-group difference, 13.9 percentage points; 95% CI 7.29-20.41; p = 0.003) and reduced wake after sleep onset. Synbiotic assignment did not suggest meaningful differences in SE. Insomnia Severity Index and Pittsburgh Sleep Quality Index scores showed sustained improvement from 2 to 6 months. Treatment was well tolerated, with mild, self-limited adverse events and no serious adverse events. The intervention increased microbial richness and diversity and altered community structure; responders and non-responders showed similar posttreatment β-diversity change but differed in baseline microbiota composition. CONCLUSION:In adults with chronic insomnia disorder, an FMT-based treatment protocol incorporating antibiotic pretreatment improved objective sleep continuity and sustained subjective insomnia outcomes. Baseline microbial composition may contribute to treatment heterogeneity and merits further investigation.

Accumulating evidence has demonstrated that dysfunction in the brain reward circuit plays a pivotal role in the pathogenesis of both major depressive disorder (MDD) and addictive disorders (substance use and behavioral addictions). However, it remains unclear whether the neural dysfunctions during reward processing are shared or distinct between these disorders. To address this, we employed the Seed-based d Mapping (SDM) toolbox to explore shared and distinct task-based fMRI findings in MDD and addictive disorders during the processing of reward (anticipation and outcome) and loss (outcome). The electronic databases PubMed, EMBASE, Scopus, Web of Science, and PsycINFO were searched from their inception until September 20, 2025. Studies were included if they used fMRI to compare brain responses during reward/loss tasks between patients (with MDD or addictive disorders) and healthy controls, and reported whole-brain coordinates (in Talairach or MNI space). The analysis included 76 articles, including 45 articles with 1212 addiction patients, 31 articles with 1211 MDD patients, and 2126 healthy controls aggregated from the 76 articles. Conjunction analysis revealed no significant common hypo- or hyper-activated brain regions between patients with addictive disorders and those with MDD (addiction & MDD) relative to healthy controls across all experimental conditions. Differential analysis showed addiction patients had greater left striatum activation (vs. MDD) during reward anticipation, and greater right putamen and left striatum activation (vs. MDD) during reward outcome. No significant differences were found under the loss outcome. Our findings indicate that during reward processing, the striatum is a common functionally abnormal brain region in both MDD and addictive disorders, but the activation directions of the two disorders in this region show opposite trends. The striatum can serve as a transdiagnostic common abnormal marker for MDD and addictive disorders. Our results provide potential targets for future research on neuromodulatory, pharmacological, and clinical interventions in patients with MDD alone, addictive disorders alone, and comorbid patients of the two disorders.

BACKGROUND:Non-suicidal self-injury (NSSI) persists as a major public health challenge worldwide. Identifying and strategically targeting risk factors for NSSI constitutes a practical approach to its prevention. We aim to synthesize existing knowledge concerning the range and magnitude of risk factors for NSSI among children and adolescents, and to critically assess the robustness of the available evidence. METHODS:In this umbrella review, six bibliographic databases were systematically searched for articles published from database inception to Dec 2024. For the assessment of evidence credibility, pre-specified criteria for classifying evidence were utilized, categorized as convincing ("class I"), highly suggestive ("class II"), suggestive ("class III"), weak ("class IV"), or no evidence ("class V"). The Amstar-2 framework was employed to evaluate the quality of the evidence which graded as "high," "moderate," "low," or "critically low" quality. RESULTS:The study included meta-analyses of observational studies in the past 30 years on risk factors for NSSI in children and adolescents. We identified 16 meta-analyses comprising 410 primary studies on 43 risk factors from 38 countries, involving 2,659,156 children and adolescents. Twenty-three (e.g. LGBTIQ) risk factors were categorized as individual, followed by family level (n = 8, e.g. childhood maltreatment), school/peer level (n = 8, e.g. bully victims) and multifactorial level (n = 4, e.g. no religion). Eighteen (41.86%) risk factors provided highly suggestive (Class II) evidence of association with NSSI. Suggestive evidence (class III) indicated that NSSI was associated with adverse childhood experiences (2.31, 1.77-3.01) and being left-behind children (1.37, 1.11-1.69). CONCLUSION:A multitude of risk factors spanning diverse domains were identified, highlighting the multifactorial nature of NSSI in adolescents and children. Comprehensive prevention strategies and measures should be conducted for children and adolescents to decrease the risk of NSSI and associated harms in multilevel approaches.

