The digitization of traditional Chinese medicine (TCM) has generated vast amounts of data. However, these data are characterized by significant heterogeneity and complex semantic structures, posing substantial challenges for systematic integration and intelligent analysis, and limiting its potential for modern clinical and computational research. To address the challenges posed by the high heterogeneity and complex structure in TCM data, we designed and developed the TCM Data Hub platform, which is powered by the YiYuan large language models (LLMs). This platform aims to enhance intelligent data processing capabilities and unlock the potential for clinical application of TCM data through systematic integration and efficient utilization, thereby bridging the gap between traditional knowledge and modern computational research. This study first analyzed the heterogeneity and complexity of TCM information with respect to data types, structures, and semantics. A standardized data framework was constructed to enhance data integration and interoperability. Based on the TCM Intelligent Computing Platform of the China Academy of Chinese Medical Sciences, we trained the YiYuan LLMs to acquire domain-specific semantic understanding of TCM, thereby improving the platform’s comprehension of specialized terminology and knowledge systems. Leveraging the natural language processing capabilities of the LLMs, we developed a human-in-the-loop data processing system to enable efficient extraction, cleansing, and structured organization of TCM data. In addition, utilizing Vue and Java technologies, we developed multiple LLM-powered intelligent agents and systems, including a human-in-the-loop data processing system, as well as automated prescription mining and network pharmacology analysis agents. Task-specific agents tailored to TCM data processing were developed to enhance the model’s effectiveness in clinical knowledge discovery. System functionality and platform infrastructure were implemented using Java and Vue technologies.The TCM Data Hub platform has completed system construction and core functionality implementation. It supported integrated management and efficient access to 8 key types of TCM data: prescriptions, materia medica, ingredients, targets, diseases (Western medicine), diseases (TCM), syndromes, and therapeutic methods. The human-in-the-loop data processing system achieved an accuracy of 95.34% in structuring TCM data and supported annotation for data requiring manual labeling. The intelligent agent-driven big-data analytics module enabled 1-click, end-to-end workflows for TCM prescription mining, herb-syndrome association analysis, network pharmacology, and molecular biology research, completing a full data mining task in approximately 30 minutes. Users can interact with and manipulate data through a visual front-end interface. The system demonstrated stable performance, strong scalability, and a user-friendly experience. Empowered by the YiYuan LLMs, the TCM Data Hub platform significantly improves the accessibility, usability, and intelligence of TCM data. It effectively bridges traditional TCM knowledge with modern intelligent technologies, providing robust data support and intelligent tools for TCM research and clinical applications.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

This study was aimed at tracing the molecular typing and drug resistance characteristics of a suspected food poi-soning event caused by Clostridium perfringens in a district of Wuhan City.The FilmArray detection system and multiple fluo-rescence quantitative PCR methods were used to rapidly screen for pathogens in samples from the poisoning event.According to the initial screening results,bacteria were isolated,cultured,and identified by mass spectrometry.Fluorescence PCR was used to detect six virulence genes of the isolated Clostridium perfringens strains.On the basis of whole genome sequencing results,we conducted virulence genes,resistance genes,and whole genome single nucleotide polymorphism genetic evolution(wgSNPs)analyses.Antibiotic sensitivity testing was conducted with the agar dilution method.A total of ten strains of Clos-tridium perfringens were isolated,including eight strains from seven anal swab samples,one strain from fecal samples,and one strain from food samples.Food with suspected contamination had a Clostridium perfringens count of 7.8×106 CFU/g.The PLC(a)toxin gene was detected in all ten gas producing capsule isolation strains,but no other 5 tox-in genes such as CPE were detected,thus confirming that all were type A bacteria producing capsule Clostridium.All strains were 100％resistant to clindamycin and almost completely sensitive to antibiotics such as vancomycin,cefoxitin,and meropenem.Ten strains of Clos-tridium perfringens carried resistance genes such as tetB(P),tetA(P),and mprF,followed by ermQ(70％),ant(6)-Ⅰb(10％),and LnuP(10％).Genetic evolution analysis of wgSNPs indicated that the four outbreak strains clustered together and belonged to an independent subbranch with the suspected food sourcestrains,thus indicating close genetic relationships.In con-clusion,this food poisoning incident might have been be caused by hand torn chickens contaminated with Clostridium perfrin-gens,and the molecular types of the strains revealed high genetic diversity.No multiple drug resistance was observed,but all strains were resistant to clindamycin,an aspect requiring further clinical attention.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Rare diseases are a collective term for diseases with extremely low prevalence and incidence rates.Up to now,China has released two lists identifying a total of 207 rare diseases.Given that most rare diseases do not have drugs with corresponding indications,physicians frequently resort to using off-label drugs when treating patients with rare diseases.However,there is currently no systematic guideline or expert consensus for the use of off-label medications in China.To comprehensively collect existing evidence of off-label drug use for rare diseases,fully analyze and evaluate the rationality of off-label drug use for rare diseases,and standardize the management of off-label drug use for rare diseases,the Rare Disease Branch of Beijing Medical Association,Chinese Pharmaceutical Association,Beijing Pharmaceutical Association,and the School of Public Health,Peking University have jointly initiated the drafting of the Expert Consensus on Off-label Use of Drugs for Rare Diseases.This consensus refer to the WHO Handbook for Guideline Development,the Guidelines for Developing/Revising Clinical Diagnostic and Treatment Guidelines in China(2022 Edition),the AGREE Ⅱ and the STAR tools.This protocol outlines the background and purpose of consensus,as well as the comprehensive framework for consensus development,encompassing panel formation,clinical issue identification,evidence retrieval,data extraction,and evidence-based recommendation formulation.

