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.

Objective To evaluate cardiac involvement in patients with anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) using echocardiography combined with electrocardiography. Methods A retrospective analysis was performed on the detailed medical records of AAV patients treated in Jining First People’s Hospital between January 2020 and December 2024. Eighty patients were enrolled in the AAV group, and the risk of heart disease was compared between the AAV group and a control group with 80 subjects matched for age, sex, and cardiovascular disease risk factors. Results Electrocardiographic abnormalities were observed in 78.75% of patients in the AAV group, while significant electrocardiographic abnormalities only occurred in symptomatic patients in the control group. There were no differences in left atrial enlargement or interventricular septal thickening between the AAV group and the control group. The overall left ventricular systolic function in the AAV group was lower than that in the control group (8.75% vs. 0). The incidence of reduced diastolic function in the AAV group was significantly higher than that in the control group (37.5% vs. 15%). The incidence rates of tricuspid regurgitation, mitral regurgitation, aortic regurgitation, and pericardial effusion in the AAV group were significantly higher than those in the control group. Pericardial thickening, aortic stenosis, pulmonary hypertension, and rare periaortic granulomas were found in the AAV group, but not in the control group. Conclusion Echocardiography and electrocardiography are important examination methods for evaluating cardiac involvement in AAV. These methods have key roles in disease screening, diagnosis and treatment, follow-up, and prognosis judgment.

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.

Biliary atresia is a progressive disease involving the intrahepatic and extrahepatic bile ducts. At present, the widely used treatment strategy is portojejunostomy (Kasai procedure). However, fat-soluble vitamin deficiency is common in children with biliary atresia, leading to growth retardation and malnutrition, which further affects the therapeutic effect prognosis of children. This article reviews the etiology, performance, prevention and treatment of fat-soluble vitamins deficiency in children with biliary atresia.

In epidemiological research, accurate estimation of prevalence is important for understanding disease distribution, evaluating the effectiveness of interventions, and allocating health resources. However, the prevalence estimation is often influenced by misclassification bias. Quantitative bias analysis (QBA) can comprehensively evaluate the potential impact of bias on outcomes from three dimensions: bias type, level, and uncertainty. Although QBA research has been developed rapidly in the world in recent years, the introduction of QBA design principles, evaluation methods, and application cases is still insufficient in China. In our previous study, we introduced a new method for adjusting misclassification bias of prevalence and suggested the corresponding analytical tools. Based on the results of previous studies, this paper introduces the principles of QBA design, evaluation indexes, and the application of Bayesian methods in bias adjustment, which provide methodological support for epidemiologists conducting research in this field.

In epidemiological research, accurate estimation of prevalence is important for understanding disease distribution, evaluating the effectiveness of interventions, and allocating health resources. However, the prevalence estimation is often influenced by misclassification bias. Quantitative bias analysis (QBA) can comprehensively evaluate the potential impact of bias on outcomes from three dimensions: bias type, level, and uncertainty. Although QBA research has been developed rapidly in the world in recent years, the introduction of QBA design principles, evaluation methods, and application cases is still insufficient in China. In our previous study, we introduced a new method for adjusting misclassification bias of prevalence and suggested the corresponding analytical tools. Based on the results of previous studies, this paper introduces the principles of QBA design, evaluation indexes, and the application of Bayesian methods in bias adjustment, which provide methodological support for epidemiologists conducting research in this field.

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.

Biliary atresia is a progressive disease involving the intrahepatic and extrahepatic bile ducts. At present, the widely used treatment strategy is portojejunostomy (Kasai procedure). However, fat-soluble vitamin deficiency is common in children with biliary atresia, leading to growth retardation and malnutrition, which further affects the therapeutic effect prognosis of children. This article reviews the etiology, performance, prevention and treatment of fat-soluble vitamins deficiency in children with biliary atresia.

