Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

To explore the application of sequential analysis in post-market safety dynamic surveillance of vaccines. Under the dynamic monitoring data of vaccines post-market approval, this research introduces the fundamental principles of maximizing sequential probability ratio test (MaxSPRT) and Bayesian sequential analysis, employing R software. Through an example of dynamic safety monitoring data of vaccines post-market approval, we analyze using the MaxSPRT and Bayesian sequential analysis. The MaxSPRT identified a safety signal in week 4 ( P<0.05), while Bayesian sequential analysis indicated that the 95% highest density interval for the RR value at week 4 is 1.13-3.27, suggesting the first appearance of a safety signal at week 4. The MaxSPRT and Bayesian sequential analysis effectively leverage continuously accumulating dynamic monitoring data, thereby serving as a valuable method for post-market safety surveillance of vaccines.

Objective:To investigate the impact of preconception body mass index (BMI) on neonatal birth weight and the risk of macrosomia in pregnant women across various age groups.Methods:A cohort study was conducted, selecting pregnant women who underwent their initial prenatal assessment at Beijing Obstetrics and Gynecology Hospital from September 1st, 2018 to March 31st, 2020. Relevant data were collected from the hospital′s electronic medical record system. Logistic regression nested cubic spline was used to analyze the nonlinear association between preconception BMI and neonatal birth weight. Binary logistic regression was also employed to assess the association between preconception BMI and macrosomia risk.Results:(1) A total of 13 015 pregnant women were examined, revealing an incidence of macrosomia of 6.33% (824/13 015). The preconception BMI of pregnant women in the macrosomia group was significantly higher than that in the non-macrosomia group [(23.1±3.4) vs (21.6±3.1) kg/m 2], and the age was significantly higher than that in the non-macrosomia group [(32.1±3.6) vs (31.7±3.7) years], the differences were statistically significant (all P<0.05). (2) Preconception BMI was positively correlated with neonatal birth weight. Pregnant women with preconception BMI of 15.0 kg/m 2, 20.0 kg/m 2, and 25.0 kg/m 2 had decreased birth weight of 121 g (95% CI: 35-183 g) and increased birth weights of 78 g (95% CI: 54-102 g) and 182 g (95% CI: 151-213 g), respectively, compared to those with a preconception BMI of 18.0 kg/m 2. (3) For each 1.0 kg/m 2 increase in preconception BMI, the risk of macrosomia increased by 14% ( OR=1.14, 95% CI: 1.11-1.16; P<0.001). When stratified by age, it was observed that elevated preconception BMI significantly increased the incidence of macrosomia in women aged 27-38 years. Among them, the risk of delivering macrosomia among 37 years old pregnant women was most affected by preconception BMI ( OR=1.33, 95% CI: 1.17-1.51; P<0.001). (4) The stability and sensitivity analysis results showed that the preconception BMI of pregnant women with a preconception BMI of 18.0-<25.0 kg/m 2 had a significant impact on the risk of macrosomia ( OR=1.23, 95% CI: 1.17-1.29; P<0.001), while the preconception BMI of other preconception BMI stratification pregnant women had no significant impact on the risk of macrosomia (all P>0.05). Hypertension disorders in pregnancy, gestational diabetes mellitus and abnormal blood lipid during pregnancy were not the mediators associated with preconception BMI and macrosomia. After excluding three factors respectively, the impact of preconception BMI on the risk of macrosomia was the same as before ( OR=1.14, 95% CI: 1.11-1.16; P<0.001). Conclusions:Preconception BMI is linked to neonatal birth weight and the risk of macrosomia, which is influenced by the pregnant woman′s age. Both factors should be considered when evaluating the risk of macrosomia in clinical practice.

AIM:To evaluate the application effec-tiveness of a Bayesian mixture model based on the principal stratum strategy for estimating the com-plier average causal effect(CACE)in clinical trials with non-compliance.METHODS:Using a non-infe-riority randomized controlled trial investigating a novel drug for primary type 2 diabetes mellitus(non-inferiority margin:-0.4)as a case study,the primary analysis applied a Bayesian mixture model under the monotonicity assumption to estimate CACE of between-group differences in glycated he-moglobin(HbA1c)changes within the compliant stratum,followed by non-inferiority testing.Sensi-tivity analyses included a Bayesian mixture model relaxing the monotonicity assumption and compar-ing results with per-protocol set(PPS)analysis.RE-SULTS:In the primary analysis,the posterior mean of CACE for HbA1c change in the compliant stratum was 0.081％,with a one-sided 97.5％credible inter-val lower bound of-0.124,exceeding the non-infe-riority margin(-0.4％),supporting the non-inferiori-ty efficacy of the novel drug in the compliant stra-tum(P(H1|Data)=1).Consistent findings were ob-served in PPS analyses(estimated effect:0.136％;one-sided 97.5％credible interval lower bound:-0.069％),further validating methodological robust-ness.CONCLUSION:In clinical trials with noncom-pliance as an intercurrent event,the Bayesian mix-ture model under the principal stratum strategy ef-fectively adjusts for compliance-related bias and yields conservative,robust estimates of causal ef-fects,supporting its value in efficacy evaluation un-der complex compliance scenarios.

