Objective To assess the survival vulnerability of Oncomelania hupensis in Jiangxi Province under future climate scenarios, and to identify low-vulnerability areas for its survival in this province. Methods Village-level O. hupensis snail survey and O. hupensis snail control with chemical treatments in Jiangxi Province from 2016 to 2024 were captured from the Parasitic Disease Prevention and Control Information Management System of China Disease Prevention and Control Information System. Climatic data were primarily sourced from the Resource and Environmental Science Data Platform, Chinese Academy of Sciences (http://www.resdc.cn/), including annual average temperature, annual average precipitation, annual accumulated temperature above 10 °C, annual accumulated temperature above 0 °C, annual maximum temperature, annual minimum temperature, and annual average relative humidity, and nineteen bioclimatic variables were downloaded from the WorldClim website (https://www.worldclim.org/), including mean diurnal range, isothermality, temperature seasonality, and so on. Elevation and normalized difference vegetation index were catprued from the Resource and Environmental Science Data Platform, Chinese Academy of Sciences (http://www.resdc.cn/), and distance to rivers was downloaded from the WorldPop website (http://www.worldpop.org), and land use and land cover (LULC) data were downloaded from the Big Earth Data Center, Chinese Academy of Sciences (https://data.casearth.cn/), and nature reserve data were obtained from the China Nature Reserve Specimen Resource Sharing Platform (http://www.papc.cn/). Three Shared Socioeconomic Pathways (SSPs) from the Beijing Climate Center-Climate System Model version 2-Medium Resolution (BCC-CSM2-MR) global climate model were employed as future climate scenarios, including SSP126, SSP245, SSP585, and the biomod2 ensemble model in R package was used to simulate suitable habitats for O. hupensis snails in Jiangxi Province in 2050 and 2070 under these scenarios. A snail survival vulnerability index was constructed based on the area of suitable snail habitats, area covered by snail control through chemical treatment, area covered by nature reserves, and changes in snail habitat fragmentation, and a map of snail survival vulnerability distribution was plotted. Results The real area of snail habitats ranged from 78 486.76 to 85 309.47 hm², and the area of snail control with chemical treatment ranged from 10 138.98 to 13 240.16 hm² in Jiangxi Province from 2016 to 2024. There were 429 to 531 villages detected with snails during the nine-year period, and the number of actually snail-infested villages ranged from 645 to 686. A total of 818 snail-present points and 1 996 snail-absent points were obtained from snail survey records. The best performance of the biomod2 ensemble model was achieved if a weighted mean approach was used as the ensemble strategy, with a true skill statistic value of 0.799 and an area under the receiver operating characteristic curve of 0.957, and modeling identified annual average relative humidity and annual average precipitation as two most influencing climatic variables for snail distribution. Relative to the current areas of suitable snail habitats under present climate conditions, the area of suitable snail habitats was projected to expand by 24.49% to 46.28% in Jiangxi Province under future climate scenarios, and the proportion of nature reserves areas in the areas of suitable snail habitats was projected to decrease slightly from the current 2.77% to approximately 2.52%, while the proportion of areas of snail control through chemical treatment in areas of suitable snail habitats varied from 0.64% to 19.57%, and the percentage of changes in snail habitat fragmentation ranged from 3.86% to 12.23%. Based on these four indicators, the snail survival vulnerability index was estimated to range from –1.96 to 0.62 in Jiangxi Province. The arithmetic mean of the snail survival vulnerability index differed under three SSP scenarios (SSP126, SSP245 and SSP585), with the highest mean value (–0.69) in 2070 under SSP126, and the lowest mean value (–0.78) in 2070 under SSP585. Conclusions The snail survival vulnerability index ranges from –1.96 to 0.62 in Jiangxi Province under future climate scenarios, and the suitable habitats for O. hupensis snails appear an overall tendency towards expansion. Low-vulnerability snail habitats are mainly distributed along the shores of Poyang Lake and the Yangtze River in Jiangxi Province, partially overlapping with nature reserves. Intensified surveillance of O. hupensis snails is recommended in these areas in the future.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Disruption of the intestinal mucosal barrier caused by gut dysbiosis and metabolic imbalance is the underlying pathology of inflammatory bowel disease (IBD). Traditional Chinese medicine Wuji Wan (WJW) is commonly used to treat digestive system disorders and showed therapeutic potential for IBD. In this interdisciplinary study, we aim to investigate the pharmacological effects of WJW against experimental colitis by combining functional metabolomics and gut-microbiota sequencing techniques. Treatment with WJW altered the profile of the intestinal microbiota and notably increased the abundance of Lactobacillus, thereby facilitating the conversion of tryptophan into indole-3-acetic acid (IAA) and indoleacrylic acid (IA). These indole derivatives activated the aryl hydrocarbon receptor (AhR) pathway, which reduced colonic inflammation and restored the expression of intestinal barrier proteins. Interestingly, the beneficial effects of WJW on gut barrier function improvement and tryptophan metabolism were disappeared in the absence of gut microbiota. Finally, pre-treatment with the AhR antagonist CH-223191 confirmed the essential role of IAA-mediated AhR activation in the therapeutic effects of WJW. Overall, WJW enhanced intestinal barrier function and reduced colonic inflammation in a murine colitis model by modulating Lactobacillus-IAA-AhR signaling pathway. This study provides novel insights into colitis pathogenesis and presents an effective therapeutic and preventive approach against IBD.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.

