BACKGROUND AND AIMS:The systemic inflammation response index (SIRI) and albumin-globulin ratio (AGR) reflect inflammatory and nutritional status relevant to cardiovascular health, yet their joint prognostic relevance in early-stage cardiovascular-kidney-metabolic (CKM) syndrome remains unclear. We evaluated the independent and joint associations of SIRI and AGR with all-cause and cardiovascular mortality in CKM stages 0-3. METHODS:In this prospective cohort study, 284,012 UK Biobank participants with CKM stages 0-3 were followed until August 2025. Associations of SIRI and AGR with mortality were assessed using Cox proportional hazards models. Mediation analyses evaluated contributions of cardiometabolic factors. Non-linear dose-response relationships were examined using restricted cubic splines, and joint analyses were performed based on spline-derived cut-offs. RESULTS:Over a median follow-up of 16.48 years, 24,022 all-cause and 2712 cardiovascular deaths occurred. Higher SIRI was associated with increased mortality (HR 1.24 [95% CI 1.22-1.25] for all-cause; 1.23 [1.18-1.29] for cardiovascular), whereas higher AGR was inversely associated (HR 0.61 [0.58-0.64]; 0.64 [0.55-0.75]). BMI and eGFR mediated 11-18% of the AGR-mortality association, whereas SIRI risk was largely independent of conventional pathways. Joint analyses identified a high-risk phenotype (high SIRI/low AGR) with the greatest mortality risk. Cardiovascular associations were stronger among participants with favorable social determinants of health (p for interaction <0.05). CONCLUSION:In early CKM stages, SIRI and AGR were independently and jointly associated with long-term risks of all-cause and cardiovascular mortality. Incorporating SIRI and AGR into the CKM framework may improve early risk stratification and identification of high-risk inflammatory-nutritional phenotypes in preclinical stages.

Background:Habitual snoring is a common symptom in primary care but is often overlooked as a minor nuisance. Although sleep-disordered breathing has been associated with adverse health outcomes, the association between self-reported habitual snoring and health-related quality of life (HRQoL) remains insufficiently characterized among adults attending community health centers in China. This study examined the association of habitual snoring with physical and mental HRQoL in this setting. Methods:We conducted a cross-sectional study among 2,312 adults recruited from community health centers in Shanghai, China. Habitual snoring was defined as self-reported snoring on at least 3 nights per week. HRQoL was assessed using the 12-item Short Form Health Survey (SF-12), from which the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were derived. PCS and MCS scores < 50 were classified as below norm and were not interpreted as diagnostic cutoffs. Multivariable logistic regression models were used to examine the associations of habitual snoring with below-norm HRQoL after adjustment for sociodemographic characteristics, health behaviors, BMI, and chronic disease. As a sensitivity analysis, PCS and MCS were additionally analyzed as continuous outcomes using multivariable linear regression models. Results:Of the 2,312 participants (mean age 44.4 years; 63% female), 36.9% reported habitual snoring (≥3 nights/week). Habitual snorers had significantly lower scores on both the Physical Component Summary (PCS: 42.9 vs. 46.0, P < 0.001) and the Mental Component Summary (MCS: 49.9 vs. 50.8, P = 0.023) compared to non-snorers. After multivariable adjustment, habitual snoring was associated with higher odds of below-norm physical HRQoL (ORm = 1.590, 95% CI: 1.291-1.957) and mental HRQoL (ORm = 1.250, 95% CI: 1.047-1.493). In sensitivity analyses, habitual snoring remained significantly associated with lower PCS (β = -2.38, 95% CI: -3.00 to -1.75) and lower MCS (β = -1.21, 95% CI: -2.04 to -0.38). Conclusion:Habitual snoring was common among adults attending community health centers in Shanghai and was associated with lower physical and mental HRQoL, particularly physical HRQoL. These findings support greater attention to simple symptom-based screening for snoring in community care, where it may help identify individuals who may benefit from further sleep-related evaluation and health management.

