This study used UK Biobank data from ∼500 000 participants to examine how smoking intensity and duration jointly shape the risk of 12 smoking-related cancers. We modeled excess relative risks (ERRs) per pack-year, separating the effect of smoking intensity while holding total pack-years constant to compare low-intensity/long-duration with high-intensity/short-duration patterns. For the same cumulative exposure, cancer risk increases differed across these smoking patterns, indicating that pack-years alone do not fully capture carcinogenic risk. We further evaluated time since cessation (TSC) and found that longer TSC was associated with lower ERR/pack-year for most cancers. Lifestyle factors modified these associations: participants with favorable behaviors (e.g., regular physical activity and healthier diet) generally had lower ERRs/pack-year than those with unfavorable behaviors. Overall, our findings show that smoking pattern and cessation history meaningfully influence pan-cancer risk beyond cumulative exposure, supporting the incorporation of detailed smoking histories-intensity, duration, and cessation-into risk stratification, screening eligibility, and prevention strategies.

INTRODUCTION:Frailty is dynamic in later life, yet prospective evidence linking frailty trajectories to incident stroke across diverse populations is limited. The aim of this study was to investigate the association between frailty trajectories and incident stroke risk in adults aged 50 years and older using data from three nationally representative cohorts. METHODS:We analyzed harmonized data from three longitudinal cohorts: the English Longitudinal Study of Ageing (ELSA), the Health and Retirement Study (HRS), and the China Health and Retirement Longitudinal Study (CHARLS). Frailty was assessed using a deficit-accumulation frailty index at each wave. Frailty trajectories were identified using latent class mixed models, which classified participants into 3 groups: low, moderate, and increasing-to-high. Incident stroke was ascertained through self-reported physician diagnosis. Associations between frailty trajectory group membership and incident stroke were assessed using Cox proportional hazards models in the pooled dataset, stratified by cohort, and adjusted for demographic, socioeconomic, lifestyle, and laboratory covariates. RESULTS:Among 19,666 participants, the median follow-up was 7.5 years (IQR 7.0-8.0) and 1,131 (5.8%) incident strokes occurred. In the fully adjusted model, compared with the low trajectory group, the moderate trajectory group had a significantly higher risk of incident stroke (HR 2.05, 95% CI: 1.73-2.43) as did the increasing-to-high group (HR 3.51, 95% CI: 2.99-4.12) (both p < 0.001). Cohort-specific analyses showed consistent directions of association. CONCLUSION:Frailty trajectories were strongly associated with incident stroke risk across three prospective cohorts. Longitudinal frailty monitoring may help identify individuals at elevated stroke risk and inform targeted prevention strategies.

BACKGROUND:Differentiating the depressive phase of bipolar disorder (BD) from major depressive disorder (MDD) remains a significant clinical challenge. As CACNA1C is a prominent susceptibility gene for BD across diverse populations, this study evaluated the utility of its polymorphisms, combined with clinical risk factors, in distinguishing BD from MDD, while investigating the potential mediating role of sleep quality. METHODS:Subjects (188 MDD, 69 BD) were compared using Mann-Whitney U or chi-square tests. Logistic regression identified independent discriminative factors. Forty-two CACNA1C SNPs were analyzed via Haploview and UNPHASED to assess genetic distributions. Mediation analysis evaluated whether sleep quality mediates the relationship between CACNA1C variants and the BD phenotype. RESULTS:Younger onset age, positive family history, and psychotic symptoms were independently associated with BD diagnosis. Genetically, SNP rs2239128 showed a significant association with BD (adjusted P = 0.04895). Moderate linkage disequilibrium (LD) was observed for rs215976/rs215992 (r2 = 0.543, D' = 0.801), and weak LD for rs215995/rs123263 (r2 = 0.157, D' = 0.433). Four haplotypes-rs215976/rs215992 CC (OR = 0.257) and rs215995/rs123263 T-G (OR = 0.112), C-T (OR = 0.029), and C-G (OR = 0.814)-were significantly associated with BD (all P < 0.05). Sleep quality showed no significant mediating effect. CONCLUSION:Integrated clinical risk factors and CACNA1C genetic variations are promising indicators for differentiating BD from MDD. Although sleep quality did not demonstrate a mediating role, these findings contribute preliminary insights into the distinct genetic and phenotypic architecture of these disorders, offering a foundation for future mechanistic exploration and personalized interventions.