Purpose:Sleep encompasses multiple dimensions, each potentially involving distinct parameters that collectively capture the complex features of sleep health. However, limited studies focused on the construction of objective sleep multidimensions and its associations with cognitive impairment. Patients and Methods:The study included 2670 community-dwelling older men from the Osteoporotic Fractures in Men study. Wrist actigraphy was used to collect sleep data. Latent sleep dimensions were identified from objectively measured sleep parameters using exploratory factor analysis without prespecified structures. Longitudinal associations between sleep domains and cognitive impairment were evaluated using Cox proportional hazards models over a mean follow-up of 7.4 years. XGBoost with SHapley Additive exPlanations (SHAP) was used to rank the predictive importance of each domain. Results:Five dimensions of sleep health framework were identified: rhythmicity, quality, duration, regularity, and timing. Disrupted rhythmicity was significantly associated with an increased risk of cognitive impairment (HR = 1.21, 95% CI: 1.07-1.37, p = 0.002). Longer duration (HR = 1.13, 95% CI: 1.00-1.28, p = 0.043), poorer regularity (HR = 1.13, 95% CI: 1.00-1.27, p = 0.046) and lower quality (HR = 1.13, 95% CI: 1.01-1.27, p = 0.040) were also linked to elevated risk in fully adjusted model. No significant association was observed between timing and cognitive impairment. SHAP analysis indicated that rhythmicity ranked high in predictive importance compared with traditional risk factors and was the most influential domain among the sleep dimensions. After stratification, disrupted rhythmicity remained significantly associated with cognitive impairment in most demographic and lifestyle subgroups. Conclusion:Our integrative modelling approach provides novel insights into the complex relationships between distinct sleep domains and cognitive impairment, highlighting rhythmicity as a key factor for preserving cognitive health in aging men. These findings suggest that sleep-related interventions, especially for management of rhythmicity, may be a promising approach for the prevention of cognitive impairment.

OBJECTIVE:The objective was to evaluate the longitudinal patterns of central and general obesity, identify their genetic and behavioral risk determinants, and investigate the association of distinct obesity trajectories beyond middle age with subsequent cognitive decline and the risk of developing dementia in late life. METHODS:Using a nationally representative, longitudinal, community-based cohort, we examined trajectory patterns of obesity over a 14-year span beyond middle age employing latent mixture modeling. We then evaluated their relationship with subsequent cognitive decline through linear mixed models and with the risk of developing dementia using Cox models, adjusting for confounding variables. RESULTS:Among the 4751 eligible participants (mean age, 58.7 [SD 8.1] years; 57% female), our analysis identified five distinct BMI trajectories and four WC trajectories spanning a 14-year period. In comparison with individuals in the low-stable BMI group, characterized by a consistent and healthy body weight (range, 22.8-22.9 kg/m2), those in the high-stable group, maintaining a stable obesity status (range, 34.3-35.4 kg/m2), exhibited an elevated risk of developing dementia (odds ratio [OR], 1.43; 95% CI: 1.02 to 2.00) and experienced a more accelerated cognitive decline over 6 years (difference in 6-year decline, -0.11 SD [95% CI: -0.18 to -0.03]). Similarly, when compared with participants in the low-stable WC group, indicating a stable and healthy WC (range, 76-79 cm), those in the high-increasing WC group, showing an increasing trend (range, 115-122 cm), demonstrated an increased risk of developing dementia (OR, 1.57, 95% CI: 1.01 to 2.49) and experienced a swifter cognitive decline (OR: -0.18 [95% CI: -0.28 to -0.07]). CONCLUSIONS:General and central obesity trajectories beyond midlife with persistently high or increasing patterns were significantly associated with an increased risk of developing cognitive decline and dementia in late life. Longitudinal obesity patterns may assist in precise identification of older adults at risk of developing cognitive impairment for targeted intervention.

Background and aims:The inclusion of gaming disorder as a new diagnosis in the 11th revision of the International Statistical Classification of Diseases (ICD-11) has caused ongoing debate. This review aimed to summarise the potential neural mechanisms of gaming disorder and provide additional evidence for this debate. Methods:We conducted a comprehensive literature review of gaming disorder, focusing on studies that investigated its clinical characteristics and neurobiological mechanisms. Based on this evidence, we further discuss gaming disorder as a psychiatric disorder. Results:The present review demonstrated that the brain regions involved in gaming disorder are related to executive functioning (e.g., anterior cingulate cortex and dorsolateral prefrontal cortex), reward systems (e.g., striatum and orbitofrontal cortex), and emotional regulation (e.g., insula and amygdala). Despite the inclusion of gaming disorder in the ICD-11, the debate remains on the benefits and harms of classifying it as a mental health disorder. Opponents argue that the current manifestations that support gaming disorder as a psychiatric disorder remain inadequate, it could cause moral panic among healthy gamers, and that the label of gaming disorder is stigmatising. Discussion:Evidence suggests that gaming disorder shares similar neurobiological alterations with other types of behavioural and substance-related addictions, which further supports gaming disorder as a behavioural addiction. Ongoing debates on whether gaming disorder is a psychiatric disorder push for further exploring the nature of gaming disorder and resolving this dilemma in the field.

