Aim To identify the underlying target of bullatine A(BA)against rheumatoid arthritis(RA)u-sing limited proteolysis-small molecule mapping(LiP-SMap)drug target proteomics and to provide a scientif-ic basis for clinical application of Aconiti brachypodi Radix in the treatment of RA.Methods LiP-SMap drug target proteomics was employed to perform bioin-formatics analysis for comparing and validating the dif-ferential protein expression after BA intervention.A collagen-induced arthritis(CIA)model was estab-lished in DBA/1 mice using bovine type Ⅱ collagen.The mice were then divided into the CIA model group,methotrexate-positive control group(MTX group),and BA groups(10 mg·kg-1 and 20 mg·kg-1)based on their clinical scores.After drug intervention,the thera-peutic efficacy against RA was assessed by joint index scores and foot thickness measurements.Histopatholog-ical changes in the arthritic joints of CIA mice were e-valuated using hematoxylin and eosin(HE)staining.Enzyme-linked immunosorbent assay(ELISA)was employed to detect inflammatory cytokines interleukin-17(IL-17)and total IgG and IgG3 anti-collagen-spe-cific antibodies levels from the serum of CIA mice.Flow cytometry was used to detect the expression levels of intracellular Th17 cells(IL-17+CD4+T cells)and Th1 cells(IFN-γ+CD4+T cells).Fluorescent quanti-tative PCR was performed to detect the expression of genes related to differential proteins.Results The proteomic analysis identified Serpinb1a as a protein with strong binding affinity to BA,and KEGG enrich-ment analysis indicated IL-17 signaling pathway was a crucial pathway of BA in against RA.BA treatment significantly reduced clinical scores and foot thickness,improved local arthritis symptoms in CIA mice,and al-leviated inflammatory cell infiltration into arthritic joints(P＜0.05).Differential protein validation re-sults showed that BA had strong affinity with Serpinb1a(-5.92 kJ·mol-1)and downregulated the expres-sion of Serpinb1a mRNA.Furthermore,the administra-tion of BA markedly reduced serum IL-17 A levels from CIA mice,inhibited the expression of intracellular IL-17 A and IFN-γ cytokines in splenic CD4+T cells(P＜0.05),and significantly downregulated the transcrip-tional expression of IL-17F(P＜0.05).Conclusion BA exhibits therapeutic effects on collagen-induced arthritis,and its mechanism of action may involve the regulation of Serpinb1a and the IL-17 signaling path-way.

Objective:To investigate the prevalence of scoliosis among middle school students through school-based screening, and to analyze the correlations between axial trunk rotation (ATR) angle and Cobb angle among those with scoliosis considering gender, age, body mass index (BMI) and curve type, so as to provide references for early prevention and treatment.Methods:Random cluster sampling was used to enroll 9501 middle school students from 11 schools in the Hanyang District of Wuhan. They underwent a six-step scoliosis screening with a forward bending test, scoliometer measurements, and confirmatory X-ray examinations. The students exhibiting scoliosis were stratified by gender, age, BMI, and curve type. The correlation between ATR angle and Cobb angle was subsequently analyzed within each subgroup.Results:Scoliosis was observed in 358 of the students (150 boys and 208 girls), a positive screening rate of 3.8%. Moreover, the positive screening rate was significantly higher among the girls (4.8%) than among the boys (2.9%). There was a moderate Spearman correlation ( r=0.69) between the ATR and Cobb angles in 358 of the students with scoliosis, both the boys ( r=0.69) and the girls ( r=0.70). When it came to age, there was a positive correlation between the ATR and Cobb angles for students aged 13 to 16 irrespective of their BMI classification, except that there was no significant correlation between the two angles for those classified as obese. In terms of Peking Union Medical College (PUMC) classification, there was a positive correlation for those in types Ia, Ib, Ic, IIa, IIb, IIc and IId, but there was no significant correlation between the two angles for those in type III. Conclusions:Scoliosis is prevalent among the group of adolescents studied. Society, schools and parents need to prioritize prevention, heightened awareness, timely diagnosis and early treatment, particularly for girls. The ATR angle may serve as a practical surrogate for estimating Cobb angle severity in scoliosis screening for single-curve or double-curve cases. However, ATR measurements may yield false-negative results among those with triple-curve deformities or the obese, underscoring the need for supplementary diagnostic methods among such populations.

