Background: Adolescent obesity and depressive symptoms have increased concurrently, often presenting as co-occurrence. However, evidence on the timing of physical activity (e.g., weekday vs. weekend) and adherence to planetary health diets remains limited. This study examined these associations among adolescents in Zhejiang Province from 2022 to 2024. Methods: A total of 261,495 adolescents aged 11-18 years were included. Physical activity (PA) and dietary behaviors were assessed through the China Common Disease and Risk Factor Surveillance among Students questionnaire (reliability: Cronbach's α = 0.84, validity: RMSEA = 0.07). The plant-based Planetary Health Diet Index (PHDI-green) adherence was defined as consuming at least one daily serving of both vegetables and fruits. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression (CES-D) scale, and co-occurrence was defined as the coexistence of obesity and depressive symptoms. Temporal trends were tested using χ2 tests. Sex-stratified logistic regression, restricted cubic spline analyses, and population attributable fraction (PAF) analyses were applied. Results: From 2022 to 2024, obesity (p for trend = 0.013) and depressive symptoms (p for trend = 0.003) increased significantly, while co-occurrence remained stable (p for trend = 0.058). Boys had higher obesity and co-occurrence, whereas girls showed higher depressive symptoms (all p < 0.001). Higher weekly PA, greater weekend PA and PHDI-green adherence were associated with reduced odds of obesity in both sexes (all p < 0.001). Weekend PA showed stronger associations with depressive symptoms among girls, while PHDI-green showed stronger inverse associations in boys (p for sex difference < 0.001). PAF analyses suggested that low weekend PA accounted for substantial proportions of cases (girls: obesity 10.17%, depressive symptoms 31.30%, co-occurrence 35.64%). Joint adherence to adequate PA and PHDI-green conferred the lowest odds of co-occurrence (boys: OR = 0.40, 95% CI: 0.34-0.46; girls: OR = 0.33, 95% CI: 0.26-0.43). Conclusions: Adherence to the Planetary Health Diet may be particularly relevant for boys, whereas PA-especially weekend PA-may be more strongly associated with health outcomes among girls. These findings suggest the importance of sex-specific and time-targeted behavioral strategies for obesity, depressive symptoms, and their co-occurrence in adolescents.

BACKGROUND:Extreme heat is an intensifying environmental stressor, with pregnant women identified as a highly vulnerable population. Although prior researches have examined heat exposure and preterm birth (PTB), inconsistencies in heat definitions and limited evaluation of effect modifiers hinder clear interpretation. OBJECTIVE:To systematically evaluate the association between extreme heat exposure (including temperature percentiles and heatwaves) and PTB risk. METHODS:We systematically searched five major databases (through June 2025) for studies on extreme heat ( ≥ 90th percentile) or heatwaves and PTB. Risk of bias was assessed using the WHO tool for epidemiologic studies. Random-effects meta-analysis estimated pooled relative risks (RRs), and subgroup analyses were conducted to evaluate potential modifiers. Certainty of evidence was evaluated using the WHO-adapted framework. (PROSPERO: CRD420251066785). RESULTS:In total, 49 studies from 13 countries were included. Both short-term exposure ( ≤ 4 weeks before delivery; RR = 1.07, 95% CI: 1.05-1.09) and long-term exposure ( > 4 weeks before delivery; RR = 1.27, 95% CI: 1.18-1.36) significantly increased PTB risk. For short-term exposure, the 90th percentile threshold yielded the largest effect (RR = 1.20, 95% CI: 1.12-1.29; p < 0.001). Long-term associations were stronger in studies spanning multiple climate zones (RR = 1.42; 95%CI: 1.24-1.62; p = 0.024). Key modifiers included exposure window, geographic region, study design, and country income level. SIGNIFICANCE:Extreme heat exposure is significantly elevated PTB risk, particularly during late pregnancy and through cumulative long-term exposure. These findings emphasize the urgent need for targeted public health interventions and climate adaptation strategies to protect maternal and neonatal health in heat-vulnerable and low-resource regions. IMPACT:This study provides the first systematic review and meta-analysis that specifically applies standardized percentile-based definitions of extreme heat to examine its association with preterm birth (PTB). Crucially, it quantifies risk for both short-term and long-term exposure, finding that long-term exposure throughout pregnancy poses a significantly higher risk. By identifying specific vulnerability windows and the heightened risk in multi-climate zones, these results offer a framework for clinicians and policymakers to implement timely heat-health alerts and protective infrastructure for pregnant populations.

