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 investigate the distribution characteristics of Oncomelania hupensis snails in Yunnan Province fol-lowing interruption of schistosomiasis transmission, so as to provide the evidence for assessing the risk of schistosomiasis transmission and scientifically formulating the schistosomiasis surveillance program. Methods According to the requirements of the National Schistosomiasis Surveillance Scheme (2020 Edition), O. hupensis snail surveillance data were collected from 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province from 2020 to 2024, including area of snail survey, area of snail habitats, area of re-emerging snail habitats, number of frames surveyed, number of frames with O. hupensis snails, number of O. hupensis snails captured, and number of living snails, and the occurrence of frames with snails and mean density of living snails were calculated. Changes in snail status over the 5-year period from 2020 to 2024 and the differences in snail distributions specified by epidemic intensity, environmental type, and vegetation type were analyzed. Results The areas of snail survey increased from 1 727.96 hm² in 2020 to 3 894.45 hm² in 2024 (peak) across 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province during the period from 2020 through 2024. The areas of snail habitats increased from 70.36 hm² in 2020 to a peak in 2023 (172.04 hm²), followed by a reduction to 132.36 hm² in 2024, and the areas of re-emerging snail habitats increased from 42.71 hm² in 2020 to a peak in 2022 (78.43 hm²), followed by a reduction to 40.21 hm² in 2024. The occurrence of frames with snails and mean density of living snails increased from 1.24% (3 025/244 404) and (0.033 2 ± 0.038 7) snails/0.1 m² in 2020 to peaks at 2.03% (6 231/307 563) and (0.066 9 ± 0.068 4) snails/0.1 m² in 2023, followed by reductions to 1.04% (5 829/559 941) and (0.032 6 ± 0.057 7) snails/0.1 m² in 2024, respectively. There was a significant difference in the occurrence of frames with snails over the 5-year study period (χ² = 1 962.95, P < 0.05), and the occurrence of frames with snails reduced by 48.71% in 2024 relative to in 2023 (χ² = 1 411.05, P < 0.005); however, there was no significant difference in the mean density of living snails over the 5 years (H = 5.310, P > 0.05). There were significant differences in the occurrence of frames with snails (χ² = 481.27, P < 0.05) and mean density of living snails (H = 6.872, P < 0.05) in schistosomiasis-endemic areas with different epidemic intensities. The occurrence of frames with snails (χ² = 25.32 and 38.70, both P values < 0.017) and mean density of living snails (Z = 28.55 and 49.96, both P values < 0.017) were higher in schistosomiasis transmission-interrupted and eliminated areas with snails than in schistosomiasis-eliminated areas without snails, and the occurrence of frames with snails (χ² = 453.54, P < 0.017) and mean density of living snails (Z = −56.97, P < 0.017) were higher in schistosomiasis-eliminated areas with snails than in schistosomiasis transmission-interrupted areas with snails. O. hupensis snails were mainly distributed in paddy fields, dry farmlands and ditches; however, the occurrence of frames with snails (13.40%, 424/3 164) and mean density of living snails [(0.252 8 ± 0.158 7) snails/0.1 m²] were higher in ponds/weirs than in other types of environments (both P values < 0.05). Rice, dry farmland crops and weeds were main vegetations in which O. hupensis snails were distributed, and the occurrence of frames with snails (2.29%, 7 111/310 140) and mean density of living snails [(0.072 3 ± 0.018 9) snails/0.1 m²] were higher in weeds than in other types of environments (both P values < 0.05). Conclusions O. hupensis snails have been effectively controlled in Yunnan Province following implementation of integrated schistosomiasis control measures; however, there are still risk factors for schistosomiasis transmission, including reduced attention to schistosomiasis control and snail re-emergence. Improved control efforts and surveillance system construction and timely identification of risk factors of snail status and timely management are recommended to ensure the achievement of the target of schistosomiasis elimination as scheduled.