Decorin (DCN) predominantly produced by fibroblasts is a small leucine-rich proteoglycan with tumor-suppressive property. However, whether DCN has a role in shaping the tumor immune microenvironment remains elusive. The TCGA and GEO databases were analyzed to identify fibroblast-specific secretory proteins that are downregulated in most types of human tumors, positively correlate with CD8⁺ T cell infiltration, and associate with improved response to immune checkpoint blockade (ICB) therapy. The function of DCN in vivo was assessed using cell lines with stable DCN overexpression in both immunocompetent and immunodeficient mice. The changes in the composition and function of immune cell subpopulations in tumors were analyzed by flow cytometry analysis (FCM) and immunofluorescence staining. The role of CD8⁺ T cells in the DCN-mediated tumor suppression was further elucidated by utilizing B2m-knockout tumor cells and CD8⁺ T cell depletion assays. The in vitro co-culture system of tumor cells and T cells was applied to dissect the effects of DCN on CD8+ T lymphocyte activation and functions. Finally, the therapeutic efficacy of DCN in combination with anti-PD1 antibody was evaluated in mouse tumor model. DCN-mediated tumor suppression was present in immunocompetent mice, wherein either depletion of CD8⁺ T cells in mice or ablation of β2M in tumor cells abrogated the tumor-inhibitory effects mediated by DCN, indicating the importance of CD8⁺ T cells in the DCN-antitumor activities. DCN overexpression promoted CD8⁺ T cell infiltration into tumors and increased the production of TNF-α, IFN-γ, and perforin in infiltrating T cells, and DCN expression substantially enhanced the tumor-suppressive efficacy of anti-PD1 therapy. More importantly, integrative analyses of clinical data indicated that DCN expression is downregulated in multiple types of human tumors and positively associated with the presence of CD8⁺ T cells and their expression of cytotoxic genes. Furthermore, high DCN levels were correlated with favorable prognosis in certain types of cancer patients with ICB therapy. Our study reveals that DCN functions as a tumor-suppressive factor by enhancing T cell response, and proposes the potential of exogenous DCN as a supplement to cancer immunotherapy.

BACKGROUND:Herpes zoster (HZ) poses a growing health burden, yet vaccine uptake remains low in China. Amid ongoing digital transformation, eHealth literacy has emerged as a key factor in vaccination decisions. This study aimed to examine its association with HZ vaccination intention through Health Belief Model (HBM) constructs, focusing on variations across socioeconomic status (SES). METHODS:A cross-sectional survey was conducted among 2047 adults (18-59 years) in Shanghai from October to December 2022. Participants completed questionnaires assessing HZ vaccination intention, eHealth literacy, and HBM-related beliefs (perceived benefits, threat, barriers, and cues to action). SES was derived from education, income, and occupation using latent class analysis. Structural equation modelling was used to test direct and mediated pathways, with SES-stratified analyses. RESULTS:Only 10.3% of participants reported vaccination intention. eHealth literacy was positively associated with perceived benefits (β = 0.331, P < 0.001) and threat perception (β = 0.239, P < 0.001), and negatively associated with perceived barriers (β = -0.051, P = 0.024). Vaccination intention was positively associated with perceived threat (β = 0.193, P < 0.001) and cues to action (β = 0.220, P < 0.001), and negatively with perceived barriers (β = -0.259, P < 0.001). Associations between eHealth literacy and HBM constructs were stronger among high-SES participants. CONCLUSIONS:eHealth literacy is associated with HZ vaccination intention through multiple cognitive pathways, with distinct patterns across SES groups. Tailored strategies are needed to promote more equitable HZ vaccine uptake. High-SES groups may benefit from better online information, while external support is more crucial for disadvantaged groups.