To explore the application of sequential analysis in post-market safety dynamic surveillance of vaccines. Under the dynamic monitoring data of vaccines post-market approval, this research introduces the fundamental principles of maximizing sequential probability ratio test (MaxSPRT) and Bayesian sequential analysis, employing R software. Through an example of dynamic safety monitoring data of vaccines post-market approval, we analyze using the MaxSPRT and Bayesian sequential analysis. The MaxSPRT identified a safety signal in week 4 ( P<0.05), while Bayesian sequential analysis indicated that the 95% highest density interval for the RR value at week 4 is 1.13-3.27, suggesting the first appearance of a safety signal at week 4. The MaxSPRT and Bayesian sequential analysis effectively leverage continuously accumulating dynamic monitoring data, thereby serving as a valuable method for post-market safety surveillance of vaccines.

Objective:To investigate the impact of preconception body mass index (BMI) on neonatal birth weight and the risk of macrosomia in pregnant women across various age groups.Methods:A cohort study was conducted, selecting pregnant women who underwent their initial prenatal assessment at Beijing Obstetrics and Gynecology Hospital from September 1st, 2018 to March 31st, 2020. Relevant data were collected from the hospital′s electronic medical record system. Logistic regression nested cubic spline was used to analyze the nonlinear association between preconception BMI and neonatal birth weight. Binary logistic regression was also employed to assess the association between preconception BMI and macrosomia risk.Results:(1) A total of 13 015 pregnant women were examined, revealing an incidence of macrosomia of 6.33% (824/13 015). The preconception BMI of pregnant women in the macrosomia group was significantly higher than that in the non-macrosomia group [(23.1±3.4) vs (21.6±3.1) kg/m 2], and the age was significantly higher than that in the non-macrosomia group [(32.1±3.6) vs (31.7±3.7) years], the differences were statistically significant (all P<0.05). (2) Preconception BMI was positively correlated with neonatal birth weight. Pregnant women with preconception BMI of 15.0 kg/m 2, 20.0 kg/m 2, and 25.0 kg/m 2 had decreased birth weight of 121 g (95% CI: 35-183 g) and increased birth weights of 78 g (95% CI: 54-102 g) and 182 g (95% CI: 151-213 g), respectively, compared to those with a preconception BMI of 18.0 kg/m 2. (3) For each 1.0 kg/m 2 increase in preconception BMI, the risk of macrosomia increased by 14% ( OR=1.14, 95% CI: 1.11-1.16; P<0.001). When stratified by age, it was observed that elevated preconception BMI significantly increased the incidence of macrosomia in women aged 27-38 years. Among them, the risk of delivering macrosomia among 37 years old pregnant women was most affected by preconception BMI ( OR=1.33, 95% CI: 1.17-1.51; P<0.001). (4) The stability and sensitivity analysis results showed that the preconception BMI of pregnant women with a preconception BMI of 18.0-<25.0 kg/m 2 had a significant impact on the risk of macrosomia ( OR=1.23, 95% CI: 1.17-1.29; P<0.001), while the preconception BMI of other preconception BMI stratification pregnant women had no significant impact on the risk of macrosomia (all P>0.05). Hypertension disorders in pregnancy, gestational diabetes mellitus and abnormal blood lipid during pregnancy were not the mediators associated with preconception BMI and macrosomia. After excluding three factors respectively, the impact of preconception BMI on the risk of macrosomia was the same as before ( OR=1.14, 95% CI: 1.11-1.16; P<0.001). Conclusions:Preconception BMI is linked to neonatal birth weight and the risk of macrosomia, which is influenced by the pregnant woman′s age. Both factors should be considered when evaluating the risk of macrosomia in clinical practice.

AIM:To evaluate the application effec-tiveness of a Bayesian mixture model based on the principal stratum strategy for estimating the com-plier average causal effect(CACE)in clinical trials with non-compliance.METHODS:Using a non-infe-riority randomized controlled trial investigating a novel drug for primary type 2 diabetes mellitus(non-inferiority margin:-0.4)as a case study,the primary analysis applied a Bayesian mixture model under the monotonicity assumption to estimate CACE of between-group differences in glycated he-moglobin(HbA1c)changes within the compliant stratum,followed by non-inferiority testing.Sensi-tivity analyses included a Bayesian mixture model relaxing the monotonicity assumption and compar-ing results with per-protocol set(PPS)analysis.RE-SULTS:In the primary analysis,the posterior mean of CACE for HbA1c change in the compliant stratum was 0.081％,with a one-sided 97.5％credible inter-val lower bound of-0.124,exceeding the non-infe-riority margin(-0.4％),supporting the non-inferiori-ty efficacy of the novel drug in the compliant stra-tum(P(H1|Data)=1).Consistent findings were ob-served in PPS analyses(estimated effect:0.136％;one-sided 97.5％credible interval lower bound:-0.069％),further validating methodological robust-ness.CONCLUSION:In clinical trials with noncom-pliance as an intercurrent event,the Bayesian mix-ture model under the principal stratum strategy ef-fectively adjusts for compliance-related bias and yields conservative,robust estimates of causal ef-fects,supporting its value in efficacy evaluation un-der complex compliance scenarios.

