Tropical diseases, the transmission of which is affected by multiple natural and social factors, pose a great challenge to global public health, notably in African countries. During the past several decades, China and African countries have continuously collaborated for the control of neglected tropical diseases and malaria, which has become an important part of global South-to-South cooperation and global health governance. This article reviews the history of China-Africa cooperation for tropical diseases control, summarizes the experiences and achievements over the past decade, analyzes the current challenges in the coopera tion, and proposes future recommendations. The China-Africa cooperation has achieved significant progress in the control of tropical diseases, such as malaria, schistosomiasis, and filariasis, and established a China-Africa cooperation network for tropical diseases control. Through the "Three-Step" strategy of China-Africa cooperation, the effectiveness of China's integrated control strategies has been validated in Africa, and the application of China's tropical disease control technologies has been promoted in African disease-epidemic countries. Currently, China-Africa collaboration, however, still experiences multiple realistic challenges, such as insufficient resources, difficulty in technology transfer, and weak primary healthcare systems. In the future, both sides are recommended to further strengthen policy coordination, deepen technological cooperation, innovate cooperation models, aiming to continuously promote the high-quality development of China-Africa cooperation for tropical diseases control.

Objective To evaluate the effectiveness of schistosomiasis surveillance in Jiangsu Province during the stage moving from transmission control to transmission interruption, and to analyze the current risk and challenges, so as to provide the evidence for achieving the target of schistosomiasis elimination. Methods Schistosomiasis surveillance data were collected from Jiangsu Province from 2012 to 2024, and the endemic areas, Schistosoma japonicum infections in humans and livestock, Oncomelania hupensis snail distribution and implementation of integrated interventions were descriptively analyzed. In addition, the trends in areas with snails, seroprevalence of human S. japonicum infections and numbers of advanced schistosomiasis cases were assessed using a Joinpoint regression model. Results The endemic areas of schistosomiasis continued to shrink in Jiangsu Province from 2012 to 2024, with the number of schistosomiasis-eliminated counties (cities, districts) increasing from 53 (75.71%) to 63 (96.92%), and interruption of schistosomiasis transmission was achieved across the province. A total of 4 600 300 person-times were tested for serum antibodies against S. japonicum, with 28 719 person-times positive detected; and 616 500 person-times were tested S. japonicum infections among local residents in Jiangsu Province from 2012 to 2024, with only 3 egg-positives detected, and no egg-positives found since 2017. A total of 187 600 herd-times were tested for schistosomiasis in livestock, and no S. japonicum infections were found. O. hupensis snail survey was performed covering 1 018 408.97 hm², and a total of 35 556.35 hm² was found with snail-infested habitats, including 174.40 hm2 of emerging snail-infested habitats. A total of 1 102 800 O. hupensis snails were identified for S. japonicum infections, and no infections were found. The areas of snail-infested habitats appeared a tendency towards a rise in Jiangsu Province from 2019 to 2023 (APC = 23.67%, P < 0.05), and the actual areas of snail-infested habitats appeared a tendency towards a decline from 2012 to 2015 (APC = −22.77%, P < 0.05), and towards a rise from 2015 to 2023 (APC = 9.76%, P < 0.01). The seroprevalence of anti-S. japonicum antibodies appeared a tendency towards a decline among residents in Jiangsu Province from 2017 to 2023 (APC = −14.92%, P < 0.01). In addition, the number of newly diagnosed advanced schistosomiasis cases appeared a tendency towards a decline from 2012 to 2024 (APC = −12.02%, P < 0.01), and the numbers of advanced schistosomiasis patients requiring treatment showed a tendency towards a decline from 2012 to 2021 (APC = −10.56%, P < 0.01) and from 2021 to 2023 (APC = −20.06%, P < 0.01). Conclusions Great progresses had been achieved in schistosomiasis control in Jiangsu Province following transmission control, and transmission interruption had been achieved; however, there are still snail-infested habitats. High-intensity surveillance and integrated control are required to be maintained to advance the achievement of the target of schistosomiasis elimination in Jiangsu Province.