BackgroundFalls are a leading cause of injury, disability, and death among older adults, posing significant public health challenges. However, comprehensive global analyses of fall-related burdens in older populations remain scarce. ObjectiveThis study aimed to explore the patterns and distribution of the global, regional, and national burden of falls among adults aged 65 years and older. MethodsData from the Global Burden of Disease study 2021 were used to assess the overall, disability, and mortality burden of falls among adults aged 65 years and older. Age-standardized rates of disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost were calculated to compare burdens across countries. Health inequalities were evaluated via the slope index of inequality and the concentration index. Frontier analysis identified optimal burden levels by sociodemographic index (SDI). Bayesian age-period-cohort models projected trends up to 2050. ResultsDALY age-standardized rates showed a U-shaped distribution across SDI regions: lower-SDI countries faced higher mortality burdens, while higher-SDI countries had elevated disability burdens. Despite an increase in absolute overall burden inequality from 1990 to 2021, absolute inequalities in YLDs and years of life lost declined, with DALYs and YLDs exhibiting relatively more balanced distributions. Frontier analysis pinpointed countries with the greatest burden reduction potential. Projections suggest decreasing overall and mortality burdens by 2050 but rising disability burdens. ConclusionsHigher- and lower-SDI countries face distinct fall-related challenges. Reducing cross-national health inequalities and closing gaps between the observed burden and the optimal burden level achievable at a similar SDI level are critical. Despite projected declines in the overall burden (DALYs), the rising disability burden (YLDs) could present evolving challenges, potentially underscoring the importance of proactive preparedness.

Objectives Mental health is an important indicator in the evaluation of healthy aging. As individuals age, they become increasingly susceptible to symptoms of anxiety, depression, and insomnia, which are often exacerbated by diminished daily functioning, reduced social engagement, and a decline in self-care capabilities. This study uses network analysis to examine the symptom interactions and applies clique percolation to detect overlapping items across the three symptom domains. Methods This online survey was conducted in three cities in Jiangsu Province between September 2023 and March 2024. Using a cross-sectional design, anxiety, depression, and insomnia symptoms in elderly individuals were assessed with the Generalized Anxiety Disorder Scale (GAD-7), the Patient Health Questionnaire (PHQ-9), and the Insomnia Severity Index (ISI-7). The symptom network was constructed using the EBICglasso algorithm and the clique-percolation algorithm was employed to reveal cross-dimensional connections between symptoms. The study was approved by the Ethics Committee of Huashan Hospital, Fudan University (Approval No. 2022-057). Results A total of 2,086 elderly individuals were included, with mean age of 72.5 years and 1,061 females (50.9%). The prevalence rates for anxiety, depression, and insomnia were 21.6%, 13.2%, and 46.3%, respectively. Based on clique percolation analysis, three symptom clusters were identified, revealing clear cross-dimensional symptom connectivity among anxiety, depression, and insomnia. Network analysis revealed that the central symptoms—namely “difficulty maintaining sleep,” “the perceived impact of sleep disturbances on quality of life by others,” and “sleep interference with daytime functioning”—were pivotal in the network. The bridging nodes linking anxiety, depression, and insomnia were identified as “sleep problems,” “suicidal ideation,” and “restlessness.” Conclusion The central nodes within this network are predominantly sleep-related, underscoring their significant role in the network’s structural integrity. Notably, “sleep problems,” as the principal bridging node, is important to the comorbidity of anxiety, depression, and insomnia. Addressing and preventing these key symptoms could substantially enhance the psychological well-being of the elderly, offering a promising direction for intervention strategies aimed at fostering healthier aging.