Objective To construct a visual intelligent recognition model for Oncomelania hupensis robertsoni in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of O. hupensis robertsoni. Methods A total of 400 O. hupensis robertsoni and 400 Tricula snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 O. hupensis robertsoni and 300 Tricula snails. A total of 925 O. hupensis robertsoni images and 1 062 Tricula snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 O. hupensis robertsoni and 354 images from the remaining 100 Tricula snails served as an external test set. All acquired images were subjected to preprocessing, including cropping and resizing. Three data augmentation approaches were employed, including baseline, Mixup and Gaussian blurring, and model hyperparameters included two optimization algorithms of adaptive moment estimation (Adam) and stochastic gradient descent (SGD), two loss functions of focal loss and cross entropy loss, and two learning rate decay strategies of cosine annealing and multi-step. The intelligent recognition models of O. hupensis robertsoni and Tricula snails were constructed based on the EfficientNet-B4 model, and 7 training strategy groups were generated by combinations of different data augmentation approaches and hyperparameters. The performance of intelligent recognition models was tested with external test sets, and evaluated with accuracy, precision, recall, F1 score, loss, Youden’s index, and the area under the receiver operating characteristic curve (AUC) under different training strategies. Results The variation of loss values was comparable among intelligent recognition models with different data augmentation approaches. The Group 4 model constructed with Mixup and Gaussian blurring data augmentation approaches showed the optimal performance, with an accuracy of 90.38%, precision of 90.07%, F1 score of 89.44%, Youden’s index of 0.81 and AUC of 0.961 in the external test set. The accuracy of models using the SGD optimizer reduced by 29.16% as compared to those using the Adam optimizer (χ² = 81.325, P < 0.001), and the accuracy of models using the cross entropy loss function reduced by 0.80% as compared to the Group 4 model (χ² = 3.147, P > 0.05), while the accuracy of models using the multi-step learning rate decay strategy increased by 0.65% as compared to the Group 4 model (χ² = 0.208, P > 0.05). In addition, the model with the baseline + Mixup + Gaussianblurring data augmentation approach and hyperparameters of Adam optimizer, focal loss function and multi-step learning rate decay strategy showed the highest performance, with an accuracy of 91.03%, precision of 91.97%, recall of 88.11%, F1 score of 90.00%, Youden’s index of 0.82 and AUC values of 0.969 in external test set, respectively. Conclusions The intelligent recognition model of O. hupensis robertsoni based on EfficientNet-B4 model is accurate for identification of O. hupensis robertsoni and Tricula snails in Yunnan Province.

Objective:To elucidate the causal relationship between serum metabolites and hepatocellular carcinoma (HCC) by the Mendelian randomization.Methods:The serum metabolite genome-wide association study (GWAS) data from the Metabolomics GWAS server was selected as the exposure group. The study sample includes 7 824 adults from two European population studies. The GWAS data of HCC was obtained from the IEU Open GWAS project as the outcome group, including a total sample of 197 611 cases, to evaluate the relationship between 486 serum metabolites and HCC. The inverse variance weighting method (IVW) was used as the primary analysis method. Supplementary analysis methods included MR-Egger, weighted median, simple mode, and weighted mode. Sensitivity analyses were conducted using Cochran's Q test, MR-Egger intercept test, leave-one-out analysis, and MR-PRESSO. Reverse MR and MR-Steiger tests were employed to exclude the influence of reverse causality. Metabolomic pathway analysis was performed using MetaboAnalyst 5.0. Results:The MR results finally identified six metabolites with potential causal relationships with HCC: mannose ( OR=0.38, 95% CI: 0.16-0.92, P=0.032), γ-glutamyltyrosine ( OR=3.34, 95% CI: 1.14-9.83, P=0.028), glycerol-3-phosphate ( OR=0.17, 95% CI: 0.04-0.70, P=0.014), 2-linoleoylglycerophosphocholine ( OR=0.33, 95% CI: 0.13-0.98, P=0.028), 1-stearoylglycerophosphoethanolamine ( OR=2.44, 95% CI: 1.05-5.65, P=0.038), and palmitoyl sphingomyelin ( OR=5.62, 95% CI: 1.56-20.18, P=0.008). Sensitivity analyses for the six metabolites showed robustness, with no abnormal variables in the heterogeneity tests, and no evidence of genetic pleio-tropy was observed. Both reverse MR and Steiger tests did not support the existence of reverse causality between the metabolites and HCC. Metabolic pathway analysis indicated that ether lipid metabolism is closely related to the occurrence of HCC ( P=0.002). Conclusion:Six serum metabolites (mannose, γ-glutamyltyrosine, glycerol-3-phosphate, 2-linoleoylglycerophosphocholine, 1-stearoylglycerophosphoethanolamine, and palmitoyl sphingomyelin) have causal relationships with HCC.