Methamphetamine (METH) is a widely abused stimulant that affects the central nervous system. The persistent maladaptive conditioned stimuli (CS, drug cues)-drug associative memories represent a primary factor precipitating relapse. Interfering with the reconsolidation of these memories may help disrupt and modify these maladaptive CS-drug associations, potentially reducing their influence on drug-seeking behavior. The present study explored the effect of CS-triggered memory retrieval-extinction on METH craving, potentially offering a new treatment strategy for addiction. This was a single-center, randomized, controlled trial involving individuals with METH use disorder (MUD). Participants completed one of three interventions on consecutive days (days 2 and 3): CS-triggered memory retrieval followed by extinction after a 10-min interval, CS-triggered memory retrieval followed by extinction after a 6-h interval, or extinction without prior retrieval. Self-report cue-induced craving for METH, salivary cortisol and sympathetic responses were measured at baseline (day 1), post intervention (day 4) and two follow-up timepoints (days 34 and 184), with cue-induced craving and salivary cortisol as primary outcomes. Ninety-eight MUD individuals (mean age 28.15 ± 6.31) were analyzed. After two-day’s interventions, cue-induced METH craving (time × cue interaction: F(1,94) = 60.02, p < 0.001) reduced in all groups. Results from follow-up data indicated, when the extinction was performed 10 min, but not 6 h after memory retrieval or no retrieval, the intervention decreased experimental cue-induced METH craving (intervention × time × cue: F(2,94) = 14.32, p < 0.001; intervention: F(2,94) = 24.28, p < 0.001) and saliva cortisol increases (F(2,90) = 9.51, p < 0.001), with effects lasting up to 6-month follow-up. The results revealed a substantial reduction in cue-elicited craving and saliva cortisol in the retrieval-10 min-extinction group over the 6-month follow-up. These findings provide compelling evidence that a brief reconsolidation-based intervention can effectively diminish METH-related craving and cortisol levels, underscoring its potential as a supportive measure in METH treatment. Salivary cortisol is a readily accessible and sensitive biomarker for evaluating intervention effects.

Aging and age-related diseases share convergent pathways at the proteome level. Here, using plasma proteomics and machine learning, we developed organismal and ten organ-specific aging clocks in the UK Biobank (n = 43,616) and validated their high accuracy in cohorts from China (n = 3,977) and the USA (n = 800; cross-cohort r = 0.98 and 0.93). Accelerated organ aging predicted disease onset, progression and mortality beyond clinical and genetic risk factors, with brain aging being most strongly linked to mortality. Organ aging reflected both genetic and environmental determinants: brain aging was associated with lifestyle, the GABBR1 and ECM1 genes, and brain structure. Distinct organ-specific pathogenic pathways were identified, with the brain and artery clocks linking synaptic loss, vascular dysfunction and glial activation to cognitive decline and dementia. The brain aging clock further stratified Alzheimer's disease risk across APOE haplotypes, and a super-youthful brain appears to confer resilience to APOE4. Together, proteomic organ aging clocks provide a biologically interpretable framework for tracking aging and disease risk across diverse populations.

This review and meta-analysis aimed to evaluate esketamine in managing major depressive disorder (MDD) / treatment resistant depression (TRD), preventing postpartum depression (PPD) and postoperative depression, including comparisons within and between administrations. Five databases (Pubmed, MEDLINE, Embase, Web of science and the Cochrane Library) were searched up to July 24, 2025. Randomized clinical trials investigating esketamine for depression, compared with control group were included. Efficacy outcomes included depression scales, remission rate, response rate and depression incidence rates. Safety outcomes included adverse events reported in more than two comparisons. Standardized mean differences (SMDs) and Relative risks (RRs) with their corresponding 95% confidential intervals (CIs) were estimated using fixed- or random-effects models. Administration regimes were ranked using surface under the cumulative ranking. Of 12,285 studies identified, 67 trials with 11,553 participants were included: 19 trials on MDD/TRD, 18 studies on PPD and 30 studies on postoperative depression. The overall pooled SMDs, compared with comparator, showed effective in treating and preventing depression (MDD -0.36, 95%CI -0.49, -0.24; PPD for postpartum 6 week -0.40, 95%CI -0.78, -0.02, and postoperative depression for postoperative 3 month -0.82, 95%CI -1.46, -0.18). Intravenous administration yields the greatest effect in treating MDD/TRD and preventing PPD, no matter of the measured outcome (scales scores, response rate of MDD/TRD or incidence rate of PPD or postoperative depression). Esketamine was associated with higher incidences of dizziness in both therapeutic and preventive effects. These findings highlight both short- and long-term efficacy of eskatemine in treating MDD/TRD, and preventing PPD and postoperative depression, with efficacy differing across administration routes.