Objectives This study aimed to analyze the burden of idiopathic developmental intellectual disability (IDII) attributed to lead exposure in Asian children and adolescents aged <20 years from 1990 to 2021. Methods We estimated the disease burden using disability-adjusted life years (DALYs). Temporal trends from 1990 to 2021 were analyzed using joinpoint regression. Decomposition analyses were performed to disaggregate changes in DALYs. Health inequality analyses and frontier analyses were applied to explore the relationship between DALYs and the socio-demographic index (SDI). The Bayesian age-period-cohort model was employed to predict the future disease burden by 2035. Results The number of IDII DALYs attributable to lead exposure among children and adolescents in Asia decreased from 1.11 million (95% uncertainty interval [UI]: 0.48-1.96 million) in 1990 to 0.82 million (95% uncertainty interval: 0.36-1.49 million) in 2021, which was primarily driven by epidemiologic changes. Females and the 10-14-year age group faced the highest risk. Lower-SDI regions, especially South Asia, bore a disproportionately higher burden, with absolute health inequality narrowing but relative inequality remaining prevalent between 1990 and 2021. Projections to 2035 showed a continued decline in disease burden in India and Pakistan, in contrast to rising in Afghanistan and Yemen. Conclusions This study underscored the critical need to strengthen targeted lead exposure interventions addressing gender, age, and regional disparities.

BACKGROUND:Falls can repeatedly occur as people age, which leads to injury, disability and mortality in older adults. Sleep duration may be a modifiable factor, but longitudinal evidence on its association with recurrent falls is limited. METHODS:We analysed data from two prospective cohorts: the China Health and Retirement Longitudinal Study (CHARLS) and the English Longitudinal Study of Ageing (ELSA). Baseline self-reported sleep duration was classified as short (<6 hours), normal (6-10 hours) and long (>10 hours). Fall status was assessed in each follow-up wave and analysed as recurrent events. HRs and 95% CIs were estimated using Andersen-Gill models. Non-linear associations were explored using restricted cubic splines (RCS). RESULTS:A total of 11 603 participants from CHARLS and 8083 from ELSA were included. During median follow-ups of 9.0 years and 9.1 years, 7783 and 6472 recurrent falls were reported, respectively. Compared with normal sleep, short sleep was associated with higher fall risk (CHARLS: HR 1.127, 95% CI 1.066 to 1.191; ELSA: HR 1.115, 95% CI 1.041 to 1.195). Long sleep also showed increased risk (CHARLS: HR 1.293, 95% CI 1.020 to 1.640; ELSA: HR 1.413, 95% CI 1.027 to 1.946). RCS analysis revealed non-linear relationships, with the lowest risk observed at 7-8 hours. CONCLUSION:Both short and long sleep durations are associated with increased risk of recurrent falls in adults aged 50 and above. A sleep duration of 7-8 hours appears to represent the lowest risk. Sleep-focused interventions may be a valuable strategy for fall prevention in public health and geriatric care.

Major depressive disorder (MDD) is a global mental health challenge characterized by high prevalence, disability, and recurrence, with its susceptibility showing substantial polygenic inheritance. Polygenic risk scores (PRS) provide a novel method to explore cumulative polygenic effects on MDD pathogenesis. Initial study inclusion comprised 721 patients meeting Diagnostic and Statistical Manual of the American Psychiatric Association, fourth edition (DSM-IV) diagnostic criteria for MDD and 960 healthy controls. Using summary statistics from the Psychiatric Genomics Consortium (PGC) East Asian cohort for MDD and schizophrenia (SCZ) phenotypes, as well as from the BioBank Japan (BBJ) for insomnia and body mass index (BMI) phenotypes as the base dataset, we performed stringent quality control (QC) on the data using PLINK v1.90. Subsequently, PRSice v2.3.5 with its PRSet function was employed to generate optimal PRSs for the four phenotypes in QC-passed patients and controls, and pathway-specific PRSs based on Gene Ontology (GO) gene sets. Statistical comparisons of PRS differences between MDD patients and healthy controls were conducted using independent two-sample t-tests or Mann-Whitney U tests in R v4.3.0. Furthermore, logistic regression analyses, adjusted for age, sex, and ancestry principal components as covariates, were performed to assess associations between phenotypic PRSs, pathway PRSs, and MDD status. A significant difference was demonstrated in MDD-PRS between patients and controls (p < 0.001), with a significant logistic prediction model (empirical p = 9.999 × 10⁻⁵, pseudo-R² = 2.1