BackgroundThe rapid development of digital technologies has intensified concerns about social media addiction, particularly among university students. The perceived parenting styles they experienced may influence this risk, with unmet interpersonal needs acting as potential mediators. ObjectiveThis study aimed to investigate whether perceived burdensomeness and thwarted belongingness mediate the relationship between perceived parenting and social media addiction among university students. MethodsA cross-sectional survey was conducted among 1766 university students from March 2023 to May 2023. Parenting styles were assessed using the short-form Egna Minnen av Barndoms Uppfostran (Swedish for “My memories of upbringing”) for Chinese, unmet interpersonal needs were assessed via the 15-item version of the Interpersonal Needs Questionnaire, and social media addiction was assessed through the Social Network Addiction Tendency Scale. Parenting profiles were identified using latent profile analysis, and mediation models were examined through path analysis. ResultsThree perceived parenting profiles were identified: supportive (1094/1766, 61.9%), emotionally distant (192/1766, 10.9%), and controlling-critical (480/1766, 27.2%). Path analyses indicated indirect effects of parenting profile on social media addiction via unmet interpersonal needs. Compared to the supportive group, the emotionally distant (β=0.107 for perceived burdensomeness; β=0.354 for thwarted belongingness) and controlling-critical (β=0.543 for perceived burdensomeness; β=0.457 for thwarted belongingness) groups reported higher unmet interpersonal needs, which in turn were positively associated with social media addiction (β=0.119 for perceived burdensomeness; β=0.158 for thwarted belongingness). Multigroup analyses further showed that perceived burdensomeness was significantly associated with social media addiction among male students (β=0.180; P<.001) but not among female students (β=0.038; P=.30). ConclusionsUnmet interpersonal needs mediate the link between maladaptive parenting and social media addiction. Early interventions that promote emotional warmth and reduce rejecting and controlling behaviors may help prevent digital dependence among young adults, particularly when tailored to specific interpersonal vulnerabilities.

Background:Cyberbullying victimization is a significant risk factor for poor psychological well-being among college students. Existing tools fail to capture the distinct dimensions of victimization in social media contexts. Objective:This study aimed to adapt and validate a Social Media Cyberbullying Victimization Scale (SMCVS) and examine its associations with psychological outcomes among Chinese college students. Methods:In Shanghai, China, 1766 students from multiple universities completed questionnaires including demographic information, the SMCVS, the Patient Health Questionnaire-9, and the Generalized Anxiety Disorder-7 scale. The psychometric evaluation of the SMCVS included content validity, construct validity, criterion validity, internal consistency reliability, split-half reliability, and test-retest reliability. Receiver operating characteristic analyses were used to evaluate predictive ability for depression and anxiety. Results:Content validity was satisfactory, and 13 items were retained, with item-level content validity index values ranging from 0.833 to 1 and a scale-level content validity index of 0.949. Exploratory factor analysis identified 2 dimensions-"information harassment" and "reputation and privacy violation"-explaining 82.2% of the total variance. Confirmatory factor analysis supported this structure (goodness-of-fit index=0.943, comparative fit index [CFI]=0.982, normed fit index=0.978, and root mean square error of approximation [RMSEA]=0.075). Multigroup confirmatory factor analysis supported configural and metric invariance across both gender and academic major (ΔCFI≤0.01, ΔRMSEA≤0.015); scalar invariance was achieved for gender but not for academic major. Both dimensions showed significant correlations with depression (r=0.433-0.457) and anxiety (r=0.356-0.372). Cronbach α, Spearman-Brown coefficient, and test-retest reliability coefficient were 0.959, 0.973, and 0.860, respectively. The SMCVS demonstrated moderate discriminative accuracy for depression (area under the curve [AUC]=0.738; optimal cutoff=19.5) and anxiety (AUC=0.739; optimal cutoff=22.5). Conclusions:The SMCVS demonstrates sound psychometric properties and may be useful for assessing social media cyberbullying victimization among Chinese college students. Its validated 2D structure clarifies distinct patterns of victimization and their psychological correlates, offering implications for targeted prevention and intervention.

