Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

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: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

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: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective: To analyze the drug resistance of multidrug-resistant organism (MDRO) among hospitalized children in a children's hospital in Hebei Province from 2019 to 2023, so as to provide the basis for the rational clinical application of antibacterial drugs. Methods: Specimens including sputum, blood, urine, pus, bronchoalveolar lavage fluid, secretions, pleural fluid, and peritoneal fluid of hospitalized children from January 2019 to December 2023 were collected. Pathogen identification and drug susceptibility tests were performed on methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum β-lactamase-producing Escherichia coli (ESBLs-EC), extended-spectrum β-lactamase-producing Klebsiella pneumoniae (ESBLs-KP), carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Acinetobacter baumannii (CRAB), carbapenem-resistant Pseudomonas aeruginosa (CRPA) and carbapenem-resistant Escherichia coli (CREC). The department distribution, specimen distribution, and drug resistance of MDROs were analyzed. Results: A total of 279 086 samples were submitted for testing, with 3 512 MDROs detected. Among these, MRSA and ESBLs-EC had relatively high detection rates of 35.76% and 41.50%, respectively. In the internal medicine pediatric patients, 1 869 MDROs were detected, accounting for 53.22%. The main departments were respiratory medicine, neonatology, and intensive care. In the surgical department, 1 643 MDROs were detected, accounting for 46.78%, with the main sources being general surgery and cardiac surgery. The highest numbers of MDROs were detected in sputum, pus, and urine samples, with 1 372, 527, and 494 isolates, representing 39.07%, 15.01%, and 14.07%, respectively. The resistance rates of MRSA to penicillin, oxacillin, and erythromycin were between 81.76% and 100.00%. ESBLs-EC and ESBLs-KP had a resistance rate of 100.00% to ceftriaxone. CRKP had a resistance rate of 100.00% to ampicillin/sulbactam and imipenem. CRAB had a resistance rate of 100.00% to cefoxitin, imipenem, and meropenem. CRPA had a resistance rate of 100.00% to ampicillin/sulbactam, ceftriaxone, cefoxitin, and imipenem. CREC had a resistance rate of 100.00% to imipenem. Conclusions In a children's hospital in Hebei Province, infections with MDROs among hospitalized pediatric patients are primarily caused by MRSA and ESBLs-EC. These infections are mainly distributed in the departments of respiratory medicine, neonatology, intensive care, general surgery, and cardiac surgery, with the highest detection rates in sputum, pus, and urine samples. Additionally, MRSA, ESBLs-EC, ESBLs-KP, CRKP, CRAB, CRPA, and CREC show high resistance rate to most antimicrobial agents.

Objective To analyze the monitoring data of cardio-cerebrovascular diseases prevention and control system in Yichang in 2022, and to provide data support and experience for the precise prevention and treatment of cardio-cerebrovascular diseases. Methods Acute cardiovascular and cerebrovascular event data were collected from the Yichang Cardio-cerebrovascular Events Monitoring System from January 1, 2022 to December 31, 2022. Descriptive analysis was conducted for the data collected. Statistical analysis was performed using SPSS 20.0 software, and a chi-square test was used to analyze the count data. Results A total of 37,217 cases of cardio-cerebrovascular events were monitored in Yichang in 2022. The crude incidence and the standardized incidence were 983.84/100,000 and 541.55/100,000, respectively. The incidence in males was higher than females (554.93/100,000 vs 428.91/100,000，χ² =464.52，P＜0.05). The top three diseases were cerebral infarction, acute myocardial infarction, and cerebral hemorrhage. The incidence of events increased with age, and 79.80% of the cases were over 60 years old. The main onset time was from May to August. Conclusion The use of the cardio-cerebrovascular events monitoring system in Yichang and the implementation of ^“mandatory reporting card^” monitoring can timely obtain the epidemic characteristics of the diseases, provide support for the precise formulation of prevention and control strategies and measures, reduce underreporting rates, and improve the monitoring system, which is worthy of reference and promotion.

Objective:To explore the relationship among sleep phenotypes, attention deficit and hyperactivity impulsivity (ADHD) symptoms and cognitive information processing in children with ADHD.Methods:A total of 244 children with ADHD aged 6-12 were selected from December 2021 to December 2022.Swanson, Nolan and Pelham rating scale Ⅳ(SNAP-Ⅳ) was used to evaluate the core symptoms of ADHD.Sleep disturbance scale for children (SDSC) was used to evaluate six sleep phenotypes((disorders in initiating and maintaining sleep (DIMS), sleep breathing disorders(SDB), sleep-wake transition disorders(SWTD), disorders of arousal(DA), disorders of excessive somnolence (DOES), and Nocturnal hyperhidrosis(SHY)). Das-Naglieri cognitive function assessment system (DN-CAS) was used to evaluate the cognitive information processing (planning, simultaneous processing, attention and successive processing). Descriptive statistical analysis, Spearman correlation analysis, and mediation analysis were conducted by SPSSAU 23.0 and Zstats software, respectively.Results:Correlation analysis showed that DIMS, SDB, SWTD, DA and DOES were significantly and positively correlated with attention deficit ( r=0.190-0.349, all P<0.01).DIMS(2.14(1.71, 2.57)), SWTD(1.67(1.33, 2.00)) and SHY(2.00(1.50, 3.00)) were significantly positively correlated with hyperactive impulsivity (1.44(1.00, 2.00))( r=0.193, 0.242, 0.133, P<0.05). Attention deficit(1.78(1.44, 2.33)) was significantly and negatively correlated with successive processing(105.00(96.00, 112.00)) ( r=-0.127, P<0.05). The results of multiple linear regression analysis showed that after controlling for sex, age, verbal IQ and operational IQ, DIMS ( β=0.152, P<0.05) and SWTD ( β=0.178, P<0.05) had significant positive predictive effects on hyperactive impulsivity symptoms. DOES ( β=0.259, P<0.01) had significant positive predictive effects on attention deficit symptoms. Attention deficit ( β=-0.183, P<0.05) had a significant negative predictive effect on successive processing. Mediation effect analysis showed that attention deficit played a complete mediating role between DOES and successive processing(effect=-0.179, Bootstrap 95% CI=-0.196--0.110). Conclusion:Different sleep phenotypes are associated with ADHD core symptoms and different dimensions of cognitive information processing processes. DOES indirectly affects successive processing capability by attention deficit symptoms.