Objective To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Methods Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Results Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R²) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m²). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m²) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m²) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. Conclusions The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

Objective To investigate the species of sandflies and the prevalence of Leishmania infections in sandflies from selected areas of northern and northwestern China, so as to provide insights into identification of leishmaniasis vectors and assessment of epidemiological trends of leishmaniasis in China. Methods Sandfly samples were collected from Mentougou District of Beijing Municipality, Xiangning County in Linfen City of Shanxi Province, Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, and Payzawat County of Karamay District of Karamay City, Gaochang District of Turpan City in Xinjiang Uygur Autonomous Region from July 2023 to July 2024. Approximately 100 intact female sandfly samples were randomly selected from each site and the species of sandflies was identified according to morphological characteristics and molecular assays. Female sandflies originating from the same habitat were grouped into pools of 10 individuals. Leishmania infection was detected using polymerase chain reaction (PCR) assay targeting the internal transcribed spacer 1 (ITS-1) gene, and the prevalence of Leishmania infection was calculated in sandflies from different sampling sites using the minimum infection rate (MIR) method. In addition, positive amplicons were sequenced and subjected to phylogenetic analysis. Results A total of 6 155 sandflies were collected from different environments at sampling sites across the six aforementioned regions from July 2023 to July 2024. Phlebotomus chinensis (96.00%) was the dominant sandfly species in Mentougou District, Beijing Municipality, with a small proportion of Ph. sergenti (4.00%), and only Ph. chinensis was found in Xiangning County, Linfen City, Shanxi Province. Ph. wui was the only sandfly species detected in Ejin Banner, Alxa League, Inner Mongolia Autonomous Region, and Payzawat County, Kashgar City, Xinjiang Uygur Autonomous Region, and Ph. caucasicus (97.70%) was the dominant sandfly species in Karamay District, Karamay City, Xinjiang Uygur Autonomous Region, with a small proportion of Ph. wui (2.30%), while Ph. alexandri was the only species in Gaochang District, Turpan City, Xinjiang Uygur Autonomous Region. A total of 40, 60, 34, 18, 18, and 22 pools of sandfly samples were tested from Mentougou District in Beijing Municipality, Xiangning County in Linfen City of Shanxi Province, Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, Payzawat County in Kashgar City, Karamay District in Karamay City, and Gaochang District in Turpan City of Xinjiang Uygur Autonomous Region, respectively. L. infantum was detected in Ph. chinensis samples from Mentougou District in Beijing Municipality, and Xiangning County of Linfen City in Shanxi Province, with MIR of 0.25% to 1.00%, and L. donovani was detected in Ph. wui from Ejin Banner in Alxa League of Inner Mongolia Autonomous Region, and Payzawat County in Kashgar City of Xinjiang Uygur Autonomous Region, with MIR of 0.56% to 0.88%; however, no Leishmania infection was detected in Ph. caucasicus from Karamay District in Karamay City or Ph. alexandri from Gaochang District in Turpan City of Xinjiang Uygur Autonomous Region. Phylogenetic analysis showed that the Leishmania ITS-1 gene sequences obtained from Mentougou District in Beijing Municipality and Xiangning County in Linfen City of Shanxi Province were clustered into the same clade with the reference sequences of L. infantum ITS-1 gene, while the Leishmania ITS-1 gene sequences obtained from Ejin Banner in Alxa League of Inner Mongolia Autonomous Region and Payzawat County in Kashgar City of Xinjiang Uygur Autonomous Region were clustered into the same clade with the reference sequences of L. donovani ITS-1 gene. Conclusions There are variations in sandfly species in selected areas of northern and northwestern China, and variations in the species of Leishmania infecting sandflies. Improved surveillance of sandfly vectors and targeted control strategies with adaptations to geographical features and leishmaniasis vectors are recommended.