Background:Young and middle-aged adults are vulnerable to poor sleep quality. eHealth literacy, defined as the ability to effectively access and use digital health information, has been linked to improved health behaviors and may promote better sleep outcomes. However, its relationship with sleep quality remains unclear, especially across age groups. Age-related disparities in eHealth literacy may contribute to a digital health divide in sleep outcomes. Objective:This study aimed to examine the relationship between eHealth literacy and sleep quality among adults aged 18 to 59 years in Shanghai, China, as well as explore age-stratified effects. Methods:A cross-sectional study was conducted between October and December 2022 in 3 districts of Shanghai, with 7 community health service centers randomly selected. Participants were recruited through convenience sampling to complete an online survey. eHealth literacy was assessed using the eHealth Literacy Scale, and sleep quality was measured using the Pittsburgh Sleep Quality Index. Covariates included sociodemographic characteristics, health status, and health behaviors. Logistic regression models were applied to examine the relationship between eHealth literacy and sleep quality, with stratified analyses conducted by age (emerging adults [18-29 years], established adults [30-45 years], and middle-aged adults [46-59 years]). Results:A total of 1810 participants completed the survey. The prevalence of poor sleep quality was 37.9% (686/1810). Participants with eHealth literacy scores in the 25th to 75th percentile range (odds ratio [OR] 1.594, 95% CI 1.216-2.089, P<.001) and below the 25th percentile (OR 1.584, 95% CI 1.149-2.182, P=.005) had a significantly higher likelihood of reporting poor sleep quality compared to those with scores above the 75th percentile. Age-stratified analysis indicated that this association was significant only among emerging adults (OR 2.491, 95% CI 1.133-5.479, P=.02 for scores between the 25th and 75th percentiles; OR 2.975, 95% CI 1.230-7.195, P=.02 for scores below the 25th percentile) and established adults (OR 1.439, 95% CI 1.001-2.067, P=.049 for scores between the 25th and 75th percentiles). Conclusions:This study found that eHealth literacy was associated with sleep quality among younger participants but not middle-aged ones, highlighting the digital divide in sleep health. These findings suggest that enhancing eHealth literacy may serve as an effective strategy for improving sleep outcomes. However, to ensure equitable health outcomes, interventions should be tailored to address the age-specific needs and varying levels of digital access across different groups.

Monkeypox (mpox) has emerged as a global public health concern, particularly among men who have sex with men (MSM). Stigma limits access to care, and the role of social support in shaping care-seeking through psychosocial mechanisms remains unclear. This study examined whether social support influences care-seeking intentions via stigma and perceived healthcare benefits among MSM in China. A cross-sectional study was conducted from October 2023 to March 2024 across 6 provinces in China. Descriptive statistics, chi-square tests, Spearman correlations, and logistic regression were performed to explore associations between HBM-related constructs and healthcare-seeking intentions. Structural equation modeling was used to examine the direct and indirect effects of social support via stigma and perceived healthcare benefits. Among participants, 83.4

AIMS:The triglyceride-glucose (TyG) index is a recognized surrogate marker for insulin resistance. This study explores the relationship between the baseline TyG index and the subsequent risk of stroke in middle-aged and older adults, while also examining the variable importance of various predictors potentially influencing stroke incidence. METHODS:We included 6863 participants from the China Health and Retirement Longitudinal Study (CHARLS) who had no history of stroke at the start of the study. To identify important predictors, we employed the Least Absolute Shrinkage and Selection Operator (Lasso) Cox regression model, followed by a multivariate Cox proportional hazards model to analyze the association between the TyG index and future stroke incidence. Subgroup analyses by age and gender were conducted. The significance of different predictors was assessed using explainable survival machine learning models that accounted for temporal changes. RESULTS:Over a 9-year follow-up, 787 participants (11.5 %) experienced a first stroke. The baseline TyG index had an inverted U-shaped relationship with stroke risk. After adjustment for confounders, participants in the second, third, and highest quartiles of the baseline TyG index showed a higher stroke risk compared to those in the lowest quartile (P < 0.01), with adjusted hazard ratios (HR) [95 % confidence intervals (CI)] of 1.45 (1.16-1.82), 1.64 (1.31-2.04), and 1.36 (1.08-1.72), respectively. These associations were consistent across all subgroups except for individuals younger than 60 years. Notably, age emerged as the most significant predictor of stroke risk in the explainable machine learning analysis, with the TyG index also identified as a relatively important factor. CONCLUSIONS:This research employs explainable machine learning to delineate factors that contribute to stroke risk, highlighting how the TyG index's impact on stroke risk varies by age and gender. As an established surrogate marker for insulin resistance, the TyG index monitoring may play a crucial role in stroke prevention and management strategies.

