Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

ObjectiveTo develop a systematic case management model based on the International Classification of Functioning, Disability and Health (ICF) Vocational Rehabilitation Core Set (VR-CS) and explore its application pathways and efficacy in vocational rehabilitation. The 13 core categories of VR-CS are applied in vocational rehabilitation case management practices, utilizing comprehensive functional assessments to design and implement structured and individualized case management plans. This, in turn, seeks to enhance individuals' vocational independence, social participation and overall quality of life. MethodsThis study adopted a combined approach of theoretical research and empirical case analysis. Based on the 13 core categories of VR-CS, a comprehensive vocational assessment was conducted across three dimensions: body function and structure, activities and participation, and environmental factors. The study theoretically analyzed how to develop and implement holistic and individualized vocational rehabilitation plans, along with dynamic monitoring processes. Three types of typical vocational rehabilitation cases were examined, including individuals with major depressive disorder, high-functioning autism and mild cognitive impairment, to illustrate the evidence and effectiveness of VR-CS-based case management. ResultsThe VR-CS-based case management method, grounded in the ICF bio-psycho-social model, constructed an integrated and individualized case management system. This system combined the assessment and analysis of vocational rehabilitation needs, goal setting and planning, multidisciplinary team intervention, individualized interventions, process monitoring and dynamic adjustments, return-to-work activities, and long-term follow-up services.The case management model developed through VR-CS demonstrated significant and systematic efficacy in promoting vocational independence and social participation. This study provided a detailed analysis of the primary methods and steps involved in designing and implementing case management and elaborates on the practical applications of this model from the three representative cases. At the body function and structure level, case management included the evaluation of energy and drive functions (b130), high-level cognitive training (b164), and the enhancement of exercise tolerance (b455). Case management focused on accurately setting short- and long-term goals around functional limitations and needs. Dynamic monitoring during interventions ensured timely adjustments to intervention measures. This process improved physical endurance and cognitive function, enhanced sustained work capacity in complex vocational environments. At the activities and participation level, the focus was on skill acquisition (d155), stress management and psychological adjustment (d240), and the cultivation of complex interpersonal communication abilities (d720). Individuals engaged in regular professional skills training, role-playing and simulated work scenarios to progressively enhance vocational adaptability and social interaction skills. Through this process, individuals acquired practical skills, gradually boosting self-efficacy and vocational confidence, thereby facilitating the reshaping of their social roles. At the environmental and personal factors level, case management pathways involved active participation from immediate family (e310), positive shifts in societal attitudes (e460), and the effective utilization of health and employment policy resources (e580, e590). By integrating social and community resources comprehensively, the program created a more inclusive and supportive rehabilitation environment. By designing and implementing systematic case management programs, individuals showed significant improvements in psychological well-being, vocational adaptability and social interaction skills. The majority of cases achieved stable employment or continued participation in social activities following the intervention. ConclusionVR-CS provides a systematic, multidimensional and person-centered case management framework. This framework applies the bio-psycho-social model, analyzing individuals' primary functional impairments and vocational rehabilitation needs from the perspectives of body functions, psychological states and environmental factors. On this basis, a comprehensive, targeted and holistic case management plan is developed and implemented. The VR-CS-based case management approach not only enhances vocational capabilities but also improves overall quality of life and social adaptability.