The clinical utility of metabolic syndrome (MetS) in predicting cardiovascular disease (CVD) and diabetes has been questioned due to its binary definition, which results in substantial loss of information from metabolic indicators. This study aims to longitudinally evaluate MetS measurement indicators, identify their multi-trajectory patterns over time, and assess the associated CVD risk. Group-based multi-trajectory modeling (GBMTM) was applied to metabolic syndrome indicators to identify multi-trajectory patterns and describe baseline characteristics of subgroups. Subsequently, with CVD as the outcome, trajectory groups were incorporated into interval-censored Cox proportional hazards models to estimate the associated CVD risks across different trajectory patterns. This study identified five distinct metabolic patterns through trajectory typing. The “progressive hyperglycemia trajectory” (characterized by high and continuously increasing fasting plasma glucose [FPG] levels) and the “persistent dyslipidemia trajectory” (characterized by persistently high triglycerides [TG], low high-density lipoprotein cholesterol [HDL-C] with continuous decrease) demonstrated higher CVD incidence density. After adjusting for age and sex, their hazard ratios (HR) were 1.469 (95

BACKGROUND:Depression is a leading cause of global disease burden, with diagnosis often relying on subjective measures. Genetic factors significantly contribute to its onset, supporting the use of machine learning for objective risk prediction. This study aims to develop a genetic-based risk prediction model by machine learning to distinguish between patients with depression and healthy people. METHODS:This study included 791 patients with depression and 413 healthy controls. Based on the Kyoto Encyclopedia of Genes and Genomes pathways, 1309 candidate genes were selected. Genotyping used the Illumina MiSeq platform. Single nucleotide polymorphisms (SNPs) were filtered for missing rate > 5% and minor allele frequency < 5%. The data were split into training and testing sets. In training set, feature selection combined Boruta and Least Absolute Shrinkage and Selection Operator Regression (LASSO) methods. In testing set, model performance was evaluated. RESULTS:The final model incorporated 12 features (only 12 SNPs). It achieved an area under the curve (AUC) of 0.66 in the testing set, demonstrating good discrimination and stability. CONCLUSION:This risk prediction model shows promise for objective depression risk assessment and could aid in early detection.

Abstract Objectives To investigate the attitudes of Chinese radiologists or interns towards generative pre-trained (GPT)-like technologies. Methods A prospective survey was distributed to 1339 Chinese radiologists or interns via an online platform from October 2023 to May 2024. The questionnaire covered respondent characteristics, opinions on using GPT-like technologies (in clinical practice, training and education, environment and regulation, and development trends), and their attitudes toward these technologies. Logistic regression was conducted to identify underlying factors associated with the attitude. Results After quality control, 1289 respondents (median age, 37.0 years [IQR, 31.0–44.0 years]; 813 males) were surveyed. Most of the respondents (n = 1223, 94.9%) supported adoption of GPT-like technologies. Based on the acceptance level of GPT-like technologies, the respondents were 3 (0.2%), 29 (2.2%), 352 (27.3%), 677 (52.5%), and 228 (17.7%) from low to high acceptance degrees. Multivariable analysis revealed significant associations between positive attitudes towards GPT-like technologies and their acceptance: writing papers and language polishing (odds ratio [OR] = 1.99; p < 0.001), influence of colleagues using such technologies (OR = 1.77; p = 0.007), government regulation introduction (OR = 2.25; p < 0.001), and enhancement of decision support capabilities (OR = 2.67; p < 0.001). Sensitivity analyses confirmed these results for different acceptance thresholds (all p < 0.001). Conclusions Chinese radiologists or interns generally support GPT-like technologies due to their potential capabilities in clinical practice, medical education, and scientific research. They also emphasize the need for regulatory oversight and remain optimistic about their future medical applications. Critical relevance statement This study highlights the broad support among Chinese radiologists for GPT-like technologies, emphasizing their potential to enhance clinical decision-making, streamline medical education, and improve research efficiency, while underscoring the need for regulatory oversight. Key Points The impact of GPT-like technologies on the radiology field is unclear. Most Chinese radiologists express the supportive adoption of GPT-like technologies. GPT-like technologies’ capabilities at research and clinic prompt the attitude. Graphical Abstract

