ObjectiveTo investigate the mechanism of Yangxin Tongmai Formula （养心通脉方， YTF） in trea-ting coronary heart disease with blood stasis syndrome based on DNA methylation. MethodsSeventy-two SD rats were randomly divided into a control group （n=12） and a modeling group （n=60）. The modeling group was subjected to a high-fat diet， intragastric administration of vitamin D3， and subcutaneous injection of isoprenaline to establish the rat model of coronary heart disease with blood stasis syndrome. Forty-one successfully modeled rats were then randomly allocated into model group， YTF low-， medium-， and high-dose groups， and the atorvastatin calcium group， with 8 rats in each group and 1 rat reserved. The YTF low-， medium-， and high-dose groups received YTF at 6， 12， and 18 g/（kg·d） by gavage， respectively. The atorvastatin calcium group received atorvastatin calcium tablets at 1.8 mg/（kg·d） by gavage. The control group and the model group received 0.9% sodium chloride injection at 4 ml/（kg·d） by gavage. All administrations were performed once daily for 3 weeks. Twenty-four hours after the last administration， serum lipid levels including total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， and high-density lipoprotein cholesterol （HDL-C）， myocardial enzymes including cardiac troponin T （cTnT）， creatine kinase MB （CK-MB）， and lactate dehydrogenase （LDH）， and inflammatory factors including interleukin-1β （IL-1β） and interleukin-10 （IL-10） were detected by ELISA. Pathological changes in myocardial tissue were observed via HE staining. Whole blood DNA methylation sequencing was used to analyze differential methylation gene expression among the control group， model group， and YTF high-dose group. Western Blotting was used to verify the protein levels of the key genes and downstream signaling pathways. ResultsCompared to the control group， the model group showed increased levels of TC， TG， LDL-C， cTnT， CK-MB， LDH， and IL-1β， along with decreased levels of HDL-C and IL-10 （P＜0.05 or P＜0.01）. Compared to the model group， all treatment groups exhibited decreased levels of TC， LDL-C， CK-MB， and LDH， along with increased IL-10 levels. Among these， the high-dose YTF group demonstrated superior efficacy in reducing cTnT levels compared to the other TCM groups （P＜0.05 or P＜0.01）. HE staining indicated that the YTF high-dose group ameliorated myocardial cell swelling， disordered arrangement， pyknosis， and disappearance of nuclei， thereby reducing myocardial cell damage. Whole blood DNA methylation sequencing identified 240 differentially methylated genes shared by the control group， model group， and YTF high-dose group， including 109 hypermethylated and 131 hypomethylated genes； eif2ak3 was identified as a key differentially methylated gene. Compared to the control group， the model group exhibited increased protein levels of eukaryotic translation initiation factor 2 alpha kinase 3 （eIf2ak3）， phosphorylated protein kinase RNA-like endoplasmic reticulum kinase （p-PERK）， activating transcription factor 4 （ATF4）， C/EBP homologous protein （CHOP）， and Bax， along with a decreased level of B-cell lymphoma-2 （Bcl-2） protein （P＜0.05 or P＜0.01）. Compared to the model group， the YTF high-dose group showed decreased protein levels of eIf2ak3， p-PERK， ATF4， CHOP， and Bax， and an increased level of Bcl-2 protein （P＜0.05 or P＜0.01）. ConclusionYTF may regulate key differentially methylated genes such as eIf2ak3 and the PERK/ATF4/CHOP signaling pathway， thereby inhibiting endoplasmic reticulum stress， reducing myocardial cell apoptosis， and exerting therapeutic effects in coronary heart disease blood stasis syndrome.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Aim To explore the key components and mechanisms of Lizhong decoction in treating rats with cold-damp diarrhea based on network pharmacology,molecular docking technology and animal experiments.Methods By literature review and database collec-tion,the components of Lizhong decoction,therapeutic targets,and the mapping with diarrhea disease targets were conducted to construct an intersection target pro-tein-protein interaction network for screening core tar-gets,and GO and KEGG pathway enrichment analysis was performed to build an"active component-target-pathway"network,followed by molecular docking vali-dation.Forty-eight rats were randomly divided into the normal control group(K),model group(DG),Lizhong decoction group(LZDG),and Pulsatilla decoction group(BTDG).Subsequently,a rat cold-damp diar-rhea model was established using Senna combined with low-temperature high-humidity environment,and the rats were intervened with Lizhong decoction and Pul-satilla decoction.HE staining was used to detect path-ological changes in intestinal tissue,ELISA was em-ployed to measure the levels of peripheral blood IL-6,IL-10,IL-1 β,and TNF-α,and western blot was used to determine the expression of colon tight junction pro-teins.Results Network pharmacology initially identi-fied 125 compounds in Lizhong decoction,5 186 drug target components,438 disease targets,and 60"drug-disease"shared targets.GO and KEGG enrichment a-nalysis showed that signaling pathways such as IL-17 and TNF were highly enriched.Molecular docking in-dicated that the core components of the drug had good binding activity with corresponding key targets.Liz-hong decoction could effectively improve the clinical symptoms of rats with cold-damp diarrhea,and com-pared with the DG group,the diarrhea rate,diarrhea in-dex,and other related indicators also gradually de-creased to normal levels.Compared with the DG group,the LZDG group showed reduced inflammation levels and a recovery in energy metabolism levels.Conclusion It can regulate targets such as MMP9 and IL-17 signaling pathways through multi-components like Calycosin and formononetin to exert its therapeutic effect on cold-damp diarrhea.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1％of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9％)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5％)was lower than that in inpatient children(31.5％).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1％)than in inpatient children(59.4％).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14％,11.7％,47.8％in outpatients,but 24.2％,20.6％,and 52.8％in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.