Objective:To explore the mechanism of ubiquitin-specific protease 11(USP11)affecting the proliferation and invasion of oral squamous cell carcinoma(OSCC)cells by regulating IGF2BP3 expression.Methods:USP11 expression in OSCC tissues and adja-cent tissues from OSCC patients(n=50)was detected by immunohistochemistry and western blot,and USP11 expression in normal hu-man oral keratinocyte(HOK)cell line and human OSCC cell lines SCC-25 and CAL-27 was detected by western blot.SCC-25 and CAL-27 cells were transfected with siRNA USP11(si-USP11)or siRNA negative control(si-NC).Western blot was performed to de-tect the silencing efficiency of USP11.CCK-8,wound healing assay and Transwell assay were carried out to evaluate the effects of USP11 silencing on cell proliferation,migration and invasion.Western blot was employed to detect IGF2BP3 expression after the knockdown of USP11.Nude mice were inoculated with SCC-25 cells to construct the transplanted tumor model,and the inhibitory effect of USP11 knock-down on SCC-25 cell tumorigenicity was investigated.Results:The USP11 protein level in carcinoma tissues of OSCC patients was significantly higher than in the adjacent tissues,USP11 protein expression was significantly higher in SCC-25 and CAL-27 cells than in HOK cells.The knockdown of USP11 markedly reduced the proliferation,migration and invasion of SCC-25 and CAL-27 cells,and down-regulated the expression of IGF2BP3 cells.Compared with the USP11 silencing group,the proliferation,migration and invasion of SCC-25 and CAL-27 cells were significantly increased in the simultaneous knockdown of USP11 and overexpression of IGF2BP3 cells.Compared with the USP11 overexpression group,the proliferation,migration and invasion of SCC-25 and CAL-27 cells were decreased in the simultaneous IGF2BP3 knockdown and USP11 overexpression cells.Tumorigenicity experiments in nude mice showed that the tumor volume and weight were significantly declined by USP11 knockdown.Conclusion:USP11 is highly expressed in OSCC tissues,which may promote the proliferation,migration and invasion of OSCC cells through up-regulation of IGF2BP3 expression.

BACKGROUND: China bears the biggest atrial fibrillation (AF) burden in the world. However, little is known about the incidence and predictors of AF. This study aimed to investigate the current incidence of AF and its electrocardiographic (ECG) predictors in general community individuals aged over 60 years in China. METHODS: This was a prospective cohort study, recruiting subjects who were aged over 60 years and underwent annual health checkups from April to July 2015 in four community health centers in Songjiang District, Shanghai, China. The subjects were then followed up from 2015 to 2019 annually. Data on sociodemographic characteristics, medical history, and the resting 12-lead ECG were collected. Kaplan-Meier curve was used for showing the trends in AF incidence and calculating the predictors of AF. Associations of ECG abnormalities and AF incidence were examined using Cox proportional hazard models. RESULTS: This study recruited 18,738 subjects, and 351 (1.87%) developed AF. The overall incidence rate of AF was 5.2/1000 person-years during an observation period of 67,704 person-years. Multivariable Cox regression analysis indicated age (hazard ratio [HR], 1.07; 95% confidence interval [CI]: 1.06-1.09; P < 0.001), male (HR, 1.30; 95% CI: 1.05-1.62; P = 0.018), a history of hypertension (HR, 1.55; 95% CI: 1.23-1.95; P < 0.001), a history of cardiac diseases (HR, 3.23; 95% CI: 2.34-4.45; P < 0.001), atrial premature complex (APC) (HR, 2.82; 95% CI: 2.17-3.68; P < 0.001), atrial flutter (HR, 18.68; 95% CI: 7.37-47.31; P < 0.001), junctional premature complex (JPC) (HR, 3.57; 95% CI: 1.59-8.02; P = 0.002), junctional rhythm (HR, 18.24; 95% CI: 5.83-57.07; P < 0.001), ventricular premature complex (VPC) (HR, 1.76; 95% CI: 1.13-2.75, P = 0.012), short PR interval (HR, 5.49; 95% CI: 1.36-22.19; P = 0.017), right atrial enlargement (HR, 6.22; 95% CI: 1.54-25.14; P = 0.010), and pacing rhythm (HR, 3.99; 95% CI: 1.57-10.14; P = 0.004) were independently associated with the incidence of AF. CONCLUSIONS The present incidence of AF was 5.2/1000 person-years in the studied population aged over 60 years in China. Among various ECG abnormalities, only APC, atrial flutter, JPC, junctional rhythm, short PR interval, VPC, right atrial enlargement, and pacing rhythm were independently associated with AF incidence.
Humans ; Male ; Middle Aged ; Aged ; Atrial Fibrillation/epidemiology* ; Prospective Studies ; Incidence ; Atrial Flutter/complications* ; Risk Factors ; China/epidemiology* ; Electrocardiography

Pregnancy ; Female ; Humans ; Cesarean Section ; Retrospective Studies ; East Asian People ; Maternal Age ; Policy