Cyberbullying victimization is a significant risk factor for poor psychological well-being among college students. Existing tools fail to capture the distinct dimensions of victimization in social media contexts. This study aimed to develop and validate a Social Media Cyberbullying Victimization Scale (SMCVS) and examine its associations with psychological outcomes among Chinese college students. In Shanghai, China, 1,766 students from multiple universities completed questionnaires including demographic information, the SMCVS, the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder scale-7. The psychometric evaluation of the SMCVS included construct and criterion validity, internal consistency, split-half and test-retest reliability. Receiver operating characteristic analyses were used to evaluate predictive ability for depression and anxiety. Exploratory factor analysis identified two dimensions: Information Harassment and Privacy Violation, explaining 82.21% of the total variance. Confirmatory factor analysis supported this structure (goodness-of-fit index = 0.943, comparative fit index = 0.982, normed fit index = 0.978, root mean square error of approximation = 0.075). Both dimensions showed significant correlations with depression (r = 0.433-0.457) and anxiety (r = 0.356-0.372). Cronbach’s alpha, Spearman-Brown coefficient, and test-retest reliability coefficient were 0.959, 0.973, and 0.860, respectively. The SMCVS demonstrated moderate discriminative accuracy for depression (area under the curve [AUC] = 0.738) and anxiety (AUC = 0.739). The SMCVS demonstrates robust psychometric properties and is useful for assessing social media cyberbullying victimization among Chinese college students. Its validated two-dimensional structure clarifies distinct patterns of victimization and their psychological correlates, informing targeted prevention and intervention strategies.

Background:Electronic health literacy (eHL) has been increasingly associated with health-related quality of life (HRQoL). However, the underlying mechanisms, especially in the general population, remain insufficiently explored. Objective:This study aimed to investigate the mediating role of health self-management behaviors (HSMB) in the relationship between eHL and HRQoL. Methods:A cross-sectional study was conducted in Shanghai, China, from October to December 2022. Participants were recruited via convenience sampling from 7 community health service centers. Data were collected through an online survey platform Wenjuanxing. Validated scales, including the eHL Scale, the adults' health self-management skill rating scale, and the 12-item short form health survey were used to measure eHL, HSMB, and HRQoL, respectively. The HRQoL was summarized into the physical component summary (PCS) and the mental component summary (MCS). Correlation analysis, multivariate linear regression with stepwise backward selection, and mediation analysis were performed to explore the relationships among eHL, HSMB, PCS, and MCS, with adjustments for sociodemographic and health-related covariates. Results:Among the 2364 participants recruited from urban, periurban, and rural areas, eHL scores varied significantly by demographic characteristics. Positive correlations among eHL, HSMB, PCS, and MCS were observed, with Spearman correlation coefficients ranging from 0.24 to 0.46 (P<.001). Multivariate analysis showed that eHL was significantly positively associated with PCS (R2=0.14, 95% CI 0.09-0.18, P<.001) and MCS (R2=0.23, 95% CI 0.17-0.28, P<.001). Mediation analysis indicated that eHL had a significant direct (PCS: βc=.18, 95% CI 0.13-0.23, P<.001; MCS: βc=.32, 95% CI 0.25-0.38, P<.001) and an indirect effect on HRQoL through HSMB (PCS: βc'=.11, 95% CI 0.09-0.14, P<.001; MCS: βc'=.14, 95% CI 0.10-0.17, P<.001). Conclusions:This study demonstrated a positive association between eHL and HRQoL, with HSMB acting as a partial mediator among the general population in Shanghai. Targeted interventions should be implemented to improve eHL and HSMB.