Introduction:The study aimed to evaluate whether the association of sedentary behavior on suicide risk differs by sex and the modifying roles of social support and universal health coverage (UHC) index.Methods:We analyzed data from the Global School-based Student Health Survey across 53 countries (2013-2022). We examined the associations between sedentary time and suicide behaviors by binary logistic regression models and explored the modifying roles of social support by restricted cubic spline analysis. Stratified analyses were conducted based on overall social support and UHC groups.Results:A total of 190,329 adolescents (53.6% of girls) aged 12-17 years were included. The prevalence of suicidal ideation, plans, and attempts increased with daily sedentary time, with a more pronounced increase observed among girls. In boys, sedentary time of >8 h was associated with a 44% increase in higher risk suicide behavior (odds ratio [OR]: 1.44, 95% confidence interval [CI]: 1.36-1.53), while in girls, the increase was 88% (OR: 1.88, 95% CI: 1.79-1.97). In addition, compared to those with high support, both boys and girls with low social support showed a more pronounced increase in the risk of suicide behaviors as sedentary time increased. However, stratified analysis by UHC showed no significant differences between sedentary time and suicidal behavior among different social support groups.Conclusions:This study underscores the significant association between sedentary time and suicide behaviors among adolescents, particularly in girls. Furthermore, bolstering social support systems emerges as a promising approach to alleviate the negative associations of sedentary behavior.

Despite continuous viral evolution, it remains unclear whether SARS-CoV-2 has transitioned to transmission dynamics resembling those of other endemic respiratory pathogens in the post-pandemic era. During 2023–2025, we compared post-pandemic SARS-CoV-2 transmissibility with influenza virus (IFV) and common human coronaviruses (HCoV) transmissibility and SARS-CoV-2 transmissibility during and after the pandemic. Among 7,399 participants in 1,764 households, there were 704 acute-respiratory-infection index cases and 2,684 household contacts. Healthcare-seeking was higher for SARS-CoV-2 (17.6%) and IFV (28.1%) than HCoVs (9.8%). Post-pandemic SARS-CoV-2 household secondary attack risk (HSAR) (7.9%) was comparable to IFV (7.0%) and higher than HCoV (4.4%;P<0.05). Older adults had highest SARS-CoV-2 and HCoV transmissibility; children had highest IFV transmissibility, correlated with peak viral loads. Post-pandemic SARS-CoV-2 HSAR was lower than pandemic-period Omicron HSAR (adjusted odds ratio:3.30;95%CI:1.82–5.98) and Delta-period HSAR (aOR:2.46;95%CI:1.27–4.79). Pre-symptomatic transmission was observed for all three pathogens, highest for SARS-CoV-2 (35.7%). Our findings indicate that SARS-CoV-2 maintains high household transmissibility and requires substantial medical resources in the post-pandemic era, and non-pharmaceutical interventions and vaccination strategies that account for age-specific transmissibility and susceptibility merit study and consideration.

Sustained participation in physical activity is essential for addressing the global burden of obesity and related health conditions, yet many individuals struggle to maintain regular exercise. Exercise enjoyment is a key psychological factor associated with continued participation, underscoring the importance of identifying exercise modalities that maximize enjoyment. High-intensity interval training (HIIT), a time-efficient alternative to moderate-intensity continuous training (MICT), has been proposed as a strategy to enhance exercise enjoyment. However, evidence comparing their effects on enjoyment is inconsistent, and the role of specific HIIT prescription parameters is unclear. This systematic review and meta-analysis synthesized evidence from 18 randomized controlled trials (N = 964) to examine differences in Physical Activity Enjoyment Scale (PACES) scores between HIIT and MICT and to identify HIIT prescription characteristics associated with enjoyment. Across studies, HIIT elicited greater enjoyment than MICT (Hedges' g = 0.44, 95% CI 0.09-0.79; I2 = 78.9%). Meta-regression showed that exercise duration (β = 0.715, P = 0.015) and weekly exercise volume (β = 0.001, P = 0.032) positively predicted enjoyment. Subgroup analyses identified statistically significant between-group differences in exercise duration and intervention duration (P < 0.001; P = 0.020). Dose-response analyses further indicated clinically meaningful effects (MCID = 6.63) when HIIT was performed approximately 4 times/week, weekly exercise duration exceeded 81 min, intervention duration ranged from 4 to 11 weeks, and weekly volume surpassed 688 MET·min/week. These findings suggest that HIIT is associated with greater exercise enjoyment than MICT, and that specific prescription parameters play an important role in shaping enjoyment responses. This evidence may inform the design of exercise interventions aimed at optimizing enjoyment.