Objective To examine the impact of extreme temperature and drought stress on the survival of Bulinus globosus, so as to provide the theoretical evidence for the genomic research of Bulinus in absence of reference genes. Methods B. globosus snail samples were collected from Kiwani Shehia in Pemba Island, Zanzibar, Tanzania, and offspring snails were obtained through laboratory breeding and reproduction. A total of 120 10-week-old B. globosus snails from the same generation were selected and randomly assigned into four groups, including the high-temperature drought (HD) group, normal temperature drought (D) group, low-temperature drought (LD) group, and the control (C) group, of 30 snails in each group. Snails in HD, D, and LD groups were placed in beakers containing dry soil at the bottom and subsequently housed in climate chambers at 35, 26 ℃, and 10 ℃, respectively, while snails in Group C were maintained in 500 mL petri dishes containing dechlorinated tap water at 26 ℃. Following 3 days of breeding, living snails in each group were collected, and soft tissues were dissected and isolated. Total RNA was extracted from snail soft tissues for library construction, followed by high-throughput sequencing on the Illumina HiSeq 4000 sequencing system. De novo transcriptome assembly was performed using the Trinity software, and the longest transcripts were selected as unigenes. Gene functional annotations of unigenes were conducted using the Diamond software against Gene Ontology (GO) knowledgebase, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, NCBI non-redundant (NR) protein sequences database, Protein Family (Pfam) database, and UniProtKB/Swiss-Prot (Swiss-Prot) knowledgebase. GO and KEGG enrichment analyses of differentially expressed genes (DEGs) were performed using the topGO and clusterProfiler software, respectively. In addition, four relevant genes were selected for validation using a real-time quantitative PCR (qRT-PCR) assay to verify the reliability of transcriptome sequencing results. Results Following 3 days of breeding, there were 7, 20, 28, and 30 survival B. globosus snails in HD, LD, D, and C groups, with corresponding survival rates of 23.33% (7/30), 66.67% (20/30), 93.33% (28/30), and 100.00% (30/30), respectively (χ² = 52.72, P < 0.001). De novo transcriptome assembly generated 176 942 unigenes, with annotation rates of 0.98%, 13.49%, 26.46%, 12.48%, and 14.39% against GO knowledgebase, KEGG pathway database, NR protein sequences database, Pfam database, and Swiss-Prot knowledgebase, respectively. There were 33 up-regulated and 72 down-regulated genes in Group D, 483 up-regulated and 815 down-regulated genes in Group HD, and 245 up-regulated and 172 down-regulated genes in Group LD relative to in Group C. Following removal of overlapping genes across groups and unmatched genes, 11 candidate genes were identified. GO and KEGG analyses revealed 3 heat shock protein (HSP)-related DEGs in these 11 candidate genes, which were annotated as HSP12.2, HSP70, and HSP20 genes and were all significantly up-regulated in each treatment group. Three immune and nervous system-related DEGs were identified, and were all significantly down-regulated in each treatment group, which were involved in the neural cell adhesion molecule L1-like protein pathway, fibrinogen binding protein pathway, and leukocyte elastase inhibitor-like protein pathway. qRT-PCR assay quantified that the expression trends of four genes related to temperature and drought stress across different treatment groups were highly consistent with transcriptome sequencing data. Conclusion The survival rate of B. globosus significantly reduces under combined stresses of extreme temperature and drought, possibly due to an imbalance in its cellular homeostasis regulatory system.

Objective To evaluate the value of serum Mac-2 binding protein (M2BP) for assessment of the severity of schisto somiasis-induced liver fibrosis, so as to provide insights into non-invasive diagnosis and disease surveillance of liver fibrosis caused by schistosomiasis. Methods A total of 234 individuals with a history of Schistosoma japonicum infection were sampled from Xinhua Village, Lushan City, Jiangxi Province from 2019 to 2020, and 234 serum samples were collected from all participants. All participants received B-ultrasound examinations of the liver. Serum samples were categorized into four groups (grades 0, Ⅰ, Ⅱ and Ⅲ schistosomiasis-induced liver fibrosis groups) according to B-ultrasound examination results, and then, each group was randomly divided into a receiver operating characteristic (ROC) curve group and an efficacy assessment group at a ratio of 7∶3. Serum M2BP concentration was measured in four groups using the enzyme-linked immunosorbent assay (ELISA), and differences in serum M2BP concentrations were compared with analysis of variance and Spearman correlation analysis. Serum M2BP concentration was subjected to ROC curve analysis among individuals with different grades of schistosomiasis-induced liver fibrosis in the ROC curve group to determine the optimal diagnostic threshold of M2BP concentration at different fibrosis grades, and the area under the ROC curve (AUC) was calculated to evaluate the diagnostic performance. The diagnostic accuracy was verified by comparing the accordance rate and Kappa consistency test in the efficacy assessment group. Results Among 234 serum samples, there were 79 samples with grade 0 schistosomiasis-induced liver fibrosis, 87 samples with Grade Ⅰ, 46 samples with Grade Ⅱ and 22 samples with Grade Ⅲ according to the B-ultrasound examinations. The mean serum M2BP concentrations were (0.40 ± 0.31) [95% confidence interval (CI): (0.33, 0.47)], (0.64 ± 0.48) [95% CI: (0.53, 0.74)], (1.76 ± 0.58) [95% CI: (1.59, 1.93)] μg/mL and (2.56 ± 0.93) [95% CI: (2.14, 2.97)] μg/mL in the four groups, respectively (F = 150.796, P < 0.001), and the severity of schistosomiasis-induced liver fibrosis significantly positively correlated with serum M2BP concentration (rs = 0.715, P < 0. 