Traumatic brain injury (TBI) can lead to secondary brain damage, with post-traumatic neuroinflammation being a crucial indicator of the condition's progression and a predictor of patient prognosis. However, effective, evidence-based pharmacotherapy targeting post-TBI neuroinflammation remains lacking. In our study, we show that the use of the mechanosensitive ion channel inhibitor GsMTx4 effectively alleviated neuronal apoptosis and neuroinflammation, thereby ameliorating abnormal neurological behaviors in mice following TBI. Transcriptomic analysis of the tissue surrounding the injury site indicated downregulation of the extracellular matrix(ECM) degradation and inflammation-related signaling pathways. Complementary metabolomic profiling revealed the metabolic signature and a reduced abundance of metabolites associated with inflammatory responses and ECM degradation after treatment. We speculate that GsMTx4 may modulate various proteases, thereby disrupting the ECM degradation-neuroinflammation feedback loop and ultimately attenuating the progression of neuroinflammation-driven secondary brain damage. Immunostaining and functional assays further confirmed that GsMTx4 treatment preserved ECM-related proteins. These findings suggest that GsMTx4 may offer a promising therapeutic approach for the management of secondary damage following TBI.

BackgroundInternet use exhibits diverse trajectories during adolescence, which may contribute to depressive symptoms. Currently, it remains unclear whether the association between internet use trajectories and depressive symptoms varies between urban and rural areas. ObjectiveThis study aimed to investigate the association between internet use trajectories and adolescent depressive symptoms and to explore variation in this association between urban and rural areas. MethodsThis longitudinal study used 3-wave data from the 2014-2018 China Family Panel Study. Weekly hours of internet use and depressive symptoms were measured using self-reported questionnaires. Latent class growth modeling was performed to identify the trajectories of internet use. Multivariable logistic regressions were used to examine the association between internet use trajectories and depressive symptoms, stratified by rural and urban residence. ResultsParticipants were 2237 adolescents aged 10 to 15 years at baseline (mean age 12.46, SD 1.73 years). Two latent trajectory classes of internet use were identified: the low-growth group (n=2008, 89.8%) and the high-growth group (n=229, 10.2%). The high-growth group was associated with higher odds of depressive symptoms (OR 1.486, 95% CI 1.065-2.076) compared to the low-growth group. In the stratified analysis, the association between internet use trajectories and depressive symptoms was significant solely among rural adolescents (OR 1.856, 95% CI 1.164-2.959). ConclusionsThis study elucidates urban-rural differences in the associations between trajectories of internet use and adolescent depressive symptoms. Our findings underscore the importance of prioritizing interventions for rural adolescents’ internet use behaviors to mitigate negative effects on their mental health.

ObjectiveThe burden of cardiovascular diseases (CVD) is significant, necessitating early prevention, with obesity standing out as a pivotal modifiable risk factor. We aimed to use three prospective aging cohorts to develop an obesity-focused prediction model for incident CVD risk with enhanced validation and explanation.MethodsWe analyzed longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) wave 1-4, Health and Retirement Study (HRS) wave 11-14, and English Longitudinal Study of Ageing (ELSA) wave 6-9. All participants were aged 45 years or older, had no CVD at baseline, and completed follow-up assessments across three subsequent waves. The main outcome was the occurrence of CVD (self-reported physician diagnoses of either heart disease or stroke). The predictors were screened by the Least Absolute Shrinkage and Selection Operator and Random Survival Forest. A multivariate Cox regression analysis was applied to develop the prediction model. Model performance was validated using: (1) concordance index for discrimination, (2) calibration curves for risk accuracy, and (3) time-dependent Receiver Operating Characteristic curves for classification. The time-dependent feature importance plot, partial dependence survival profiles and SHapley Additive exPlanations plot were used to interpret the model.ResultsThe study included 5768 participants from CHARLS, 3151 from HRS and 3016 from ELSA. The CVD incidence rates of CHARLS, HRS and ELSA were 21.2%, 13.2% and 13.5% respectively. Three of the seventeen screened covariates, which were age, hypertension, systolic blood pressure (SBP), as well as body mass index (BMI) and body roundness index (BRI), were included in the prediction model. The model exhibited a valid predictive value and moderate performance, with obesity showing a pronounced effect. BRI demonstrated stronger associations with CVD than BMI in both training and validation cohorts.ConclusionAge, hypertension, SBP, BMI, and BRI were significant predictors of incident CVD in middle-aged and older adults, highlighting the impact of obesity on CVD risk, and consequently offered a valuable model for public health strategies to prevent CVD.