BACKGROUND:Most antidepressants with similar pharmacological characteristics exhibit comparable therapeutic efficacy but differ in side effects. Therefore, we used a retrospective design to compare biochemical changes induced by six antidepressants and identify differences among them. METHODS:Case records from 1706 hospitalized patients with major depressive disorder (MDD) receiving antidepressant monotherapy were divided into six groups based on the specific antidepressants used: paroxetine, sertraline, fluoxetine, escitalopram, venlafaxine, and duloxetine. Electrolytes, hepatic and renal functions, body weight, and glycolipid metabolism were assessed at baseline and 2 weeks post-antidepressant initiation. Paired analysis was used for comparing the changes prior to and after administration within each group, and analysis of covariance was used for evaluating the distinctions among the six groups. RESULTS:After 2 weeks of treatment, significant decreases in serum sodium and chloride levels were observed with venlafaxine, duloxetine, and fluoxetine, while potassium, phosphorus, and carbon dioxide concentrations tended to increase across all six antidepressants. In terms of hepatic indicators, these antidepressants significantly elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), and γ-glutamyl transpeptidase (GGT) levels, with duloxetine showing the most pronounced changes from baseline, while decreasing total and direct bilirubin. Sertraline effectively reduced uric acid, although changes in renal indicators were mild with other antidepressants. Notably, these antidepressants were associated with an unfavorable lipid profile, particularly elevated triglycerides and cholesterol, but they lowered blood glucose during the acute phase. LIMITATION:Residual confounding may indirectly influence the retrospective outcomes. CONCLUSION:Early biochemical changes can distinguish differences among antidepressants and guide individualized medication.

Background: While millions of people suffer from major depressive disorder (MDD), research has shown that individual differences in antidepressant efficacy exist, potentially attributable to various factors. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response are seldom reported. Here, we examined whether PRSs for MDD and schizophrenia (SCZ) are associated with antidepressant effectiveness and the influence of other factors. Methods: A total of 999 patients were included, and the PRSs for the MDD and SCZ were calculated. The main outcome was a change in the 17-item Hamilton Depression Rating Scale (HAM-D17) - D17) scores from before to after 2-week treatment. The Mann-Whitney test, Spearman correlation analysis, multiple stepwise linear regression analysis, and interaction analysis were used for statistical analysis. Results: In the 912 subjects passing quality control, a difference in the HAM-D17 score reduction rate between the MDD phenotype PRS (MDD-PRS) high-risk and the low-risk groups was discovered (P P = 0.009), and a correlation was found between the MDD-PRS and the HAM-D17 score reduction rate (r r =-0.075, P = 0.024). Moreover, antidepressant efficacy was related to MDD-PRS ((3 =-4.086, P = 0.039), the Snaith-Hamilton Pleasure Scale- total score ((3 =-0.009, P = 0.005), and non-first episode ((3 =-0.039, P < 0.001). However, the result of the interaction analysis was nonsignificant. Limitations: The main limitation was that only 1309 targeted genes were selected based on pathways known to be involved in MDD and/or antidepressant effects. Conclusion: These findings suggest a difference in antidepressant efficacy between patients in different MDD-PRS groups. Moreover, the MDD-PRS combined with clinical characteristics partially explained inter-individual differences in antidepressant efficacy.

