Objective To analyze the structural and phylogenetic characteristics of the mitochondrial genome from Bulinus globosus, so as to provide a theoretical basis for classification and identification of species within the Bulinus genus, and to provide insights into understanding of Bulinus-schistosomes interactions and the mechanisms of parasite transmission. Methods B. globosus samples were collected from the Ruya River basin in Zimbabwe. Mitochondrial DNA was extracted from B. globosus samples and the corresponding libraries were constructed for high-throughput sequencing on the Illumina NovaSeq 6000 platform. After raw sequencing data were subjected to quality control using the fastp software, genome assembly was performed using the A5-miseq and SPAdes tools, and genome annotation was conducted using the MITOS online server. Circular maps and sequence plots of the mitochondrial genome were generated using the CGView and OGDRAW software, and the protein conservation motifs and structures were analyzed using the TBtools software. Base composition and codon usage bias were analyzed and visualized using the software MEGA X and the ggplot2 package in the R software. In addition, a phylogenetic tree was created in the software MEGA X after sequence alignment with the software MAFFT 7, and visualized using the software iTOL. Results The mitochondrial genome of B. globosus was a 13 730 bp double-stranded circular molecule, containing 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes, and 13 protein-coding genes, with a marked AT preference. The mitochondrial genome composition of B. globosus was similar to that of other species within the Bulinus genus. Phylogenetic analysis revealed that the complete mitochondrial genome sequence of B. globosus was clustered with B. truncatus, B. nasutus, and B. ugandae into the same evolutionary clade, and gene superfamily analysis showed that the metabolism-related proteins of B. globosus were highly conserved, notably the cytochrome c oxidase family, which showed a significant consistency. Conclusions This is the first whole mitochondrial genome sequencing to decode the compositional features of the mitochondrial genome of B. globosus from Zimbabwe and its evolutionary relationship within the Bulinus genus, which provides important insights for further understanding of the phylogeny and mitochondrial genome characteristics of the Bulinus genus.

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.

Objective To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. Methods The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Results Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Conclusions Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

In order to promote the development of grassroots hospitals through targeted assistance,ZhuJiang Hospital of Southern Medical University took the lead in responding in Guangdong Province and established a tight-ly-knit specialty alliance belonging to Zhujiang.It introduces the construction content,principles and measures of Zhujiang Tightly-Knit Specialty Alliance,and proposes a dual-promotion management model for the tightly-knit Spe-cialty Alliance.A comprehensive comparison of the changes in specialized technical capabilities and medical quality of Guangning County People's Hospital before and after aid shows that the close specialist alliance has achieved initial results in effectively promoting the sinking of resources and improving the medical technology capabilities and quality of member units.It is of great practical significance for further promoting the development of specialist alliances and filling the shortcomings of primary medical institutions.

In order to promote the development of grassroots hospitals through targeted assistance,ZhuJiang Hospital of Southern Medical University took the lead in responding in Guangdong Province and established a tight-ly-knit specialty alliance belonging to Zhujiang.It introduces the construction content,principles and measures of Zhujiang Tightly-Knit Specialty Alliance,and proposes a dual-promotion management model for the tightly-knit Spe-cialty Alliance.A comprehensive comparison of the changes in specialized technical capabilities and medical quality of Guangning County People's Hospital before and after aid shows that the close specialist alliance has achieved initial results in effectively promoting the sinking of resources and improving the medical technology capabilities and quality of member units.It is of great practical significance for further promoting the development of specialist alliances and filling the shortcomings of primary medical institutions.

In order to promote the development of grassroots hospitals through targeted assistance,ZhuJiang Hospital of Southern Medical University took the lead in responding in Guangdong Province and established a tight-ly-knit specialty alliance belonging to Zhujiang.It introduces the construction content,principles and measures of Zhujiang Tightly-Knit Specialty Alliance,and proposes a dual-promotion management model for the tightly-knit Spe-cialty Alliance.A comprehensive comparison of the changes in specialized technical capabilities and medical quality of Guangning County People's Hospital before and after aid shows that the close specialist alliance has achieved initial results in effectively promoting the sinking of resources and improving the medical technology capabilities and quality of member units.It is of great practical significance for further promoting the development of specialist alliances and filling the shortcomings of primary medical institutions.

In order to promote the development of grassroots hospitals through targeted assistance,ZhuJiang Hospital of Southern Medical University took the lead in responding in Guangdong Province and established a tight-ly-knit specialty alliance belonging to Zhujiang.It introduces the construction content,principles and measures of Zhujiang Tightly-Knit Specialty Alliance,and proposes a dual-promotion management model for the tightly-knit Spe-cialty Alliance.A comprehensive comparison of the changes in specialized technical capabilities and medical quality of Guangning County People's Hospital before and after aid shows that the close specialist alliance has achieved initial results in effectively promoting the sinking of resources and improving the medical technology capabilities and quality of member units.It is of great practical significance for further promoting the development of specialist alliances and filling the shortcomings of primary medical institutions.

In order to promote the development of grassroots hospitals through targeted assistance,ZhuJiang Hospital of Southern Medical University took the lead in responding in Guangdong Province and established a tight-ly-knit specialty alliance belonging to Zhujiang.It introduces the construction content,principles and measures of Zhujiang Tightly-Knit Specialty Alliance,and proposes a dual-promotion management model for the tightly-knit Spe-cialty Alliance.A comprehensive comparison of the changes in specialized technical capabilities and medical quality of Guangning County People's Hospital before and after aid shows that the close specialist alliance has achieved initial results in effectively promoting the sinking of resources and improving the medical technology capabilities and quality of member units.It is of great practical significance for further promoting the development of specialist alliances and filling the shortcomings of primary medical institutions.

In order to promote the development of grassroots hospitals through targeted assistance,ZhuJiang Hospital of Southern Medical University took the lead in responding in Guangdong Province and established a tight-ly-knit specialty alliance belonging to Zhujiang.It introduces the construction content,principles and measures of Zhujiang Tightly-Knit Specialty Alliance,and proposes a dual-promotion management model for the tightly-knit Spe-cialty Alliance.A comprehensive comparison of the changes in specialized technical capabilities and medical quality of Guangning County People's Hospital before and after aid shows that the close specialist alliance has achieved initial results in effectively promoting the sinking of resources and improving the medical technology capabilities and quality of member units.It is of great practical significance for further promoting the development of specialist alliances and filling the shortcomings of primary medical institutions.

In order to promote the development of grassroots hospitals through targeted assistance,ZhuJiang Hospital of Southern Medical University took the lead in responding in Guangdong Province and established a tight-ly-knit specialty alliance belonging to Zhujiang.It introduces the construction content,principles and measures of Zhujiang Tightly-Knit Specialty Alliance,and proposes a dual-promotion management model for the tightly-knit Spe-cialty Alliance.A comprehensive comparison of the changes in specialized technical capabilities and medical quality of Guangning County People's Hospital before and after aid shows that the close specialist alliance has achieved initial results in effectively promoting the sinking of resources and improving the medical technology capabilities and quality of member units.It is of great practical significance for further promoting the development of specialist alliances and filling the shortcomings of primary medical institutions.