001). The sample sizes of grades 0, Ⅰ, Ⅱ and Ⅲ schistosomiasis-induced liver fibrosis sera were randomly allocated as follows: 55 versus 24, 61 versus 26, 32 versus 14, and 15 versus 7 in the ROC curve and efficacy assessment groups, respectively, and the serum M2BP concentrations were (0.39 ± 0.29) μg/mL and (0.42 ± 0.36) μg/mL (F = 0.196, P > 0.05), (0.59 ± 0.47) μg/mL and (0.75 ± 0.51) μg/mL (F = 1.967, P > 0.05), (1.73 ± 0.59) μg/mL and (1.85 ± 0.57) μg/mL (F = 0.417, P > 0.05), and (2.46 ± 0.64) μg/mL and (2.76 ± 1.41) μg/mL (F = 0.491, P > 0.05), respectively. ROC curve analysis showed that the optimal diagnostic thresholds of serum M2BP concentration were 0.347 86 μg/mL (AUC = 0.635, P < 0.05), 1.188 83 μg/mL (AUC = 0.938, P < 0.000 1) and 2.021 21 μg/mL (AUC = 0.821, P < 0.000 1) for grade Ⅰ, Ⅱ and Ⅲ schistosomiasis-induced liver fibrosis. In addition, the accordance rates between the optimal diagnostic threshold of serum M2BP and B-ultrasound examinations for predicting grade Ⅰ, Ⅱ and Ⅲ schistosomiasis-induceed liver fibrosis were 69.23%, 85.71% and 71.43% (χ2 = 1.340, P > 0.05), and the overall Kappa consistency test showed moderate consistency [Kappa = 0.608, 95% CI: (0.428, 0.788); Z = 6.609, P < 0.000 1]. Conclusions Serum M2BP may serve as a potential biomarker for assessing moderate to advanced schistosomiasis-induced liver fibrosis; however, its diagnostic value for early-stage schistosomiasis-induced liver fibrosis remains limited.

Objective To investigate the current distribution and predict the future suitable habitats of Haemaphysalis longicornis in Nanjing City, so as to provide insights into control and early warning of ticks and management of tick-borne diseases in Nanjing City. Methods The electronic map of Nanjing City was obtained from the National Platform for Common GeoSpatial Information Services. The distribution of H. longicornis and the longitude and latitude of distribution points from 2022 to 2024 were obtained from centers for disease control and prevention across each district in Nanjing City. Climatic and environmental variable data in Nanjing City were captured from the Worldclim database. Initially, 19 bioclimatic variables in this database were selected, including annual mean temperature, mean diurnal range, isothermality, temperature seasonality, maximum temperature of the warmest month, minimum temperature of the warmest month, temperature annual range, mean temperature of the wettest quarter, mean temperature of the driest quarter, mean temperature of the warmest quarter, mean temperature of the coldest quarter, annual precipitation, precipitation of the wettest month, precipitation of the driest month, precipitation seasonality, precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter. The elevation and normalized difference vegetation index were obtained from Data Sharing Platform of the Center for Resources and Environmental Sciences, Chinese Academy of Sciences. Then, the distribution points of H. longicornis, elevation, vegetation index and 19 bioclimatic variables were loaded into the software MaxEnt 3.4.4 to evaluate and screen out the variables with a contribution rate of 1% and higher. ArcGIS 10.8.1 software was used to extract the elevation, vegetation index and 19 bioclimatic variables of the distribution points of H. longicornis for a correlation analysis. If the absolute value of the correlation coefficient was 0.8 and higher, the variable with the higher contribution was retained. The 2050 dataset of the BCCCSM2-MR atmospheric circulation model in the coupled model intercomparison project phase 6 (CMIP6) were obtained from the Worldclim database as climate data for 2050. Screened H. longicornis species data and environmental and climate data were loaded into the maximum entropy (MaxEnt) model with the software MaxEnt 3.4.4 for training and validation, and then, all data generated from the model were imported into the software ArcGIS 10.8.1 to generate raster data and yield the map pertaining to the distribution of H. longicornis risk in Nanjing City. The accuracy of the model was evaluated with a receiver operating characteristic (ROC) curve, and the predictive effect of the model was assessed with area under the ROC curve (AUC). The suitable habitats of H. longicornis were classified in Nanjing City with the software ArcGIS 10.8.1, and the areas of distribution of suitable habitats in various categories were recorded to create the map of current H. longicornis suitable habitats classification in Nanjing City. The climatic and geographic information data in 2050 were employed as future environmental and climatic factors, and current environmental and climatic factors and current H. longicornis distribution data were additionally used to predict the future suitable habitats of H. longicornis in Nanjing City. In addition, the contributions of environmental and climatic factors to distribution of suitable habitats of H. longicornis was evaluated with the Jackknife method in Nanjing City. Results A total of 10 environmental and climatic variables were screened for analysis of the