OBJECTIVE To explore a synergistic medication strategy integrating stage-specific modifications of Bushen huoxue decoction with hormone replacement therapy （HRT） for premature ovarian insufficiency （POI）. METHODS The connotation of Academician Tong Xiaolin’s “state-target differentiation and treatment” theory and the disease stage characteristics of POI were summarized. The latent stage， subclinical stage， clinical stage and exhaustion stage of POI were classified into corresponding core pathogenesis “states” and key objective indicator “targets”. Taking Bushen huoxue decoction as the basic prescription， its staged modification scheme and its sequential combination with HRT were analyzed. RESULTS & CONCLUSIONS The four stages of POI correspond to four pathogenic states respectively： kidney yin deficiency， kidney deficiency and blood stasis， heart-kidney disharmony， and deficiency of both yin and yang. Follicle-stimulating hormone （FSH）， anti-Müllerian hormone （AMH）， antral follicle count （AFC） and Kupperman score were taken as the evaluation targets of staging. Combined with the “state-target” characteristics of each stage， a medication principle based on Bushen huoxue decoction with syndrome differentiation modification and stage-adjusted administration was established： traditional Chinese medicine dominates in the latent stage， a sequential collaborative regimen is adopted in the subclinical stage， HRT is dominant supplemented by traditional Chinese medicine in the clinical stage， and integrated traditional Chinese and Western medicine is applied to consolidate the root cause in the exhaustion stage.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim To explore the mechanism of hispidu-lin in the treatment of cervical cancer by using network pharmacology and molecular docking methods and veri-fy it by in vitro experiments.Methods Cervical canc-er HeLa and SiHa cells were cultivated in vitro,and CCK-8 assay,cloning assay,scratch assay,transwell as-say,and flow cytometry were used to detect the effects of hispidulin on cell proliferation,migration,invasion,and apoptosis.SwissTarget Prediction was used to ob-tain predicted targets for hispidulin.Potential targets for cervical cancer were screened in GeneCards disease database.R software Venn package was used to obtain the intersection target genes of hispidulin and cervical cancer,STRING website and Cytoscape software were used to obtain protein-protein interaction(PPI)net-work,and the core targets were screened.The GEIPA data analysis platform was employed to analyze the dif-ferential gene expression levels of core targets in cervi-cal cancer.Gene Ontology(GO)and Kyoto Encyclo-pedia of Genes and Genomes(KEGG)enrichment a-nalysis were performed,and molecular docking was car-ried out on key targets.Western blot was used to detect the regulatory effects of hispidulin on the expression of key proteins PI3K,p-Akt,as well as core target pro-teins MMP9 and RARP1 in the PI3K/Akt signaling pathway.Results Cell experiments showed that after treatment with hispidulin,the proliferation and colony formation abilities of HeLa and SiHa cells significantly decreased in a concentration-and time-dependent man-ner.At the same time,the lateral and longitudinal mi-gration and invasion abilities of HeLa cells decreased,and the level of apoptosis significantly increased.A to-tal of 87 intersection targets between hispidulin and cervical cancer were obtained,and eight core targets,namely,Akt1,EGFR,SRC,ESR1,PTGS2,GSK3β,MMP9,and PARP1,were selected based on the degree values in network topology analysis.KEGG enrichment screening identified PI3K/Akt signaling pathway,canc-er pathway,and other signaling pathways.The molecu-lar docking results showed that hispidulin had strong affinity activity with AktⅠ,P13K,MMP9,and RARP1.Western blot results showed downregulation of PI3K,p-Akt expression,as well as MMP9 and RARP1 expres-sion.Conclusions Hispidulin can inhibit the prolif-eration,migration,invasion,and promote apoptosis of cervical cancer cells by downregulating the PI3K/Akt signaling pathway and the expression of MMP9 and RARP1.

