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: In Pakistan, traditional medicines are an important component of the medical system, with numerous varieties and great demands. However, due to the scattered resources and the lack of systematic collection and collation, adulteration of traditional Pakistani medicine (TPM) is common, which severely affects the safety of their medicinal use and the import and export trades. Therefore, it is urgent to systematically organize and unify the management of TPM and establish a set of standards and operable methods for the identification of TPM. METHODS: We collected and organized the information on 128 TPMs with regard to their medicinal parts, efficacy, usage, and genetic material, based on Pakistan Hamdard Pharmacopoeia of Eastern Medicine: Pharmaceutical Codex. The genetic information of TPM is summarized from national center for biotechnology information (NCBI) and global pharmacopoeia genome database (GPGD). Furthermore, we utilized bioinformatics technology to supplement the chloroplast genome (cp-genome) data of 12 TPMs. To build the web server, we used the Linux + Apache + MySQL + PHP (LAMP) system and constructed the webpage on a PHP: Hypertext Preprocessor (PHP) model view controller (MVC) framework. RESULTS: We constructed a new genomic database, the traditional Pakistani medicine genomic database (TPMGD). This database comprises five entries, namely homepage, medicinal species, species identification, basic local alignment search tool (BLAST), and download. Currently, TPMGD contains basic profiles of 128 TPMs and genetic information of 102 TPMs, including 140 cytochrome c oxidase subunit I (COI) sequences and 119 mitochondrial genome sequences from Bombyx mori, 1 396 internal transcribed spacer 2 (ITS2) sequences and 1 074 intergenic region (psbA-trnH) sequences specific to 92 and 83 plant species, respectively. Additionally, TPMGD includes 199 cp-genome sequences of 82 TPMs. CONCLUSION TPMGD is a multifunctional database that integrates species description, functional information inquiry, genetic information storage, molecular identification of TPM, etc. The database not only provides convenience for TPM information queries but also establishes the scientific basis for the medication safety, species identification, and resource protection of TPM.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Objective: To evaluate the effect of remote health interventions on self-management of hypertension. Methods: We systematically searched the literature for studies published in English in PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Cochrane Central Register of Controlled Trials. The database was used to search for relevant studies with full text and evaluate the remote health interventions for hypertension self-management versus usual care for hypertension. RevMan 5.4 was used for data analysis. Results: A total of 19 studies eventually met our inclusion criteria. The results showed that the remote health interventions group could significantly reduce the levels of SBP (MD=5.67, 95% CI=4.12-7.22, p<.001) and DBP (MD=1.88, 95% CI=1.16- 2.60, p<.001), compared with usual care group, it also significantly improving the patient's quality of life (SMD=0.84, 95% CI=0.32- 1.37, p=.002), reduce waist circumference (MD=2.39, 95% CI=0.35-4.44, p=.020) and BMI (MD=0.49, 95% CI=0.06-0.91, p=.020), and significantly increasing the physical activity of patients (SMD=0.19, 95% CI=0.06- 0.31, p=.004). No obvious publication bias was found in this meta-analysis. Conclusion This study showed that remote health interventions for self-management can significantly improve patients’ quality of life with hypertension and better BP control than usual care. Further studies could be assess the long-term clinical effectiveness and economic evaluation of remote health interventions for self-management.

Objective: To evaluate the effect of remote health interventions on self-management of hypertension. Methods: We systematically searched the literature for studies published in English in PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Cochrane Central Register of Controlled Trials. The database was used to search for relevant studies with full text and evaluate the remote health interventions for hypertension self-management versus usual care for hypertension. RevMan 5.4 was used for data analysis. Results: A total of 19 studies eventually met our inclusion criteria. The results showed that the remote health interventions group could significantly reduce the levels of SBP (MD=5.67, 95% CI=4.12-7.22, p<.001) and DBP (MD=1.88, 95% CI=1.16- 2.60, p<.001), compared with usual care group, it also significantly improving the patient's quality of life (SMD=0.84, 95% CI=0.32- 1.37, p=.002), reduce waist circumference (MD=2.39, 95% CI=0.35-4.44, p=.020) and BMI (MD=0.49, 95% CI=0.06-0.91, p=.020), and significantly increasing the physical activity of patients (SMD=0.19, 95% CI=0.06- 0.31, p=.004). No obvious publication bias was found in this meta-analysis. Conclusion This study showed that remote health interventions for self-management can significantly improve patients’ quality of life with hypertension and better BP control than usual care. Further studies could be assess the long-term clinical effectiveness and economic evaluation of remote health interventions for self-management.

Objective: To evaluate the effect of remote health interventions on self-management of hypertension. Methods: We systematically searched the literature for studies published in English in PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Cochrane Central Register of Controlled Trials. The database was used to search for relevant studies with full text and evaluate the remote health interventions for hypertension self-management versus usual care for hypertension. RevMan 5.4 was used for data analysis. Results: A total of 19 studies eventually met our inclusion criteria. The results showed that the remote health interventions group could significantly reduce the levels of SBP (MD=5.67, 95% CI=4.12-7.22, p<.001) and DBP (MD=1.88, 95% CI=1.16- 2.60, p<.001), compared with usual care group, it also significantly improving the patient's quality of life (SMD=0.84, 95% CI=0.32- 1.37, p=.002), reduce waist circumference (MD=2.39, 95% CI=0.35-4.44, p=.020) and BMI (MD=0.49, 95% CI=0.06-0.91, p=.020), and significantly increasing the physical activity of patients (SMD=0.19, 95% CI=0.06- 0.31, p=.004). No obvious publication bias was found in this meta-analysis. Conclusion This study showed that remote health interventions for self-management can significantly improve patients’ quality of life with hypertension and better BP control than usual care. Further studies could be assess the long-term clinical effectiveness and economic evaluation of remote health interventions for self-management.