Objective:To investigate the prevalence of scoliosis among middle school students through school-based screening, and to analyze the correlations between axial trunk rotation (ATR) angle and Cobb angle among those with scoliosis considering gender, age, body mass index (BMI) and curve type, so as to provide references for early prevention and treatment.Methods:Random cluster sampling was used to enroll 9501 middle school students from 11 schools in the Hanyang District of Wuhan. They underwent a six-step scoliosis screening with a forward bending test, scoliometer measurements, and confirmatory X-ray examinations. The students exhibiting scoliosis were stratified by gender, age, BMI, and curve type. The correlation between ATR angle and Cobb angle was subsequently analyzed within each subgroup.Results:Scoliosis was observed in 358 of the students (150 boys and 208 girls), a positive screening rate of 3.8%. Moreover, the positive screening rate was significantly higher among the girls (4.8%) than among the boys (2.9%). There was a moderate Spearman correlation ( r=0.69) between the ATR and Cobb angles in 358 of the students with scoliosis, both the boys ( r=0.69) and the girls ( r=0.70). When it came to age, there was a positive correlation between the ATR and Cobb angles for students aged 13 to 16 irrespective of their BMI classification, except that there was no significant correlation between the two angles for those classified as obese. In terms of Peking Union Medical College (PUMC) classification, there was a positive correlation for those in types Ia, Ib, Ic, IIa, IIb, IIc and IId, but there was no significant correlation between the two angles for those in type III. Conclusions:Scoliosis is prevalent among the group of adolescents studied. Society, schools and parents need to prioritize prevention, heightened awareness, timely diagnosis and early treatment, particularly for girls. The ATR angle may serve as a practical surrogate for estimating Cobb angle severity in scoliosis screening for single-curve or double-curve cases. However, ATR measurements may yield false-negative results among those with triple-curve deformities or the obese, underscoring the need for supplementary diagnostic methods among such populations.

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.

Objective To construct a nursing follow-up checklist for patients undergoing autologous hematopoietic stem cell transplantation,providing a basis for postoperative follow-up care.Methods Using evidence-based methods,the literature from major guide websites and databases using Chinese and English search terms was retrieved,and their quality was evaluated.The relevant items were extracted,and a first draft was formed.15 experts were selected in relevant fields from 14 tertiary hospitals in 13 provinces,cities,and autonomous regions across the country for Delphi inquiry.The nursing follow-up checklist was revised again based on expert opinions and clinical practice.The nursing follow-up checklist was initially applied and then revised again to form the final draft.Results 15 experts include 12 undergraduate and 3 master's degree holders.The positivity coefficients of the 2 rounds of inquiry were 100％;the authority coefficients of the experts were 0.815;the Kendall coefficients were 0.119 and 0.144,respectively;the differences were statistically significant(P＜0.001).The final nursing follow-up checklist was formed,which includes 6 primary indicators,including physiological status,psychological status,social and family support,living conditions,disease knowledge,and laboratory tests.19 patients(95％)found the follow-up content to be comprehensive.The follow-up nurses's satisfaction rate exceeded 85％.There were 27 secondary indicators and 61 tertiary indicators,with coefficients of variation of all indicators less than 0.25.Conclusion The nursing follow-up checklist is scientific,reliable,and practical,which can provide a basis for clinical nursing staff to follow up and comprehensively manage patients after autologous hematopoietic stem cell transplantation.