Brain diseases present considerable challenges to global public health, characterized by rising prevalence, intricate pathophysiology, and substantial disease burden. Enhancing our comprehension of their fundamental neurobiological mechanisms is essential for formulating more efficient diagnostic and treatment approaches. In recent years, neuroimaging technologies have progressed rapidly, providing non-invasive and multimodal methods to examine the structural, functional, and metabolic changes in the human brain. These technologies have significantly enhanced research into the processes of brain diseases, fostering insights across spatial and temporal dimensions. This review aims to provide a comprehensive synthesis of recent advancements in neuroimaging research related to brain disorders. Firstly, it presents a concise summary of the principles, applications, and distinguishing features of the primary neuroimaging technologies, including structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance spectroscopy (MRS), magnetoencephalography (MEG), and functional near-infrared spectroscopy (fNIRS). Attention is directed towards their distinct contributions in delineating disease-relevant features such as gray matter atrophy, white matter integrity, neurotransmitter systems, functional connectivity, and cortical dynamics. We then highlight mechanistic findings across a wide range of diseases. In neurological disorders such as Alzheimer's disease, Parkinson's disease, and epilepsy, neuroimaging has revealed early alterations in brain networks and metabolic function. In psychiatric conditions, neuroimaging studies have revealed distinct abnormalities in mental disorders, including depression, bipolar disorder, schizophrenia, autism spectrum disorder, attention deficit hyperactivity disorder, anxiety disorders, and substance use disorders. Recent implementations of Mendelian randomization and alternative causal inference models have enhanced the understanding of imaging-genetic correlations, offering new insights into illness causation. Furthermore, the clinical translation of neuroimaging is rapidly progressing. Imaging biomarkers are utilized to enhance early screening, diagnostic classification, outcome prediction, and to inform personalized treatment strategies. Innovative methods including connectivity-informed transcranial magnetic stimulation, pharmacotherapy prediction using pre-treatment fMRI, and real-time closed-loop neuromodulation illustrate the tangible effects of advancements in imaging technology. Innovative technologies such as brain-computer interfaces and artificial intelligence-driven clinical decision support systems are transforming precision psychiatry, especially in instances resistant to therapy. In spite of these advancements, challenges still exist. Data heterogeneity, small sample sizes, insufficient integration of imaging modalities, and the underrepresentation of non-Western populations impede repeatability and generalizability. This review underscores the pressing necessity for standardized, large-scale neuroimaging databases and multi-omics integration platforms, particularly concerning the Chinese population. Future directions include cross-modal data fusion, explainable artificial intelligence, structure-function coupling analysis, and gene-environment interaction models. In summary, this review offers a timely and thorough overview of contemporary neuroimaging methodologies in brain and mental health research. It emphasizes methodological advancements, molecular understanding, and translational developments, providing direction for future initiatives to integrate neuroimaging with clinical practice in the context of precision neuroscience.

White matter tracts (WMTs), which mediate information transmission in the brain, are closely associated with the pathogenesis of psychiatric disorders, yet the causality of their associations remain unclear. Thus, we employed two-sample bidirectional Mendelian randomization to explore the causality between WMTs and 10 psychiatric disorders. We found that one standard deviation changes of WMTs metrics modified risks for 8 psychiatric disorders by 2·2% to 71·4%. For example, increased fornix/stria terminalis radial diffusivities elevated PTSD risk by 8.3%, while heightened mode anisotropy reduced Tourette syndrome risk by 71.4%. Reversely, alcohol use disorder increased the risk of WMTs abnormalities. Our study provides novel insights into the potential causality between WMTs and psychiatric disorders, indicating that alterations of WMTs may serve as biomarkers for psychiatric disorders.

Epidemiological and clinical evidence suggests that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) reinfection is a complication in a proportion of patients reporting ongoing health issues. However, most studies in the field of SARS-CoV-2 reinfection have focused only on self-reported symptoms and lacked long-term objective measurements. This study aimed to estimate the pattern of chronic symptoms of Omicron reinfection in patients with original SARS-CoV-2 infection by comprehensively assessments 3 years after recovery. This community-based observational study was conducted in Wuhan, China, between January and April in 2023. All participants were recruited from community and invited to participate the interview and examination in a hospital. The subjective multi-system symptoms were self-reported. The objective radiological features and laboratory data were assessed by measuring blood inflammation and performing chest computed tomography (CT) and pulse oxygen saturation. Among 1438 individuals who participated in the study, 144 were infected with the original variant only in 2020, 980 were Omicron-infected in 2023, 215 were reinfected both in 2020 and 2023, and 99 were never infected. Compared with the non-infection group, the reinfection (odds ratio (OR), 5.15 [95