Artificial intelligence (AI) has advanced rapidly across diagnostic, prognostic, and clinical decision-support applications, yet the pathway from laboratory performance to demonstrable clinical benefit remains fragmented and inconsistently defined. Existing evaluations rely heavily on retrospective testing and algorithm-centric metrics, while current guidelines emphasize reporting standards rather than specifying validation across stages of model maturity. This study proposes a five-phase evaluation framework for medical AI, supported by a dynamic evaluation architecture reflecting the nonlinear, iterative nature of AI systems. The framework integrates technical validation, operational robustness validation, controlled interaction validation, clinical evidence validation, and real-world integration validation, while incorporating phase-gating criteria and local and systemic fall-back triggers. These mechanisms enable re-entry into earlier phases based on drift, version updates, or safety signals, and accommodate parallel activities such as implementation research informing clinical trials. By systematically mapping multicenter external validation, shadow-mode testing, human-AI comparison and cooperation studies, randomized controlled trials, real-world evaluations, and adaptive designs into a coherent lifecycle pathway, the framework addresses persistent gaps between laboratory performance and clinical benefit. It provides researchers, clinical institutions, and regulators with an operational, scalable approach aligned with evolving regulatory expectations, supporting trustworthy, ethically aligned, and lifecycle-based evidence generation for medical AI systems.

Background:The utility of aging metrics that incorporate cognitive and physical function is not fully understood. Objective:We aim to compare the predictive capacities of 3 distinct aging metrics-motoric cognitive risk syndrome (MCR), physio-cognitive decline syndrome (PCDS), and cognitive frailty (CF)-for incident dementia and all-cause mortality among community-dwelling older adults. Methods:We used longitudinal data from waves 10-15 of the Health and Retirement Study. Cox proportional hazards regression analysis was employed to evaluate the effects of MCR, PCDS, and CF on incident all-cause dementia and mortality, controlling for socioeconomic and lifestyle factors, as well as medical comorbidities. Discrimination analysis was conducted to assess and compare the predictive accuracy of the 3 aging metrics. Results:A total of 2367 older individuals aged 65 years and older, with no baseline prevalence of dementia or disability, were ultimately included. The prevalence rates of MCR, PCDS, and CF were 5.4%, 6.3%, and 1.3%, respectively. Over a decade-long follow-up period, 341 cases of dementia and 573 deaths were recorded. All 3 metrics were predictive of incident all-cause dementia and mortality when adjusting for multiple confounders, with variations in the strength of their associations (incident dementia: MCR odds ratio [OR] 1.90, 95% CI 1.30-2.78; CF 5.06, 95% CI 2.87-8.92; PCDS 3.35, 95% CI 2.44-4.58; mortality: MCR 1.60, 95% CI 1.17-2.19; CF 3.26, 95% CI 1.99-5.33; and PCDS 1.58, 95% CI 1.17-2.13). The C-index indicated that PCDS and MCR had the highest discriminatory accuracy for all-cause dementia and mortality, respectively. Conclusions:Despite the inherent differences among the aging metrics that integrate cognitive and physical functions, they consistently identified risks of dementia and mortality. This underscores the importance of implementing targeted preventive strategies and intervention programs based on these metrics to enhance the overall quality of life and reduce premature deaths in aging populations.