Background Recent studies suggest that neutrophil elastase inhibitor (Sivelestat) may improve pulmonary function and reduce mortality in patients with acute respiratory distress syndrome. We examined the association between receipt of sivelestat and improvement in oxygenation among patients with acute respiratory distress syndrome (ARDS) induced by COVID-19. Methods A large multicentre cohort study of patients with ARDS induced by COVID-19 who had been admitted to intensive care units (ICUs). We used propensity score matching to compare the outcomes of patients treated with sivelestat to those who were not. The differences in continuous outcomes were assessed with the Wilcoxon signed-rank test. Kaplan-Meier method was used to show the 28-day survival curves in the matched cohorts. A log-rank P-test stratified on the matched pairs was used to test the equality of the estimated survival curves. A Cox proportional hazards model that incorporated a robust sandwich-type variance estimator to account for the matched nature of the data was used to estimate hazard ratios (HR). All statistical analyses were performed with SPSS 26.0 and R 4.2.3. A two-sided p-value of < 0.05 was considered statistically significant. Results A total of 387 patients met inclusion criteria, including 259 patients (66.9%) who were treated with sivelestat. In 158 patients matched on the propensity for treatment, receipt of sivelestat was associated with improved oxygenation, decreased Murray lung injury score, increased non-mechanical ventilation time within 28 days, increased alive and ICU-free days within 28 days (HR, 1.85; 95% CI, 1.29 to 2.64; log-rank p < 0.001), shortened ICU stay and ultimately improved survival (HR, 2.78; 95% CI, 1.32 to 5.88; log-rank p = 0.0074). Conclusions Among patients with ARDS induce by COVID-19, sivelestat administration is associated with improved clinical outcomes.

Objectives:Since the global outbreak of SARS-CoV-2 in 2019, COVID-19 reinfection has become an increasing concern, particularly during the spread of the Omicron variant. Despite numerous international studies on COVID-19 reinfection, research focusing on healthcare workers, particularly those in primary care settings in mainland China, remains limited. This study aims to evaluate COVID-19 reinfection rates among primary healthcare workers (PHWs) in Jiangsu Province and to explore potential risk factors contributing to reinfection. Methods:This study utilized a combination of online questionnaires and on-site surveys to conduct two waves of investigation targeting PHWs after epidemic control policy adjustment in Jiangsu Province. Differences between the infection at the baseline visit and re-infection at the follow-up visit were analyzed, and multivariate logistic regression was used to assess the factors influencing reinfection. Results:A total of 5,541 PHWs were included in the study. At the baseline visit, the initial infection rate was 85.85% [95% confidence interval (CI): 84.93-86.77%], and the self-reported reinfection rate was 40.05% (95% CI: 38.65-41.44%). After adjustment, the reinfection rate was 29.41% (95% CI: 28.12-30.71%). The median reinfection interval between the two infections was 146 days (Interquartile range: 129-164 days). Logistic regression model revealed that female sex [odds ratio (OR) = 1.376, 95% CI: 1.190-1.592], history of fever clinic work (OR = 1.179, 95% CI: 1.045-1.330), working over 8 h per day (OR = 1.178, 95% CI: 1.040-1.336), being a nurse (OR = 1.201, 95% CI: 1.029-1.402), and a "less meat, more vegetables" diet (OR = 1.206, 95% CI: 1.020-1.426) were significant risk factors for reinfection. Additionally, regular physical exercise was found to be a protective factor (OR = 0.861, 95% CI: 0.754-0.983). Conclusion:COVID-19 reinfection rates were relatively high among PHWs in Jiangsu Province, particularly among women, nurses, those with fever clinic experience and working over 8 h per day. This study offers valuable insights for the prevention of COVID-19 reinfection and the development of protection strategies for PHWs. It is recommended that more targeted protective measures be implemented for high-risk groups, including appropriate work arrangements, regular health monitoring, and the promotion of healthy lifestyle habits.

