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.

Real-world study (RWS), based on multi-source data from real medical environments, is gradually becoming an important supplement to traditional randomized controlled trials, and its application in the field of transfusion medicine is becoming increasingly widespread. This article systematically reviews the definition and methodological system of RWS, examines its application cases in clinical blood transfusion research, and discusses the advantages, limitations, and future research directions of RWS, aiming to provide a reference for evidence-based research in blood transfusion medicine.

Real-world study (RWS), based on multi-source data from real medical environments, is gradually becoming an important supplement to traditional randomized controlled trials, and its application in the field of transfusion medicine is becoming increasingly widespread. This article systematically reviews the definition and methodological system of RWS, examines its application cases in clinical blood transfusion research, and discusses the advantages, limitations, and future research directions of RWS, aiming to provide a reference for evidence-based research in blood transfusion medicine.

The genus jujube(Ziziphus jujuba Mill.)within the Rhamnaceae family encompasses numerous varieties,such as Ziziphus jujuba Mill.var.jujuba,Ziziphus jujuba var.inermis,and var.spinosa,etc.Among these,the jujube fructus has the most abundant cultivated variants across the country,including Ziziphus jujuba cv.Hamidazao and Ziziphus jujuba cv.Huanghetanzao.Jujube plants are rich in variety and are used for both medicinal and food purposes.Polysaccharides,one of the main active ingredients of jujube,are important medicinal components that contribute to its efficacy.Jujube polysaccharides have been found to promote hematopoiesis,exhibit antioxidant and anti-tumor activities,repair liver damage,regulate the immune system,and provide anti-inflammatory effects.By comprehensively summarizing and analyzing the literature on jujube polysaccharides from different varieties and origins,this paper reviews the potential mechanisms of action of jujube polysaccharides in exerting biological activities.It also summarizes the primary structural features,such as relative molecular mass,monosaccharide composition,glycosidic linkage,and the substituent modifications of jujube polysaccharides by sulfation,phosphorylation,carboxymethylation,selenization,and acetylation.This review aims to provide a reference for the research and development of jujube in the fields of innovative polysaccharide drugs and functional foods.

Traditional Chinese medicine(TCM)polysaccharides are active polysaccharides extracted from Chinese herbal medicines,many of which exhibit specific biological activities.Modern research has revealed that polysaccharide components extracted from plants,animals,and algae have a significant role in improving kidney injury.Currently,drug therapy is the primary treatment for kidney injury,with few reports on the use of TCM polysaccharides.This review explores the therapeutic effects and mechanisms of TCM polysaccharides on diabetic nephropathy,nephritis,kidney stones,hypertension-induced kidney injury,chemical toxin-induced kidney injury,and drug-induced kidney injury.Additionally,it discusses the prospects for the development of TCM polysaccharides in this field to provide a reference for further research.

Objective To investigate the capability for distinguishing between the morphology of Oncomelania hupensis and Tricula snails and its influencing factors among schistosomiasis control professionals in Yunnan Province, so as to evaluate the interference of Tricula snails with O. hupensis surveys. Methods O. hupensis and Tricula snails were sampled from 9 schistosomiasis-endemic counties (districts) in Yunnan Province. The capability for distinguishing between O. hupensis and Tricula snails was evaluated using online questionnaire surveys and field blind tests among schistosomiasis control professionals, and the proportions of correct judgment, misjudgment and missed judgment were calculated. Univariate and multivariate logistic regression models were created using the software SPSS 25.0, and factors affecting the proportion of correct judgments of O. hupensis snails were identified among schistosomiasis control professionals. Results Questionnaire surveys and field blind tests showed that the overall proportions of correct judgments of O. hupensis snails were 56.77% (2 305/4 060) and 68.28% (1 556/2 279) among schistosomiasis control professionals in Yunnan Province, respectively. Univariate logistic regression analysis of online questionnaire surveys identified gender [odds ratio (OR) = 1.244, 95% confidential interval (CI): (1.073, 1.441), P < 0.05], professional title [OR = 0.628, 1.741, 95% CI: (0.453, 0.871), (1.109, 2.734), both P < 0.05], working duration [OR = 0.979, 95% CI: (0.971, 0.987), P < 0.05] and classification of schistosomiasis epidemics in endemic foci [OR = 1.410, 0.293, 0.523, 95% CI: (1.103, 1.804), (0.237, 0.361), (0.416, 0.657), all P < 0.05] as factors affecting the proportion of correct judgments of O. hupensis snails among schistosomiasis control professionals in Yunnan Province, and multivariate logistic regression analysis after adjustments showed that the proportion of O. hupensis snail misjudgments was 1.179 times higher among male schistosomiasis control professionals than among females [OR = 1.179, 95% CI: (1.006, 1.382), P < 0.05], and 1.474 times higher among schistosomiasis control professionals in schistosomiasis-elimination areas with snails than in areas without snails [OR = 1.474, 95% CI: (1.145, 1.898), P < 0.05], and the proportions of missed judgments of O. hupensis snails were 0.284 [OR = 0.284, 95% CI: (0.225, 0.359), P < 0.05] and 0.523 times [OR = 0.523, 95% CI: (0.412, 0.664), P < 0.05] higher among schistosomiasis control professionals in transmission-interruption areas with snails and schistosomiasis-elimination areas with snails than in schistosomiasis-elimination areas without snails. Univariate logistic regression analysis of field blind tests showed age [OR = 2.381, 95% CI: (1.677, 3.381), P < 0.05], professional title [OR = 1.688, 95% CI: (1.103, 2.582), P < 0.05］, working duration [OR = 0.970, 95% CI: (0.956, 0.984), P < 0.05] and classification of schistosomiasis epidemics in endemic foci [OR = 0.262, 0.593, 95% CI: (0.188, 0.364), (0.420, 0.837), both P < 0.05] as factors affecting the proportion of correct judgments of O. hupensis snails among schistosomiasis control professionals in Yunnan Province, and multivariate logistic regression analysis after adjustments showed the proportions of missed judgments of O. hupensis snails were 0.263 [OR = 0.263, 95% CI: (0.176, 0.394), P < 0.05] and 0.604 times [OR = 0.604, 95% CI: (0.416, 0.875), P < 0.05] higher among schistosomiasis control professionals in transmission-interruption areas with snails and schistosomiasis-elimination areas with snails than in schistosomiasis-elimination areas without snails. Conclusions Schistosomiasis control professionals in Yunnan Province have a low accuracy rate for distinguishing between the morphology of O. hupensis and Tricula snails, and gender and classification of schistosomiasis epidemics in endemic foci are factors that affect their ability to distinguish. The presence of Tricula snails causes a high degree of interference with O. hupensis surveys in O. hupensis snail-infested areas of Yunnan Province. Reinforced training for distinguishing between O. hupensis and Tricula snails is required among schistosomiasis control professionals in Yunnan Province.

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.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.

Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8％(62 219/261 893),and gram-negative bacteria 76.2％(199 674/261 893).The most common species were E.coli(46.7％),E.faecium(10.4％),K.pneumoniae(9.8％),E.faecalis(8.7％),P.mirabilis(3.5％),P.aeruginosa(3.4％),SS.agalactiae(2.6％),and E.cloacae(2.1％).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2％,52.8％and 37.0％,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7％,18.5％,16.4％,and 40.3％,respectively.Lower than 10％of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90％of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was＜2％.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.

Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7％)and Enterococcus faecalis(41.5％).The strains were mainly isolated from urinary tract specimens(46.9％±2.6％),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6％).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1％and 1.3％,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7％in 2015 to 3.4％in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3％.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.