Epithelial ovarian cancer (EOC) is the deadliest gynecologic cancer in women. Long noncoding RNAs (lncRNAs) are critically involved in malignant progression by modulating proliferation, apoptosis, invasion, metastasis, and chemotherapy resistance. The long noncoding RNA small nucleolar RNA host gene 29 (SNHG29) is involved in multiple malignancies, although its role in EOC has not been elucidated. In our study, SNHG29 expression was significantly downregulated in EOC tissues and was negatively related to lymphatic invasion in EOC patients according to data from the TCGA database. Kaplan-Meier survival analysis revealed that among patients with early stage EOC, compared with patients with low SNHG29 expression levels, patients with high expression levels had markedly longer progression-free survival (PFS) and overall survival (OS) times. Further studies suggested that SNHG29 knockdown enhanced the invasive and migrative potential of EOC cells, whereas SNHG29 overexpression attenuated the invasive and migrative potential capacity. In animal experiments, SNHG29 knockdown significantly promoted lung metastasis in tail vein injection models. Mechanistically, the downregulation of SNHG29 expression inhibited its competitive endogenous RNA activity, resulting in increased miR-20b-3p availability and subsequent degradation of the downstream target gene GNAI3, as determined by RNA immunoprecipitation (RIP) and luciferase reporter gene assays. miR-20b-3p inhibition significantly reduces the invasive and metastatic capabilities of EOC cells resulting from a reduction in SNHG29 expression. Our study revealed that SNHG29 may be a promising prognostic factor and therapeutic target for EOC.

Background: Depression and anxiety are prevalent mental health disorders among children and adolescents, with diet quality emerging as a modifiable risk factor. However, evidence regarding the association between comprehensive diet quality and mental health in school-aged children remains limited. Methods: This cross-sectional study included 400 Chinese children aged 8-12 years. Diet quality was assessed using the low-burden Diet Quality Questionnaire (DQQ), from which three Global Diet Recommendations (GDRs) scores were derived: GDR-Healthy, GDR-Limit, and total GDR. Depression and anxiety symptoms were evaluated using the Children's Depression Inventory (CDI) and the Social Anxiety Scale for Children (SASC), respectively. Log-binomial regression models were used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) for the associations between GDR scores and mental health symptoms (depression, anxiety, comorbidity). Subgroup analyses stratified by age and sex were conducted to explore heterogeneity. Results: Higher total GDR scores were associated with lower risks of depressive symptoms (RR = 0.90, 95% CI: 0.84-0.96), anxiety symptoms (RR = 0.93, 95% CI: 0.88-0.99), and their comorbidity (RR = 0.88, 95% CI: 0.79-0.97) after adjustment for age, sex, zBMI, physical activity, region of residence, only-child status and parental education. The GDR-Healthy score was independently associated with lower risks of depression symptoms (RR = 0.89, 95% CI: 0.83-0.96) and comorbidity (RR = 0.87, 95% CI: 0.79-0.95), while no significant associations between GDR-Limit score and mental health disorders were observed. Subgroup analyses indicated that the association was consistent across sex and age subgroups. Conclusions: Better diet quality and particularly higher intake of health-protective foods is associated with lower risks of depression, anxiety, and their comorbidity symptoms in Chinese school-aged children in this cross-sectional study. These findings support the integration of diet quality monitoring and nutritional interventions into public health strategies to promote mental health in children.

Perinatal depression is common worldwide, which can cause many adverse effects on the physical and mental health of the mother and baby, as well as the whole family. The Edinburgh Postnatal Depression Scale (EPDS) is an efficient and effective instrument for perinatal depression. However, few studies have examined its longitudinal measurement invariance (LMI) during the whole perinatal period, which is particularly important in longitudinal studies, such as exploring developmental trajectories of perinatal depression and evaluating the effects of certain interventions. 4139 pregnant women from 24 hospitals in 15 provinces of China were measured using EPDS in the first, second, third trimesters and 6 weeks postpartum. Exploratory factor analysis and confirmatory factor analysis were used to explore the factor structure of EPDS at each time point. Multi-group analyses were performed to examine LMI of EPDS. A three-factor model was optimal at all time points, showing the clearest factor structure and best model fit: Anhedonia (Items 1–2), Anxiety (Items 3–6), Depression (Items 7–10). Internal reliability of EPDS was good at all time points (e.g., Cronbach’s α > 0.80). A series of multi-group analyses further indicated that the EPDS held strict LMI (configural, metric, scalar and strict invariance) during the perinatal period. The findings further confirmed three-factor structure and good reliability of the EPDS when used in Chinese pregnant and postpartum women. The LMI justified comparisons of EPDS scores among different measurement time points.