suitability of H. longicornis in Nanjing City based on correlation analyses and contributions of the MaxEnt model, including annual mean temperature, precipitation of the warmest quarter, vegetation index, precipitation of the wettest month, temperature annual range, annual precipitation, mean temperature of the warmest quarter, elevation, mean temperature of the wettest quarter, and maximum temperature of the warmest month, and annual mean temperature (34.8%), precipitation of the warmest quarter (17.3%), vegetation index (13.1%), and precipitation of the wettest month (10.8%) contributed relatively highly to the distribution of suitable habitats of H. longicornis in Nanjing City. The mean AUC of the ROC curve was 0.810 ± 0.055 for 10 repeated modeling results of the MaxEnt model, indicating high predictive performance of the model. The potential distribution areas of H. longicornis were predicted to be mainly located in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District in Nanjing City with the MaxEnt model. Under current climatic conditions, the area of potential suitable habitats of H. longicornis was 4 182.42 km² in Nanjing City, including 1 252.94 km² highly suitable habitats, which accounted for 19.00% of the total area of Nanjing City. Under the climate scenario in 2050, the area of potential suitable habitats of H. longicornis was projected to increase to 5 467.58 km² in Nanjing City, accounting for 82.95% of the total area of the city, and these habitats were mainly concentrated in Luhe District, Pukou District, Jiangning District, Lishui District, and Gaochun District. The areas of suitable habitats of H. longicornis at various categories were predicted to vary greatly in 2050, and the area of highly suitable habitats of H. longicornis was projected to increase to 2 378.82 km², accounting for 36.08% of the total area of Nanjing City. Based on jackknife tests and contributions of environmental and climatic variables, 6 dominant environmental and climatic factors were screened, including annual mean temperature (34.8% contribution), precipitation of the warmest quarter (17.3% contribution), vegetation index (13.1% contribution), precipitation of the wettest month (10.8% contribution), temperature annual range (5.4% contribution), and mean temperature of the warmest quarter (5.0% contribution), with cumulative contributions of 86.4%. Conclusion The distribution of H. longicornis is strongly associated with vegetation, temperature and precipitation in Nanjing City. Future climate change may lead to an expansion of the distribution area of H. longicornis in Nanjing City.

Objective Objective To analyze the potential spatial factors affecting the genetic differentiation of Oncomelania hupensis robertsoni in Yunnan Province. Methods A total of 13 administrative villages were selected from schistosomiasis-endemic areas of Yunnan Province as O. hupensis snail sampling sites. At least 200 snails were collected in each site, and the spatial variable data of each site were recorded, including longitude, latitude and altitude. Thirty active and Schistosoma japonicum uninfected O. hupensis snails were selected from each sampling site by means of the crawling method and the cercarial shedding method. Genomic DNA was extracted from O. hupensis snails. Following PCR amplification, purification of PCR amplification products and sequencing, the gene sequences of O. hupensis snail samples were spliced and edited using the DNAstar software and the NCBI database to yield the complete mitochondrial sequences of O. hupensis snails at each sampling site, and the mitochondrial genetic distance matrix of O. hupensis robertsoni was calculated at each sampling site. The geographical coordinates of each sampling site were marked using the software ArcGIS 10.2, and the straight-line geographical distance between each sampling site was calculated. The altitude difference, longitude difference and latitude difference between each sampling site were calculated using the Excel software, and the correlation between the mitochondrial genetic distance matrix of O. hupensis robertsoni and each spatial variable matrix was examined by using the Mantel test at 13 sampling sites in Yunnan Province. Results Among the 13 O. hupensis snail sampling sites in Yunnan Province, the largest mitochondrial genetic distance of O. hupensis robertsoni snail populations was seen between Anding Village, Nanjian Yi Autonomous County and Caizhuang Village, Midu County (26.244 2), and the largest geographical distance was seen between Dongyuan Village, Gucheng District and Cangling Village, Chuxiong County (272.64 km). The highest altitude difference was seen between Anding Village, Nanjian Yi Autonomous County and Dongyuan Village, Gucheng District (1 086.10 m), and the largest longitude difference was found between Qiandian Village, Eryuan County and Cangling Village, Chuxiong County (1.86°), while the largest latitude difference was measured between Leqiu Village, Nanjian Yi Autonomous County and Dongyuan Village, Gucheng District (1.81°). In addition, the mitochondrial genetic distance of O. hupensis robertsoni snail populations was positively correlated with altitude at 13 snail sampling sites in Yunnan Province (r = 0.542 8, P < 0.001), and showed no significant correlations with geographical distance (r = 0.093 4, P > 0.05), longitude (r = −0.199 5, P > 0.05) or latitude (r = 0.205 7, P > 0.05). Conclusion Altitude may be a potential spatial factor affecting the genetic differentiation of O. hupensis robertsoni in Yunnan Province.