Xanthine oxidase (XO) is an important therapeutic target for the treatment of hyperuricemia and gout. Based on the previously identified potent XO inhibitor 1, seventeen oxadiazoles and their ring-opening analogues were designed and synthesized via the bioisostere replacement strategy. Among them, compounds 2l, 2n, and 3b showed obvious XO inhibitory activity at the concentration of 10 μmol·L^-1, and compound 3b exhibited an IC₅₀ value of 1.45 μmol·L^-1.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Objective: To understand the current situation and influencing factors of comorbidity of depressive and anxiety symptoms among middle school students in Chongqing, so as to provide a scientific basis for formulating a comprehensive strategy for the co prevention of multiple diseases among middle school students. Methods: From September to December 2024, 12 327 middle school students were selected from 6 districts and counties in Chongqing by the combination of stratified cluster sampling and convenience sampling method. The current status of depressive and anxiety symptoms was investigated by using the Center for Epidemiological Survey-Depression Scale (CES-D) and the Generalized Anxiety Disorder-7 (GAD-7). The Chi-squared test was used to compare the differences between groups with comorbidity of depressive and anxiety symptoms, multivariate Logistic regression analysis was used to analyze its related factors, and a nomogram prediction model was drawn. Results: The detection rates of depressive symptoms, anxiety symptoms and comorbidity among middle school students in Chongqing were 26.34%, 34.55% and 21.16%, respectively. Among them, the detection rates of the three types of symptoms in girls (29.80%, 40.99%, 25.15%) were all higher than those in boys (23.22%, 28.73%, 17.55%) ( χ 2=68.61, 204.23, 106.51, all P <0.01). Statistical significance was observed in the distribution of depressive and anxious symptoms among middle school students across different gender, academic stage, school district, family type, physical activity levels, parental discipline, smoking, alcohol consumption, sleep deprivation, excessive screen time, Internet addiction, and bullying ( χ 2=14.49-991.46, all P <0.01). Multivariate Logistic regression analysis showed that compared with junior high school students, ordinary high school students had a higher risk of comorbidity ( OR=2.71, 95% CI = 2.41-3.05); girls ( OR=2.17, 95%CI =1.95-2.40), non-core family ( OR=1.20, 95%CI =1.08-1.32), and good neighborhood ( OR=1.16, 95%CI =1.02-1.30), campus bullying ( OR=4.88, 95%CI =4.32-5.50), Internet addiction ( OR=4.77, 95%CI = 3.41 -6.68), parental beating and scolding ( OR=3.18, 95%CI =2.72-3.71), alcohol consumption ( OR=2.10, 95%CI =1.86- 2.37 ), and insufficient sleep ( OR=1.73, 95%CI =1.54-1.95) had higher risks with comorbidity of depression and anxiety symptoms (all P <0.05). A nomogram prediction model was constructed based on significant variables shows that C-index=0.75 (AUC= 0.75 , 95% CI=0.74-0.76, P <0.05), and the model had good predictive performance. Conclusions The current situation of comorbidity of depressive and anxiety symptoms among middle school students in Chongqing is not optimistic. The nomograms can be used to effectively predict the risk of comorbidity of depressive and anxiety symptoms in middle school students.

Thymocyte selection-associated high mobility group box (TOX), a member of the high mobility group protein super-family, plays an important role in T cell development, functional maintenance, and exhaustion. It has been recently found that TOX exerts critical immunoregulatory functions during pathogen infections, and TOX expression is strongly associated with the intensity and tolerance of host immune responses. This review systematically summarizes the structural and functional features of TOX and focuses on its expression dynamics, mechanisms of action, and immunomodulatory effects during viral, bacterial, and parasitic infections, which provides a theoretical support to better understanding of the role of TOX in infectious diseases and provides new insights into development of potential immunotherapeutic strategies targeting TOX.

Background::Carotid intima-media thickness (IMT) and diameter, stiffness, and wave reflections, are independent and important clinical biomarkers and risk predictors for cardiovascular diseases. The purpose of the present study was to establish nationwide reference values of carotid properties for healthy Chinese adults and to explore potential clinical determinants.Methods::A total of 3053 healthy Han Chinese adults (1922 women) aged 18-79 years were enrolled at 28 collaborating tertiary centers throughout China between April 2021 and July 2022. The real-time tracking of common carotid artery walls was achieved by the radio frequency (RF) ultrasound system. The IMT, diameter, compliance coefficient, β stiffness, local pulse wave velocity (PWV), local systolic blood pressure, augmented pressure (AP), and augmentation index (AIx) were then automatically measured and reported. Data were stratified by age groups and sex. The relationships between age and carotid property parameters were analyzed by Jonckheere-Terpstra test and simple linear regressions. The major clinical determinants of carotid properties were identified by Pearson’s correlation, multiple linear regression, and analyses of covariance.Results::All the parameters of carotid properties demonstrated significantly age-related trajectories. Women showed thinner IMT, smaller carotid diameter, larger AP, and AIx than men. The β stiffness and PWV were significantly higher in men than women before forties, but the differences reversed after that. The increase rate of carotid IMT (5.5 μm/year in women and 5.8 μm/year in men) and diameter (0.03 mm/year in both men and women) were similar between men and women. For the stiffness and wave reflections, women showed significantly larger age-related variations than men as demonstrated by steeper regression slopes (all P for age by sex interaction <0.05). The blood pressures, body mass index (BMI), and triglyceride levels were identified as major clinical determinants of carotid properties with adjustment of age and sex. Conclusions::The age- and sex-specific reference values of carotid properties measured by RF ultrasound for healthy Chinese adults were established. The blood pressures, BMI, and triglyceride levels should be considered for clinical application of corresponding reference values.