Outdoor artificial light at night (ALAN) is a widespread environmental pollution associated with the urbanization worldwide. Whether its trajectories as well as temporal patterns increase the risk of small for gestational age (SGA) remains underexplored. Based on a national population-based retrospective cohort study including 572,989 women of reproductive age (21-49 years) across 220 counties in China and satellite ALAN data, this study aimed to explore the association between the trajectories of ALAN and SGA and the effect modification by socio-economic status (SES). Group-based trajectory models according to the lowest Bayesian information criterion were used to characterize the trajectories of ALAN. Logistic regression models were used to estimate the risk ratios (RRs) of SGA associated with different trajectories of outdoor ALAN, as well as the effect modification by age, education, occupation, and region. We identified three latent classes, including low- [n = 304,577, 53.2%], moderate- [208,290, 36.4%], and high- [60,122, 10.5%] curve groups. Compared to the low-curve group, the high-curve group exhibited the highest risk of SGA (RR = 3.16, 95% CI: 2.76-3.63), followed by the moderate-curve group (RR = 1.25, 95% CI: 1.12-1.40) (P for difference <0.001). Similar associations were observed across all SES subgroups. These findings suggest that higher trajectories of outdoor ALAN exposure are associated with increased risks of SGA across populations with different SES levels.

BACKGROUND:Whether pre-pregnancy stress could modify the effects of outdoor artificial light at night (ALAN) on preterm birth (PB) has been unknown. This study aimed to explore the association between outdoor ALAN exposure and the risk of PB, as well as the effect modification by pre-pregnancy stress. METHODS:This national population-based retrospective cohort study included women of reproductive age (21-49 years) from the National Free Preconception Health Examination Project (NPHCP) across 220 counties in China, from 2010 to 2012. ALAN exposure was quantified using satellite data, and pre-pregnancy stress was assessed via a structured questionnaire focusing on life, friend, economic, and total stress. The primary outcomes were PB (28-37 weeks) and extremely PB (<28 weeks). Logistic regression models were used to estimate the risk odd ratios (ORs) of PB along with per interquartile range (IQR) increase in ALAN exposure, as well as the potential effect modification by pre-pregnancy stress. Stratified analysis was also conducted to explore differences across socioeconomic status. RESULTS:A total of 549,654 pregnant women were participated in this study. An IQR (8.0 nW/cm2/sr) increase in 1-year average ALAN exposure was associated with ORs of 1.08 (95 %CI: 1.03-1.13) and 1.03 (95 %CI: 1.02-1.04) for extremely PB and PB, respectively. Higher pre-pregnancy life stress levels were associated with a stronger association between outdoor ALAN exposure (1-year average) and extremely PB, with an odds ratio of 1.04 (95 % CI: 0.97-1.10) in the lower group and 1.16 (95 % CI: 1.08-1.24) in the higher group (P for difference = 0.017). Higher total, life, economic, and friend pre-pregnancy stress may amplify the effect of ALAN on PB risk. For instance, an IQR increment in ALAN exposure was associated with ORs of 1.02 (95 % CI: 1.00, 1.03) and 1.07 (95 % CI: 1.05, 1.10) among participants with low and high total stress (P for difference <0.001). Stratified analyses indicated more apparent effect modifications by pre-pregnancy stress in participants with lower educational levels, with non-farmer occupation, living in rural areas, and living in south regions. CONCLUSIONS:Our findings suggest that higher pre-pregnancy stress levels may amplify the risk of PB associated with outdoor ALAN exposure, especially among women with lower educational levels, of non-farmer occupation, and living in rural areas or in south regions.