Objective To construct an index system for assessment of schistosomiasis transmission risk following natural disasters such as rainstorms, floods, earthquakes, mudslides, and landslides, so as to provide insights into rapid identification of schistosomiasis transmission risk post-disasters and formulation of targeted schistosomiasis control strategies. Methods An initial framework for the index system for assessment of schistosomiasis transmission risk following natural disasters was drafted through literature review, brainstorming, and focus group discussions. Two rounds of expert correspondence consultations were conducted using the Delphi method to refine and finalize the system, and the degrees of expert activeness, authority and endorse ment, and consensus were evaluated. In addition, the weights of each index were calculated using the analytic hierarchy process. Results A total of 18 experts participated in the consultation. The expert positive coefficients were 100.00% and 94.44% for two rounds of consultations, with authority coefficients of 0.92 and 0.94, respectively. The coefficients of coordination on the index importance, rationality and operability were 0.209, 0.185, 0.222 and 0.407, 0.214, 0.257 for two rounds of consultations, respectively, and all consistency tests were statistically significant (χ² = 246.771 to 505.278, all P values < 0.001). Following two rounds of expert consultations, an index system consisting of 6 first-level indicators, 15 second-level indicators, and 49 third-level indicators was ultimately constructed. In terms of first-level indicators, “disaster situation”, “previous epidemics”, “healthcare guarantee”, “response capacity” and “emergency recovery” had the highest weights, each at 18.18%. Regarding second-level indicators, “Schistosoma japonicum infections in animals”, “S. japonicum infections in snails” and “medical treatment” had the highest weights, each at 7.35%. In terms of third-level indicators, ten items had the highest weights, including “identification of schistosomiasis cases”, “detection of S. japonicum infections in wild feces”, “detection of S. japonicum infections in snails”, “reserves of schistosomiasis diagnostic/testing reagents and consumables”, “reserves of chemotherapy agents for human and animal schistosomiasis”, “reserves of cercariacides”, “periodical surveillance on schistosomiasis”, “identification of schistosomiasis transmission risk and timely response”, “normal provision of diagnosis and treatment services” and “post-disaster schistosomiasis surveillance”, each at 2.40%. Conclusion A scientific, systematic, and practical index system has been constructed for assessment of schistosomiasis transmission risk following natural disasters, which may provide insights into rapid post-disaster identification of schistosomiasis transmission risk, formulation of targeted schistosomiasis control strategies and optimization of resource allocation.

Medical parasitology, as a course bridging basic medical sciences and clinical medicine, has an important disciplinary value in the medical education system. This study investigated the composition of parasitology teachers from multiple medical colleges and universities across China. The results showed that there was a significant difference in the proportion of teachers with clinical medicine background knowledge, and there was common dilemma that there were insufficient clinical medicine knowledge reserves among teachers in some medical colleges and universities, who encountered severe teaching challenges. Based on this issue, this study constructed a basic-clinical medicine collaborative problem-based learning (PBL) teaching model. This model integrated theoretical teaching, case analyses, and experimental operations, and combined transdisciplinary team building and multidimensional teacher training, which significantly improved the clinical teaching capability among parasitology teachers, and effectively compensated the impact of insufficient clinical medicine knowledge reserves on teaching. Following teaching reform, students' scores significantly improved, and their case analysis capability enhanced. This study provides a practical path to address the shortage of clinical medicine background knowledge among parasitology teachers, which facilitates the progress of educational reform of medical parasitology and improvement of teaching quality.