Objective To provide a scoping review of studies on the application of intelligent healthcare for continuity management in liver transplant patients,in order to provide a reference for the future development of intelligent liver transplant management.Methods A systematic search of relevant studies was conducted in PubMed,Embase,CINAHL,Web of Science,Cochrane Library,Scopus,CNKI,Wanfang Database,VIP Database,and CBM from the establishment of the databases to 30 September 2024.Following this search,the included literature was screened,summarized,and analyzed.Results A total of 19 papers were included.The intervention forms included application,intelligent medical device,and remote rehabilitation platform.The content elements included transplant health management,rehabilitation program planning,health data monitoring,telemedicine consultation,psychosocial support,and follow-up supervision.The evaluation indicators included physiological indicators,self-management ability,health-related quality of life,transplantation outcomes,patient satisfaction,and feasibility indicators.Conclusion The application of intelligent healthcare in the continuity management of liver transplantation patients is feasible and effective,and it is recommended that healthcare professionals integrate the diverse practice forms of intelligent healthcare,deepen the content elements and refine the evaluation indicator system based on the actual clinical situation,in order to improve the quality of the continuity management of liver transplantation patients.

Aim To study the mechanism of adipose mesenchymal stem cell exosomes(ASC-exo)inhibition of fluorescein isothiocyanate(FITC)-induced atopic dermatitis(AD).Methods The mouse age,extrac-tion method,and the concentration of a solution of typeⅠ collagen enzyme and other conditions were compared to study the effects on the morphology and quantity of adipose mesenchymal stem cells(ASCs)after extrac-ted.FITC-induced mouse model in vivo was estab-lished and different doses of ASC-exo were given to measure ear thickness,ear weight and ear scratching times of mice.HE staining was used to observe the pathological changes of ear tissue of mice.The non-toxicity of ASC-exo was detected.IgE,IL-5,IL-13 and other cytokines were detected by ELISA.The gene ex-pressions of TSLP,IL-33,occludin,Claudin-1(CLDN-1)and E-cadherin were detected by RT-qPCR.The protein expression was detected by immunohistochemis-try.Results An efficient method for extracting ASCs was established.Compared with the blank group,mice in the model group showed obvious AD symptoms.Compared with the model group,ASC-exo administra-tion group significantly reduced the number of ear scratches,epidermal thickening,inflammatory cell infil-tration and the secretion of Th2 cytokines IL-5 and IL-13.Meanwhile,ASC-exo administration group signifi-cantly increased the expression of structural proteins CLDN-1 and occludin in epithelial cells and decreased the expression of TSLP and IL-33.Conclusions ASC-exo can significantly improve Th2 skin inflamma-tion in AD mice,and its mechanism may be through in-creasing the expression of tight junction proteins and adhesion link protein in epithelial cells,repairing the skin barrier,and inhibiting the key promoters of allergy TSLP and IL-33.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

This study was aimed at exploring novel auxiliary diagnostic biomarkers for brucellosis and their potential miR-NA-mRNA regulatory networks.High-throughput sequencing was used to compare miRNA expression differences in serum ex-osomes between patients with brucellosis and healthy controls.Subsequently,RT-qPCR was used to validate the expression of significantly upregulated exosomal miRNAs.The diagnostic value of these miRNAs was assessed with ROC curves,and bioin-formatics analyses were performed to investigate the potential roles of the miRNAs in brucellosis infection.The ROC curve a-nalysis indicated that the area under the curve for exosomal hsa-miR-11400(P＜0.05),hsa-miR-199a-5p(P＜0.05),and hsa-miR-148a-5p(P＜0.05)was 0.79,0.81,and 0.74,respectively.A total of 465 differentially expressed miRNAs and their tar-get genes were predicted,including 25 immune-related target genes,most of which were closely associated with cancer-related proteoglycans,NF-kappa B signaling pathways,and IL-17 signaling pathways.The constructed differentially expressed gene network indicated that the immune genes PLXNA2,IL17RA,PRKCA,CD22,ACVR1B,and CBL might be regulated by hsa-miR-199a-5p and hsa-miR-148a-5p.These findings suggest that exosomal miRNAs might serve as auxiliary diagnostic indicators for brucellosis.Our exosomal miRNA-mRNA regulatory network provides new insights into the pathogenesis and treatment of brucellosis.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.