BACKGROUND:Ovarian cancer could induce alterations in both structure and function of the brain. This study employs Mendelian randomization (MR) to investigate the causal relationship between brain imaging-derived phenotypes (IDPs) and ovarian cancer, offering new insights into the potential clinical applications of IDPs for ovarian cancer risk assessment. METHODS:This study identified 587 brain IDPs using structural and diffusion magnetic resonance imaging (MRI) data from the UK Biobank and data were sourced from two independent Genome-Wide Association Studies (GWAS). We selected single nucleotide polymorphisms (SNPs) as instrumental variables based on rigorous criteria. To evaluate the causal effects of IDPs on the risk of ovarian cancer, we employed five MR models: Inverse Variance Weighted (IVW), MR-Egger regression, Weighted median, Weighted mode, and Simple mode. Furthermore, we conducted a meta-analysis to provide additional validation for our results. RESULTS:Forward MR analysis identified 72 IDPs that were significantly associated with the risk of ovarian cancer, with 65 remaining robust after conducting sensitivity tests. Conversely, reverse MR analysis indicated that 63 IDPs were influenced by ovarian cancer, highlighting a bidirectional causal relationship between these factors. The meta-analysis revealed that an increased cortical surface area of the right precentral gyrus was associated with a heightened risk of ovarian cancer, with an odds ratio (OR) of 1.139 (95% confidence interval [CI]: 1.037-1.250, P = 0.006, common effect model). In contrast, a larger volume of the right medial orbital frontal cortex was linked to a reduced risk of ovarian cancer, with an OR of 0.839 (95% CI: 0.744-0.946, P = 0.004, common effect model). Additionally, in the reverse MR analysis, a higher risk of ovarian cancer was associated with an increased fractional anisotropy (FA) in the right fornix and stria terminalis, while decreased orientation dispersion index (OD) in the left anterior corona radiata. CONCLUSIONS:This study provides compelling evidence of a causal relationship between IDPs and ovarian cancer risk. It suggests that IDPs might serve as valuable biomarkers for ovarian cancer risk assessment at brain-imaging levels and emphasize the need for further research to explore the biological mechanisms underlying these associations.

Light at night (LAN) is found to be associated with elevated overweight and obesity in broad population. However, evidence for the long-term LAN exposure trajectories and its influence to weight gain remained limited, especially to school-aged children who experience critical physical development. We aimed to analyze variations in body weight among children with different LAN exposure profiles, and how varying levels of LAN exposure influenced children’s overweight (including obesity) risk overt time. Children who had ≥ 5 school health examinations between 2005 and 2020 in Zhongshan were recruited in this population-based longitudinal study. LAN data of each child at each survey year were modeled with group-based trajectory model and named as sharp rise (reference; 5.5

To identify the risk factors for the survival of colorectal cancer (CRC) patients with type 2 diabetes mellitus (T2DM), compare the predictive performance of models based on different algorithms, and develop a risk score system to predict the survival risk of the target population. We analyzed data from the Hong Kong Hospital Authority Data Collaboration Laboratory (HADCL), including 10 749 CRC patients with T2DM from 2000 to 2020. We employed traditional statistical methods and machine learning algorithms to compare their performance using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) analysis was conducted to identify risk factors and attribute model outputs. A risk score system was developed using the AutoScore-Survival package for risk stratification. Key predictors of CRC survival among T2DM patients included age at cancer diagnosis, sex, T2DM duration, alcohol consumption, central obesity, hypertension, levels of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and serum potassium, and anti-lipid drug usage. Among the models assessed, the random survival forest showed the best performance. The risk score system was calibrated as follows: age at diagnosis, T2DM duration, LDL-C, glycated hemoglobin, creatinine, and body mass index. The AUCs for 1, 3, and 5 years of the tuned risk score system were 0.746, 0.718, and 0.677, respectively. The random survival forest model provides superior survival prediction compared to other models evaluated. A validated risk score system has been established, facilitating risk stratification for clinicians to manage these patients.

Population aging and increasing life expectancy raised concerns about functional dependency (FD) and multimorbidity. However, the impact of FD on later-life multimorbidity remains poorly understood. Participants from the China Health and Retirement Longitudinal Study (CHARLS) and the Survey of Health, Ageing and Retirement in Europe (SHARE) with complete baseline FD and 7-year follow-up data on multimorbidity were included, excluding those with multimorbidity or missing specific chronic diseases at baseline. FD levels, measured by inability to perform basic activities of daily living (ADLs) and instrumental activities of daily living (IADLs) at baseline wave, were categorized into five cumulative-score groups. Multimorbidity was defined as the presence of two or more chronic diseases. Logistic regression was employed to analyze the association of FD with incident multimorbidity and individual chronic diseases in each cohort. Cohort-specific estimates were combined using random-effects meta-analysis. Stratified analyses and interaction tests assessed modifications of associations. Compared to individuals without dependency, the risk of developing incident multimorbidity at 7-year follow-up with 2 FDs were significantly increased (2.13 [1.33–3.42] for ADL, 1.30 [1.02–1.66] for IADL), nearly doubling among patients with ≥ 4 FDs (1.52 [1.37–1.69] for ADL, 1.78 [1.18–2.69] for IADL). Significant associations between FDs and incident multimorbidity were observed across various subgroups, demonstrating dose-response relationships. Both cohorts exhibited positive interaction effects of age, gender, residential area, marital status, and social isolation on the associations between ADL dependency and incident multimorbidity. FD emerged as a significant risk factor for later-life multimorbidity, displaying interactions with demographic and social factors. This underscores the urgency for tailored interventions, integrated care models, and a reorientation of healthcare services to mitigate potential adverse health outcomes.

Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists' sensitivity by 9%. Reading time significantly decreases with DL assistance (218s vs 30s; p < 0.0001). In community-based organized screening, the DL model's sensitivity matches that of senior cytopathologists (0.878 vs 0.854; p > 0.999), yet it has reduced specificity (0.831 vs 0.901; p < 0.0001). Notably, hospital-based opportunistic screening shows that junior cytopathologists with DL assistance significantly improve both their sensitivity and specificity (0.857 vs 0.657, 0.840 vs 0.737; both p < 0.0001). When triaging human papillomavirus-positive cases, DL assistance exhibits better performance than junior cytopathologists alone. These findings support using the DL model as an assistance tool in cervical screening and case triage.

The aim of the study was to explore the impact of the clinical and embryological factors on the pregnancy outcome of FET cycle, while determining the applicability range for the primary predictor. A total of 4395 FET cycles (2986 couples) were included in this retrospective study. A bootstrapping stepwise variable selection algorithm was used to identify independent predictors of the clinical pregnancy rate (CPR) from 24 clinical and embryological variables. Multivariate logistic regression was carried out to assess the impact of these predictors. The primary predictor was stratified to ascertain their applicability. The final multivariate model incorporated the following 10 independent predictors: number of top-quality embryos transferred, age, number of in vitro fertilization/intracytoplasmic sperm injection attempts, endometrial thickness on trigger day, whether the embryo was cultured after thawing, fresh embryo transferred prior to FET, number of FET attempts, post-thaw embryo blastomere integrity, embryo developmental stage at transfer, and duration of infertility. The number of top-quality embryos transferred was found to be one of the most important predictors of pregnancy. For the younger age groups (≤ 30, 31–35 and 36–40 years), there was a significant increase in the CPR and live birth rate when more than one top-quality embryo was transferred. However, no significant differences were observed in the CPR between those with no or only one top quality embryo and those with two or more top-quality embryos transferred (14.0

The successful implementation of artificial intelligence-assisted diagnostic system (AIADS) in pathology relies not only on the maturity of AI technology but also on pathologists' cognition and acceptance of AI. However, research on pathologists' perceptions towards AIADS is limited. This study aims to explore pathologists' knowledge, attitudes, and practice toward AIADS and identify key factors influencing their willingness to use it, providing insights for the effective integration of AI technology in pathology. An online, nationwide, cross-sectional survey is to investigate pathologists' knowledge, attitudes and behavioral intention/practice regarding AIADS with a 5-point Likert scale. Descriptive analysis is used to present the results, while logistic regression examines factors influencing AIADS adoption. The mediating effect of attitude in the association between knowledge and behavioral intention is also explored. A total of 224 pathologists were surveyed, with 85 (37.9%) having used AIADS and 139 (62.1%) not using it. The mean scores for knowledge, attitude, and behavioral intention were 3.42 ± 0.97, 3.48 ± 0.44, and 3.47 ± 0.44, respectively. Pathologists who had used AIADS scored higher in knowledge, attitude, and behavioral intention, with clearer attitudes toward AIADS. Over 80% of pathologists supported the use of AIADS in clinical diagnostics, citing improved diagnostic speed and reduced workload as key reasons. The main concerns about AIADS were its diagnostic accuracy. Logistic regression analysis indicated that a greater likelihood of willingness to use AIADS was associated with not having used it before (OR=2.462, 95%CI 1.087-5.573), as well as with higher knowledge scores (OR=1.140, 95%CI 1.076-1.208) and more positive attitude scores (OR=1.119, 95%CI 1.053-1.189). Mediation analysis indicated an indirect path from knowledge to behavioral intention through attitude among individuals who have used AIADS, with the mediation effect accounting for 59.4%. In conclusion, most pathologists support the use of AIADS in clinical practice, but improvements in diagnostic performance are necessary. Enhancing pathologists' knowledge, attitudes, and user experience is crucial for the broader adoption of AIADS.