Objective To construct a nursing follow-up checklist for patients undergoing autologous hematopoietic stem cell transplantation,providing a basis for postoperative follow-up care.Methods Using evidence-based methods,the literature from major guide websites and databases using Chinese and English search terms was retrieved,and their quality was evaluated.The relevant items were extracted,and a first draft was formed.15 experts were selected in relevant fields from 14 tertiary hospitals in 13 provinces,cities,and autonomous regions across the country for Delphi inquiry.The nursing follow-up checklist was revised again based on expert opinions and clinical practice.The nursing follow-up checklist was initially applied and then revised again to form the final draft.Results 15 experts include 12 undergraduate and 3 master's degree holders.The positivity coefficients of the 2 rounds of inquiry were 100％;the authority coefficients of the experts were 0.815;the Kendall coefficients were 0.119 and 0.144,respectively;the differences were statistically significant(P＜0.001).The final nursing follow-up checklist was formed,which includes 6 primary indicators,including physiological status,psychological status,social and family support,living conditions,disease knowledge,and laboratory tests.19 patients(95％)found the follow-up content to be comprehensive.The follow-up nurses's satisfaction rate exceeded 85％.There were 27 secondary indicators and 61 tertiary indicators,with coefficients of variation of all indicators less than 0.25.Conclusion The nursing follow-up checklist is scientific,reliable,and practical,which can provide a basis for clinical nursing staff to follow up and comprehensively manage patients after autologous hematopoietic stem cell transplantation.

Objective:To develop an ultrasound multi-view fusion recognition model and evaluate its clinical value in diagnosing fetal conotruncal defects (CTD).Methods:This prospective study collected cardiac ultrasound images from fetuses at 20-32 weeks of gestation undergoing prenatal ultrasound at Dongguan Maternal and Child Health Hospital between September 2022 and May 2024. The case group comprised fetuses diagnosed with CTD, while controls with normal cardiac structures were collected at a 1∶2 ratio. Both groups were divided into modeling training and validation sets at a 3∶1 ratio. One optimal standard image each from the four-chamber view, left ventricular outflow tract view, right ventricular outflow tract view, and three vessels and trachea view was included per fetus. A deep learning-based multi-view fusion recognition model was developed to differentiate normal conotruncal anatomy from CTD. Model performance was validated against post-abortion pathology or postnatal echocardiography results. SAS software was used for statistical analysis to calculate the sensitivity and specificity of three fusion models (based on positivity in any two, three, or four views, and were designated as Fusion Model 1, Fusion Model 2, and Fusion Model 3, respectively), with the optimal model determined by the maximum Youden index. Senior, intermediate, and junior prenatal sonologists independently diagnosed cases in the validation set under blinding conditions. Their diagnostic results were compared with those of the optimal model. Paired Chi-square test (Cochran's Q test) was employed to compare the differences between the diagnostic accuracy rates of sonologists at different experience levels and the sensitivity of the optimal model, thereby analyzing the auxiliary diagnostic value of the multi-view fusion recognition model. Results:The study included 88 CTD cases, excluding six cases (non-CTD diagnosed by post-abortion pathology or postnatal echocardiography or poor image quality), divided into 60 training and 22 validation cases (12 tetralogy of Fallot, four double outlet right ventricle, three transposition of great arteries, three persistent truncus arteriosus). The control group included 176 cases, excluding 15 cases (other cardiac abnormalities confirmed postnatally or poor image quality after re-evaluation), divided into 120 training and 41 validation cases. The sensitivities of Fusion Model 1, Fusion Model 2, and Fusion Mudel 3 were 0.86, 0.64, and 0.27, while their specificities were 0.76, 0.95, and 1.00, respectively. Fusion Model 1 demonstrated the highest Youden index (0.62) and was selected as optimal. Its diagnostic sensitivity showed no significant difference from senior sonologists [86% vs. 91% (20/22), Bonferroni-corrected P>0.999], but was significantly higher than intermediate [55% (12/22), Bonferroni-corrected P=0.049] and junior sonologists [32% (7/22), Bonferroni-corrected P=0.003]. Conclusion:The deep learning multi-view fusion model achieved diagnostic performance comparable to senior sonologists, demonstrating potential value in assisting CTD diagnosis, training less experienced sonologists, and supporting research and education.