BACKGROUND:Functional abnormalities in different brain regions are related to major depressive disorder (MDD). In our previous study, we demonstrated that DNA methylation of Tryptophan Hydroxylase-2 (TPH2) is related to the occurrence of MDD. The present study aimed to identify the interaction of the functional activities of brain regions identified as regions of interest (RoI) in MDD with TPH2 gene methylation to explore their relationship. METHODS:Data from 98 patients with MDD and 63 controls were utilized. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and fractional ALFF (fALFF), were used to identify ROIs regions in the RESTPlus Software of MATLAB. General linear regression (GLM) was performed to analyze the association between functional connectivity (FC) found in rs-fMRI and the effect of TPH2 DNA methylation in patients with MDD and controls. RESULTS:In the rs-fMRI analysis, the ALFF of right superior generalized gyrus (STG) was significantly different between the MDD and HCs groups (p < 0.05). The ReHo of right Middle temporal gyrus (MTG) and left middle occipital gyrus (MOG) were significantly different between the two groups (p < 0.05). These ROIs were used to further analyze the FC differences between MDD and HCs, and it was found that the FC of right STG and right superior frontal gyrus (SFG) and the FC of right MTG and right MOG were significantly different between the two groups (p < 0.05). It was further found that the interaction between ALFF activity of the right STG and TPH2-5-203 methylation (β=-2.108, p = 0.004), ReHo activity level of the right MTG, and TPH2-5-203 methylation were correlated with the occurrence of MDD (β=-1.720, p = 0.018). CONCLUSION:This study found that the functional activities of the temporal lobe, middle occipital gyrus, and superior frontal gyrus were abnormal in patients with MDD compared to HCs. Furthermore, the interaction of functional activities of the right superior temporal gyrus /middle temporal gyrus and TPH2 methylation were associated with the occurrence of MDD, suggesting that the combination of functional activities and DNA methylation was helpful for diagnosis of MDD.

ABSTRACT Background Endometrial cancer (EC) stands as the predominant gynecological malignancy impacting the female reproductive system on a global scale. N6‐methyladenosine, cuproptosis‐ and ferroptosis‐related biomarker is beneficial to the prognostic of tumor patients. Nevertheless, the correlation between m6A‐modified lncRNAs and ferroptosis, copper‐induced apoptosis in the initiation and progression of EC remains unexplored in existing literature. Aims In this study, based on bioinformatics approach, we identified lncRNAs co‐expressing with cuproptosis‐, ferroptosis‐, m6A‐ related lncRNAs from expression data of EC. By constructing the prognosis model in EC, we screened hub lncRNA signatures affecting prognosis of EC patients. Furthermore, the guiding value of m6A‐modified ferroptosis‐related lncRNA (mfrlncRNA) features was assessed in terms of prognosis, immune microenvironment, and drug sensitivity. Method Our research harnessed gene expression data coupled with clinical insights derived from The Cancer Genome Atlas (TCGA) collection. To forge prognostic models, we adopted five machine learning approaches, assessing their efficacy through C‐index and time‐independent ROC analysis. We pinpointed prognostic indicators using the LASSO Cox regression approach. Moreover, we delved into the biological and immunological implications of the discovered lncRNA prognostic signatures. Results The survival rate for the low‐risk group was markedly higher than that for the high‐risk group, as evidenced by a significant log‐rank test (p < 0.001). The LASSO Cox regression model yielded concordance indices of 0.76 for the training set and 0.77 for the validation set, indicating reliable prognostic accuracy. Enrichment analysis of gene functions linked the identified signature predominantly to endopeptidase inhibitor activity, highlighting the signature's potential implications. Additionally, immune function and drug density emphasized the importance of early diagnosis in EC. Conclusion Five hub lncRNAs in EC were identified through constructing the prognosis model. Those genes might be potential biomarkers to provide valuable reference for targeted therapy and prognostic assessment of EC.