Electronic health literacy (eHL) has been increasingly associated with health-related quality of life (HRQoL). However, the underlying mechanisms, especially in the general population, remain insufficiently explored. This study aimed to investigate the mediating role of health self-management behaviors (HSMB) in the relationship between eHL and HRQoL. A cross-sectional study was conducted in Shanghai, China, from October to December 2022. Validated scales, including the eHealth Literacy Scale (eHEALS), the Adults Health Self-Management Skill Rating Scale (AHSMSRS), and the 12-item Short Form Health Survey (SF-12) were utilized to measure eHL, HSMB, and HRQoL, respectively. The HRQoL was summarized into the Physical Component Summary (PCS) and the Mental Component Summary (MCS). Correlation analysis, multivariate linear regression with stepwise backward selection, and mediation analysis were performed to explore the relationships among eHL, HSMB, PCS, and MCS, with adjustments for sociodemographic and health-related covariates. Among the 2,364 participants recruited from urban, peri-urban, and rural areas, eHL scores varied significantly by demographic characteristics. Positive correlations between eHL, HSMB PCS and MCS were observed, with spearman correlation coefficients ranging from 0.24 to 0.46 (p < 0.001). Multivariate analysis showed that eHL was significantly positively associated with PCS (β=0.14, p<0.001) and MCS (β=0.23, p<0.001). Mediation analysis indicated that eHL had a a significant direct (PCS: ; MCS: ) and an indirect effect on HRQoL through HSMB (PCS: ; MCS: ). This study demonstrated a positive association between eHL and HRQoL, with HSMB acting as a partial mediator within the general population in Shanghai. Targeted interventions should be implemented to improve eHL and HSMB.

Thyroid nodule, as a common clinical endocrine disease, has become increasingly prevalent worldwide. Ultrasound, as the premier method of thyroid imaging, plays an important role in accurately diagnosing and managing thyroid nodules. However, there is a high degree of inter- and intra-observer variability in image interpretation due to the different knowledge and experience of sonographers who have huge ultrasound examination tasks everyday. Artificial intelligence based on computer-aided diagnosis technology maybe improve the accuracy and time efficiency of thyroid nodules diagnosis. This study introduced an artificial intelligence software called SW-TH01/II to evaluate ultrasound image characteristics of thyroid nodules including echogenicity, shape, border, margin, and calcification. We included 225 ultrasound images from two hospitals in Shanghai, respectively. The sonographers and software performed characteristics analysis on the same group of images. We analyzed the consistency of the two results and used the sonographers’ results as the gold standard to evaluate the accuracy of SW-TH01/II. A total of 449 images were included in the statistical analysis. For the seven indicators, the proportions of agreement between SW-TH01/II and sonographers’ analysis results were all greater than 0.8. For the echogenicity (with very hypoechoic), aspect ratio and margin, the kappa coefficient between the two methods were above 0.75 (P < 0.001). The kappa coefficients of echogenicity (echotexture and echogenicity level), border and calcification between the two methods were above 0.6 (P < 0.001). The median time it takes for software and sonographers to interpret an image were 3 (2, 3) seconds and 26.5 (21.17, 34.33) seconds, respectively, and the difference were statistically significant (z = -18.36, P < 0.001). SW-TH01/II has a high degree of accuracy and great time efficiency benefits in judging the characteristics of thyroid nodule. It can provide more objective results and improve the efficiency of ultrasound examination. SW-TH01/II can be used to assist the sonographers in characterizing the thyroid nodule ultrasound images.