Objective: To evaluate the effect of remote health interventions on self-management of hypertension. Methods: We systematically searched the literature for studies published in English in PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Cochrane Central Register of Controlled Trials. The database was used to search for relevant studies with full text and evaluate the remote health interventions for hypertension self-management versus usual care for hypertension. RevMan 5.4 was used for data analysis. Results: A total of 19 studies eventually met our inclusion criteria. The results showed that the remote health interventions group could significantly reduce the levels of SBP (MD=5.67, 95% CI=4.12-7.22, p<.001) and DBP (MD=1.88, 95% CI=1.16- 2.60, p<.001), compared with usual care group, it also significantly improving the patient's quality of life (SMD=0.84, 95% CI=0.32- 1.37, p=.002), reduce waist circumference (MD=2.39, 95% CI=0.35-4.44, p=.020) and BMI (MD=0.49, 95% CI=0.06-0.91, p=.020), and significantly increasing the physical activity of patients (SMD=0.19, 95% CI=0.06- 0.31, p=.004). No obvious publication bias was found in this meta-analysis. Conclusion This study showed that remote health interventions for self-management can significantly improve patients’ quality of life with hypertension and better BP control than usual care. Further studies could be assess the long-term clinical effectiveness and economic evaluation of remote health interventions for self-management.

Objective: To evaluate the effect of remote health interventions on self-management of hypertension. Methods: We systematically searched the literature for studies published in English in PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Cochrane Central Register of Controlled Trials. The database was used to search for relevant studies with full text and evaluate the remote health interventions for hypertension self-management versus usual care for hypertension. RevMan 5.4 was used for data analysis. Results: A total of 19 studies eventually met our inclusion criteria. The results showed that the remote health interventions group could significantly reduce the levels of SBP (MD=5.67, 95% CI=4.12-7.22, p<.001) and DBP (MD=1.88, 95% CI=1.16- 2.60, p<.001), compared with usual care group, it also significantly improving the patient's quality of life (SMD=0.84, 95% CI=0.32- 1.37, p=.002), reduce waist circumference (MD=2.39, 95% CI=0.35-4.44, p=.020) and BMI (MD=0.49, 95% CI=0.06-0.91, p=.020), and significantly increasing the physical activity of patients (SMD=0.19, 95% CI=0.06- 0.31, p=.004). No obvious publication bias was found in this meta-analysis. Conclusion This study showed that remote health interventions for self-management can significantly improve patients’ quality of life with hypertension and better BP control than usual care. Further studies could be assess the long-term clinical effectiveness and economic evaluation of remote health interventions for self-management.

OBJECTIVES: To investigate the impact of hepatitis B virus (HBV) infection on oral microbiota and metabolites in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) and the underlying mechanisms. METHODS: This prospective study was conducted in 47 MAFLD patients complicated with chronic hepatitis B (CHB) and 48 MAFLD patients without CHB enrolled from November, 2023 to January, 2024. Fasting tongue coating samples were collected from the patients for analyzing microbial community structures and metabolites using high-throughput 16S rDNA sequencing and non-targeted metabolomics techniques, and their associations with clinical indicators and biological pathways were explored using correlation analysis and functional annotation. RESULTS: The levels of fasting blood glucose, total cholesterol (TC), gamma-glutamyl transferase (GGT), and severity of fatty liver were all significantly lower in MAFLD+CHB group than in MAFLD group. Microbiota analysis showed that the abundances of Patescibacteria (at the phylum level), Hydrogenophaga, and Absconditabacteriales (at the genus level) were significantly increased, while the abundance of Megasphaera was decreased in MAFLD+CHB group. The differential microbiota were significantly correlated with TC, GGT and low-density lipoprotein (r=-0.68‒0.75). Metabolomics analysis revealed that 469 metabolites (including lipids and amino acids) were upregulated and 2306 (including organic oxygen-containing compounds and phenylpropanoids) were downregulated in MAFLD+CHB group, for which KEGG enrichment analysis suggested abnormal activation of the linoleic acid metabolism and glycerophospholipid metabolism pathways. Correlation analysis between microbiota and metabolites indicated that Patescibacteria and Megasphaera, which were positively correlated with lipid metabolites and negatively with fatty acid metabolites, respectively, jointly affected glycolipid metabolism and oxidative stress pathways. CONCLUSIONS Compared to patients with MAFLD alone, MAFLD patients with concurrent chronic HBV infection showed lower levels in some lipid metabolism indicators and the degree of hepatic steatosis, accompanied by alterations in oral microbiota structure and metabolic profiles. The precise mechanisms involved require further investigation to be fully elucidated.
Humans ; Lipid Metabolism ; Prospective Studies ; Microbiota ; Hepatitis B, Chronic/microbiology* ; Male ; Female ; Adult ; Fatty Liver/microbiology* ; Middle Aged ; Mouth/microbiology* ; Metabolomics