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.

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.

Myocardial fibrosis is a serious cause of heart failure and even sudden cardiac death. However, the mechanisms underlying myocardial ischemia-induced cardiac fibrosis remain unclear. Here, we identified that the expression of sterile alpha and TIR motif containing 1 (SARM1), was increased significantly in the ischemic cardiomyopathy patients, dilated cardiomyopathy patients (GSE116250) and fibrotic heart tissues of mice. Additionally, inhibition or knockdown of SARM1 can improve myocardial fibrosis and cardiac function of myocardial infarction (MI) mice. Moreover, SARM1 fibroblasts-specific knock-in mice had increased deposition of extracellular matrix and impaired cardiac function. Mechanically, elevated expression of SARM1 promotes the deposition of extracellular matrix by directly modulating P4HA1. Notably, by using the Click-iT reaction, we identified that the increased expression of ZDHHC17 promotes the palmitoylation levels of SARM1, thereby accelerating the fibrosis process. Based on the fibrosis-promoting effect of SARM1, we screened several drugs with anti-myocardial fibrosis activity. In conclusion, we have unveiled that palmitoylated SARM1 targeting P4HA1 promotes collagen deposition and myocardial fibrosis. Inhibition of SARM1 is a potential strategy for the treatment of myocardial fibrosis. The sites where SARM1 interacts with P4HA1 and the palmitoylation modification sites of SARM1 may be the active targets for anti-fibrosis drugs.

Objective The aim of this study was to investigate the potential impact of facial heated steam treatment on the Demodex mite detection rate.Methods Overall,215 patients to suspected demodicidosis who visited the dermatology outpatient department of a tertiary hospital in Lanzhou between May and July 2023 were selected as study subjects.The facial skin scraping method was employed following no pretreatment or heated steam treatment for 10 min to detect facial Demodex.Results The Demodex mite detection rate was significantly(χ2=54.07,P=0.00)higher with heated steam facial treatment(63.7%,137/215)compared to no pretreatment(28.4%,61/215).Conclusions The results indicate a statistically significant difference in the detection rate of human Demodex mites between patients who received facial heated steam treatment and those who did not.These findings provide a novel reference for experimental diagnostic methods of demodicosis.

Objective To investigate the current demand for extended nursing services for newborns after discharge.Methods A survey was conducted on the demand for extended nursing services after discharge of newborns using the"Survey Form for Demand for Extended Nursing Services after Discharge of Newborns",and the results were analyzed.Results 62.30％of the data for this survey was provided by the father,possibly due to a Chinese tradition where mothers were confined at home during confinement after their child's discharge,making it inconvenient for them to go out.The educational level of parents was mainly undergraduate or above,with an age of 30.55±5.07 years old,mainly middle-aged and young;The top 5 scores for continuity of care content requirements were:reminder for follow-up visits(4.39±0.08)points,discharge medication guidance(4.35±0.92)points,health assessment(4.29±0.94)points,disease-related knowledge education(4.28±0.95)points,specialized nursing(4.17±1.05)points and basic nursing guidance(4.17±1.04)points;The top four scores of parents'demands for extended nursing channels were:telephone follow-up 2673(85.45％),discharge education 2529(80.85％),community two-way referral 2390(76.41％),and health education network platform 2271(72.60％)(such as WeChat official account,forum,Tiktok live broadcast);Family members hope that 2340 doctors(74.81％)and 1 764 hospital nurses(56.39％)were still the main executors of extended services provided by the hospital;After discharge,if necessary due to the condition,medical staff would come to your doorstep to provide corresponding medical care services.Patients agreed to pay 2759 cases(88.20％)according to the price standard,with the majority agreeing to pay 2 189 cases(69.98％)for consultation fees,2057 cases(65.76％)for material fees and 1 997 cases(63.84％)for diagnosis,treatment,and nursing fees.Conclusion Providing continuous extended care services during and after the discharge of newborns can reduce their readmission rate,promote physical development,and reduce complications.With the development of medical technology and information technology,parents of newborns have an increasing demand for extended care services,and it is imperative to carry out extended care services.

Objective:This paper examines the access control mechanisms of a big data platform and explores its integration with the ChatGPT artificial intelligence platform for nursing management. The aim was to pilot a self-monitoring and follow-up big data platform for valve disease patients in the Northeastern region of China and assess its effectiveness, providing healthcare professionals with a more practical follow-up tool.Methods:Convenience sampling was used to select 32 patients who underwent mechanical valve replacement surgery or postoperative follow-up at the affiliated hospital of North Sichuan Medical College between January and October 2022 by a retrospective study, were taking oral warfarin anticoagulant therapy, and were willing to use the platform. Based on their platform usage data from November to December 2022, the 32 patients were divided into two groups according to their INR compliance rates: a high compliance group (16 patients) and a low compliance group (16 patients). Evaluate the operational effectiveness of the platform and its impact on patient anticoagulation efficacy based on its usage frequency and INR value compliance rate.Results:The number of login times and INR values written by patients in the high-standard-rate group were (11.31 ± 3.38) and (7.00 ± 1.63) times respectively, which were higher than those in the low-standard-rate group (9.44 ± 3.39) and (6.06 ± 1.88) times, the difference were not statistically significant (all P>0.05). The number of INR values written within the normal range and the number of occurrences of warning values by patients in the high-standard-rate group were (6.38 ± 1.50) and 1.00(0, 2.00) times, which were different than that in the low-standard-rate group (4.05 ± 1.57) and 2.00(2.00, 3.50) times, the differences were statistically significant ( t = 4.26, Z = - 2.22, P<0.05). Conclusions:The self-monitoring and follow-up big data platform for patients after artificial mechanical valve replacement equipped with ChatGPT can optimize and standardize the nursing follow-up workflow, improve nursing work efficiency, reduce the workload of medical staff. At the same time, it provides a better self-management platform for patients after artificial mechanical valve replacement. Assist patients in monitoring INR values and predicting possible changes in their condition, providing corresponding warnings and recommendations helps patients better participate in self-anticoagulation management, and improves the quality of life of patients.