Rare diseases are a collective term for diseases with extremely low prevalence and incidence rates.Up to now,China has released two lists identifying a total of 207 rare diseases.Given that most rare diseases do not have drugs with corresponding indications,physicians frequently resort to using off-label drugs when treating patients with rare diseases.However,there is currently no systematic guideline or expert consensus for the use of off-label medications in China.To comprehensively collect existing evidence of off-label drug use for rare diseases,fully analyze and evaluate the rationality of off-label drug use for rare diseases,and standardize the management of off-label drug use for rare diseases,the Rare Disease Branch of Beijing Medical Association,Chinese Pharmaceutical Association,Beijing Pharmaceutical Association,and the School of Public Health,Peking University have jointly initiated the drafting of the Expert Consensus on Off-label Use of Drugs for Rare Diseases.This consensus refer to the WHO Handbook for Guideline Development,the Guidelines for Developing/Revising Clinical Diagnostic and Treatment Guidelines in China(2022 Edition),the AGREE Ⅱ and the STAR tools.This protocol outlines the background and purpose of consensus,as well as the comprehensive framework for consensus development,encompassing panel formation,clinical issue identification,evidence retrieval,data extraction,and evidence-based recommendation formulation.

Objective To explore the palliative care needs of peritoneal dialysis patients and their care-givers,providine a scientific basis for the targeted implementation of palliative care intervention programs.Methods A semi-structured in-depth interview was conducted on 14 patients and 6 caregivers at a peritoneal dialysis center in a third-class hospital in Beijing.We used the Colaizzi seven-step data analysis method and the phenomenological method of qualitative research to analyze the data and refine the theme.Results Through the analysis,5 theme groups were summarized,including the need to alleviate physiological and psychological symptoms,the need for family support and help,the need for social help,the lack of understanding of pallia-tive care,and the lack of scientific view of death.Conclusions Peritoneal dialysis patients and their caregiv-ers have a strong need for palliative care.Medical staff should fully respect the uniqueness of peritoneal dialysis patients and implement targeted palliative care interventions based on their needs and preferences,so as to ef-fectively improve their quality of life.

This study was aimed at tracing the molecular typing and drug resistance characteristics of a suspected food poi-soning event caused by Clostridium perfringens in a district of Wuhan City.The FilmArray detection system and multiple fluo-rescence quantitative PCR methods were used to rapidly screen for pathogens in samples from the poisoning event.According to the initial screening results,bacteria were isolated,cultured,and identified by mass spectrometry.Fluorescence PCR was used to detect six virulence genes of the isolated Clostridium perfringens strains.On the basis of whole genome sequencing results,we conducted virulence genes,resistance genes,and whole genome single nucleotide polymorphism genetic evolution(wgSNPs)analyses.Antibiotic sensitivity testing was conducted with the agar dilution method.A total of ten strains of Clos-tridium perfringens were isolated,including eight strains from seven anal swab samples,one strain from fecal samples,and one strain from food samples.Food with suspected contamination had a Clostridium perfringens count of 7.8×106 CFU/g.The PLC(a)toxin gene was detected in all ten gas producing capsule isolation strains,but no other 5 tox-in genes such as CPE were detected,thus confirming that all were type A bacteria producing capsule Clostridium.All strains were 100％resistant to clindamycin and almost completely sensitive to antibiotics such as vancomycin,cefoxitin,and meropenem.Ten strains of Clos-tridium perfringens carried resistance genes such as tetB(P),tetA(P),and mprF,followed by ermQ(70％),ant(6)-Ⅰb(10％),and LnuP(10％).Genetic evolution analysis of wgSNPs indicated that the four outbreak strains clustered together and belonged to an independent subbranch with the suspected food sourcestrains,thus indicating close genetic relationships.In con-clusion,this food poisoning incident might have been be caused by hand torn chickens contaminated with Clostridium perfrin-gens,and the molecular types of the strains revealed high genetic diversity.No multiple drug resistance was observed,but all strains were resistant to clindamycin,an aspect requiring further clinical attention.

Objective To explore the palliative care needs of peritoneal dialysis patients and their care-givers,providine a scientific basis for the targeted implementation of palliative care intervention programs.Methods A semi-structured in-depth interview was conducted on 14 patients and 6 caregivers at a peritoneal dialysis center in a third-class hospital in Beijing.We used the Colaizzi seven-step data analysis method and the phenomenological method of qualitative research to analyze the data and refine the theme.Results Through the analysis,5 theme groups were summarized,including the need to alleviate physiological and psychological symptoms,the need for family support and help,the need for social help,the lack of understanding of pallia-tive care,and the lack of scientific view of death.Conclusions Peritoneal dialysis patients and their caregiv-ers have a strong need for palliative care.Medical staff should fully respect the uniqueness of peritoneal dialysis patients and implement targeted palliative care interventions based on their needs and preferences,so as to ef-fectively improve their quality of life.