INTRODUCTION:Diagnostic Criteria for Psychosomatic Research (DCPR) serve as an instrument for identifying and classifying specific psychosomatic syndromes that are not adequately encompassed in standard nosography. The present study aimed at measuring the prevalence of DCPR syndromes in different clinical settings and exploring factors associated to such diagnoses. METHODS:A cross-sectional and nationwide study recruited 6,647 patients in different clinical settings: 306 were diagnosed with fibromyalgia (FM), 333 with irritable bowel syndrome, 1,109 with migraine, 2,550 with coronary heart disease (CHD), and 2,349 with type 2 diabetes (T2D). Participants underwent DCPR diagnostic interview and were assessed for depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder 7-Item Scale), and subjective well-being (World Health Organization-5 Well-Being Index). The PsychoSocial Index was used to evaluate global well-being, stress, and abnormal illness behavior. The prevalence of DCPR diagnoses was calculated, and factors associated to such diagnoses were analyzed by logistic regression. RESULTS:Alexithymia (64.47%), irritable mood (20.55%), and demoralization (15.60%) were the most prevalent psychosomatic syndromes, with demoralization being most common in FM (49.02%). The factors associated to DCPR diagnoses encompassed high anxiety or abnormal illness behavior, and poor well-being. Notably, stress was found to be associated specifically to FM and T2D, with OR of 1.24 (95% CI: 1.06-1.46) and 1.26 (95% CI: 1.18-1.36), respectively. CONCLUSION:DCPR is a clinically helpful complementary assessment tool in need of being widely implemented in clinical settings in order to have a comprehensive picture of the patients.

Abstract Modelling complex smoking histories, with more comprehensive and flexible methods, to show what profile of smoking behavior is associated with the risk of different cancers remains poorly understood. This study aims to provide insight into the association between complex smoking exposure history and pan-cancer risk by modelling both smoking intensity and duration in a large-scale prospective cohort. Here, we used data including a total of 0.5 million with cancer incidences of 12 smoking-related cancers. To jointly interpret the effects of intensity and duration of smoking, we modelled excess relative risks (ERRs)/pack-year isolating the intensity effects for fixed total pack-years, thus enabling the smoking risk comparison for total exposure delivered at low intensity (for long duration) and at a high intensity (for short duration). The pattern observed from the ERR model indicated that for a fixed number of pack-years, low intensity/long duration or high intensity/short duration is associated with a different greater increase in cancer risk. Those findings were extended to an increase of time since smoking cessation (TSC) showing a reduction of ERR/pack-year for most cancers. Moreover, individuals with favorable lifestyle behaviors, such as regular physical activity and healthy dietary intakes, were shown to have lower ERRs/pack-year, compared to those with unfavorable lifestyle behaviors. Overall, this study systematically evaluates and demonstrates that for pan-cancer risks, smoking patterns are varied, while reducing exposure history to a single metric such as pack-years was too restrictive. Therefore, cancer screening guidelines should consider detailed smoking patterns, including intensity, duration, and cessation, for more precise prevention strategies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was supported by the: National Natural Science Foundation of China (NSFC, 82204033); the Natural Science Foundation of Jiangsu grant (BK2022020826); Fundamental Research Funds for the Central Universities of China (2242022R10062/3225002202A1), Medical Foundation of Southeast University (4060692202/021), Zhishan Young Scholar Award at the Southeast University (2242023R40031); The Scientific Research Project for Health Commission of Anhui Province (AHWJ2023A20172; AHWJ2023BAa20055). The funders had no role in the study design, data collection, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used (or will use) ONLY openly available human data that were originally located at UK Biobank. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at UK Biobank.