Objective To investigate the prevalence of Schistosoma japonicum infections in wild murines in schistosomiasisendemic areas of Yunnan Province, so as to provide the evidence for improving the schistosomiasis surveillance and early warning capability and implementing precise interventions in the province. Methods A total of 56 natural villages were sampled from 14 schistosomiasis-endemic counties (cities, districts) in Yunnan Province as surveillance sites from 2024 to 2025. Wild murines were captured using traps at night and characterized for species. Liver samples were collected from wild murines, S. japonicum eggs and miracidia were simultaneously detected using liver tissue compression microscopy, liver tissue homogenate microscopy, and liver tissue homogenate hatching. In addition, stool samples were collected from wild murine rectum, with S. japonicum eggs detected using a modified Kato-Katz thick smear technique and S. japonicum miracidia detected using the hatching test with nylon gauzes. Results A total of 15 577 valid murine traps were recovered, and 891 wild murines belonging to 16 species were captured, with a capture rate of 5.72%(891/15 577). The captured wild murines were predominantly districted in paddy fields, dry lands, ditches, ponds and marshlands, with Apodemus chevrieri, Rattus norvegicus, Crocidura attenuata, R. rattus sladeni, R. flavipectus, and Eothenomys miletus as dominant species. No S. japonicum eggs or miradicia were in laboratory; however, other parasites were identified in 25 wild murines. The prevalence rates of Schistosoma sinensis, Capillaria hepatica and Hymenolepis nana were 2.24% (20/891), 0.90% (8/891) and 0.56% (5/891), and the prevalence of S. sinensis and C. hepatica co-infection was 0.90% (8/891), respectively. Conclusions S. sinensis, C. hepatica and H. nana are predominant parasite species in wild murines in schistosomiasis-endemic areas of Yunan Province. No S. japonicum infection was detected during the study period; however, there is still a risk of wild murines as a potential source of infection for schistosomiasis transmission. Continuous intensified surveillance of S. japonicum infections in wild murines is a critical programme in schistosomiasis surveillance and early warning in Yunnan Province.

Objective To introduce the calculation of the DISO index in multi-dimensional data ranking for disease surveillance, and evaluate its applicability. Methods Using the data from the quality supervision and inspection of infectious disease reporting in Jingzhou City in a certain year as an example, the DISO index was used to rank 16 investigated medical institutions and compared with the TOPSIS method. The consistency between the two evaluation results was analyzed using Spearman correlation. Then, The DISO index from different medical institutions was used to evaluate the ranking of 8 counties (cities, districts). The evaluation indicators for the supervision and inspection included six items: report rate, timely report rate, completeness of report card filling, accuracy of report card filling, consistency rate with network information report, and completeness of valid ID number filling. Results Among the 16 medical institutions, 8 institutions had all six evaluation indicators at 100%. Using the two methods to rank the 16 medical institutions, the two methods showed that the 8 medical institutions with all six evaluation indicators at 100% were ranked first. There was a strong correlation between the rankings of the 16 medical institutions using the two methods (r_s=0.992, P=0.000). The three counties (cities, districts) with all six evaluation indicators at 100% were all ranked first, while country G was ranked seventh and country E was ranked eighth. Conclusion The results of the DISO index and the TOPSIS method were similar in one-dimensional ranking. DISO can be used for multi-dimensional ranking of disease surveillance data and deserves to be promoted and applied.

Objective To analyze the clinical and epidemiological characteristics of influenza A(H3N2) in Tangshan City, Hebei Province, from 2023 to 2025, and to provide a scientific basis for precise prevention and control of local influenza. Methods The influenza-like cases and influenza outbreak cases in the influenza surveillance sentinel hospitals in Tangshan City from January 2023 to December 2025 were included in this study. The clinical characteristics and epidemic characteristics of H3N2 influenza patients were analyzed by descriptive epidemiological methods and chi-square test. Results A total of 11 567 influenza case samples were tested in the study, with a positive rate of 9.34% (1 080/11 567). Influenza A (H3N2) remained the predominant circulating subtype throughout the study period. January 2023 to February 2023 marked the tailing end of the previous season’s H3N2. Then, a minor peak dominated by H1N1 occurred from March to June, and H3N2 circulated again from October to December, peaking in December. From September 2024 to December 2024, H1N1 and H3N2 circulated concurrently, with H3N2 as the dominant strain, peaking in January of the following year. From March 2025 to September 2025, influenza activity remained at low level, and activity began to rise in October, with H1N1 peaking in November before declining, and H3N2 becoming the dominant strain in December. H3N2 mainly occurred in the 5-14 years old group. Cases in the 0–4-year-old group were primarily characterized by gastrointestinal symptoms, with high fever being common. Cases in the 5–59-year-old group were primarily characterized by systemic and respiratory symptoms, with better clinical outcomes. Cases aged 60 and older exhibited the highest rates of hospitalization, severe illness, and incidence of complications. Conclusion From 2023 to 2025, the influenza virus in Tangshan City, Hebei Province is dominated by influenza A (H3N2). The epidemic is characterized by a peak incidence during autumn and winter, co-circulation with H1N1, and fluctuations during the peak period. It is necessary to pay attention to the clinical management and prevention and control of influenza A (H3N2) in infants and the elderly.