Objective: To analyze the epidemiological characteristics and changing trends of non suicidal self injury (NSSI) behaviors among middle school students in Jiading District of Shanghai, from 2015 to 2023, so as to provide a basis for the development of NSSI prevention and control measures among students. Methods: Using a stratified cluster random sampling method, a total of five times for Shanghai Adolescent Health Risk Behavior Surveys were conducted for every two years in Jiading District of Shanghai from 2015 to 2023. A total of 5 231 middle school students from junior high schools and senior high schools were selected for questionnaire surveys. Intergroup comparisons were performed using the x 2 test or the χ 2 trend test, and the JointPoint 5.0 software was used to analyze the changing trends, with the annual percent change (APC) used for evaluation. A binary Logistic regression model was employed to analyze the related factors of NSSI behavior among middle school students. Results: In 2023, the reported NSSI rate among middle school students in Jiading District was 14.2%. The rate was significantly higher among junior high school students (17.1%) than that among senior high school students (11.1%), and higher among females (19.2%) than that among males (10.0%) ( χ 2=10.04, 23.21, both P <0.01). From 2015 to 2023, the overall reported NSSI rate showed an increasing trend, rising from 8.6% in 2015 to 14.2% in 2023 ( χ 2 trend =22.25), with an APC of 6.64% ( t =3.49), and the APC for girls was 9.79 % ( t =3.20) (all P <0.05). Among students reporting NSSI, the proportion experiencing ≥6 episodes increased from 10.8% in 2015 to 19.2% in 2023 ( χ 2 trend =6.57, P <0.05). Multivariate Logistic regression analysis indicated that girls, junior high school students, those with insomnia, depressive emotion and drinkers had higher risks of NSSI, compared to boys, senior high school students, those without insomnia, non depressive emotion students and non drinkers ( OR =1.71, 1.96, 3.44, 4.76, 1.77, all P < 0.05 ). Conclusions The reported rate of NSSI among middle school students in Jiading District of Shanghai, increased annually from 2015 to 2023, and the proportion of repeated NSSI also showed an upward trend. Early intervention measures targeting middle school students, especially junior high school students and females, should be implemented to prevent and control its occurrence and development.

Objective To observe CT and MRI manifestations of gastritis cystica profunda(GCP).Methods Seventeen patients with GCP confirmed by operation or biopsy pathology were enrolled,and lesions'CT and MRI manifestations were observed.Results Among 17 cases,16 cases(16/17,94.12％)were found with single lesion and 1(1/17,5.88％)with diffuse multiple lesions.The lesion located in the fundus of stomach in 5 cases(5/17,29.41％),in the body of stomach in 4 cases(4/17,23.53％),in the cardia and antrum of stomach each in 3 cases(3/17,17.65％)and in the pylorus in 1 case(1/17,5.88％),while 1 case(1/17,5.88％)was found with diffused multiple lesions within stomach.Non-enhance CT showed local thickening of gastric wall in 10 cases(10/17,58.82％),all were isodensities,and the mucosa uniformly enhanced in contrast enhance CT(CECT).Predominately cystic lesion in 5 cases(5/17,29.41％)presented as submucosal cystic protrusions,and grew into the stomach cavity with circular or oblong low density in non-enhanced CT,while sandwich enhancement of mucosa was observed in CECT.Among these 5 cases(5/17,29.41％),MRI showed lesion confined to the submucosa with low signal on T1WI and high signal on T2WI,while diffusion weighted imaging showed unrestricted diffusion,and the enhancement pattern was consistent with that of CT in 2 cases.In other 2 cases(2/17,11.77％)with cystic-solid lesion,non-enhanced CT showed soft tissue density,while CECT showed lump-like stratified enhancement.Conclusion CT and MRI manifestations of GCP had certain characteristics.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.

Objective To observe CT and MRI manifestations of gastritis cystica profunda(GCP).Methods Seventeen patients with GCP confirmed by operation or biopsy pathology were enrolled,and lesions'CT and MRI manifestations were observed.Results Among 17 cases,16 cases(16/17,94.12％)were found with single lesion and 1(1/17,5.88％)with diffuse multiple lesions.The lesion located in the fundus of stomach in 5 cases(5/17,29.41％),in the body of stomach in 4 cases(4/17,23.53％),in the cardia and antrum of stomach each in 3 cases(3/17,17.65％)and in the pylorus in 1 case(1/17,5.88％),while 1 case(1/17,5.88％)was found with diffused multiple lesions within stomach.Non-enhance CT showed local thickening of gastric wall in 10 cases(10/17,58.82％),all were isodensities,and the mucosa uniformly enhanced in contrast enhance CT(CECT).Predominately cystic lesion in 5 cases(5/17,29.41％)presented as submucosal cystic protrusions,and grew into the stomach cavity with circular or oblong low density in non-enhanced CT,while sandwich enhancement of mucosa was observed in CECT.Among these 5 cases(5/17,29.41％),MRI showed lesion confined to the submucosa with low signal on T1WI and high signal on T2WI,while diffusion weighted imaging showed unrestricted diffusion,and the enhancement pattern was consistent with that of CT in 2 cases.In other 2 cases(2/17,11.77％)with cystic-solid lesion,non-enhanced CT showed soft tissue density,while CECT showed lump-like stratified enhancement.Conclusion CT and MRI manifestations of GCP had certain characteristics.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.