ObjectiveTo understand the development stages and use of policy tools of general practitioner policies in China since it was first proposed, to summarize the experience and explore the shortcomings, so as to provide references for the adjustment and optimization of China’s general practitioner policies. MethodsContent analysis and mathematical statistics analysis were used to conduct a quantitative research on 111 policy documents with 422 policy items involving general practitioners at the national level from 1997 to 2023, through a three-dimensional analysis framework integrating policy tools, human capital process and policy development stages. ResultsCapacity‑building policy tools were most frequently used in general practitioner policies, and the policy tools gradually shifted from mandate to inducement. The general practitioner policies paid less attention to the career selection link, but paid full attention to every segment of human capital links, with a comprehensive application of policy tools observed in the integrated development stage, despite the existence of unbalanced internal distribution. ConclusionIt is suggested to promote the use of incentive policy tools and to explore multiple approaches based on incentive theory; pay attention to the career selection link for guiding the employment of general practitioners; take the appropriateness between the policy tools and human capital process into comprehensive consideration, striking a dynamic balance of the internal structure of general practitioner policies.

ObjectiveTo analyze the characteristics of policy texts related to the physician periodic assessment system in China, providing references for the improvement of the system. MethodsContent analysis was employed, examining 116 policy documents from three dimensions: policy process, policy themes, and policy tools. ResultsA total of 298 codes were obtained. The number of policies related to the periodic assessment of physicians showed an overall trend of increasing first and then decreasing, with the peak annual issuance period between 2011 and 2021, and the average number of policy texts showing a downward trend. Policy documents were summarized into 3 levels: physician periodic assessment work, individual behavior, and institutional systems, encompassing a total of 8 categories of themes. The proportion of supply-oriented, environmental-oriented, and demand-oriented policy tools were 4.03%, 60.40%, and 35.57%, respectively. Moreover, environmental-oriented tools continued to dominate over time, followed by demand-oriented tools, with supply-oriented tools being the least. ConclusionThe policy themes are relatively broad and difficult to implement, focusing on establishing regulations while neglecting resource provision, and failing to continuously improve the construction of the system. It is recommended to clearly define the scope of the periodic assessment management, improve supporting systems, increase resource supply, and continuously promote the execution of assessments and policy revisions.

OBJECTIVE To explore the main factors influencing the blood concentration of duloxetine， and provide a scientific basis for the individualized use of duloxetine. METHODS Retrospective analysis was conducted on 434 inpatients with depressive disorders at the First Hospital of Hebei Medical University， who were treated with duloxetine and underwent blood concentration monitoring between January 2022 and April 2024. The study examined the impact of various factors， including gender， age， body mass index （BMI）， gene phenotypes， combined medication， drug type （original/generic）， and genotyping results of gene single nucleotide polymorphism loci， on blood concentration and the concentration-to-dose （C/D） after dose adjustment. RESULTS The blood concentration of duloxetine was 76.65 （45.57， 130.31） ng/mL， and C/D was 0.96 （0.63， 1.60） ng·d/（mL·mg）. The blood concentration of duloxetine was positively correlated with the daily dose of administration （R2＝0.253 7， P＜0.001）. Blood concentration of duloxetine in 38.94% of patients exceeded the recommended range specified in the guidelines. Gender， age， BMI， combined use of CYP2D6 enzyme inhibitors， and CYP2D6 and CYP1A2 phenotypes had significant effects on C/D of duloxetine （P＜0.05）. CONCLUSIONS The patient’s age， gender， BMI， combined medication， and genetic phenotypes are closely related to the blood concentration of duloxetine.

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.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.