Traditional Chinese medicine （TCM）， through its multi-target and systematic regulatory effects， has demonstrated unique advantages in the treatment of gastric precancerous lesions （GPL）. At present， TCM theoretical research on GPL is mainly reflected in three aspects， the integration of macroscopic syndrome differentiation， the inflammation-carcinoma transformation mechanism， as well as the systematization and scientization of theoretical inheritance from famous TCM practitioners. High-quality evidence-based research findings serve as the foundation for clinical practice guidelines on GPL， and TCM has gained international academic recognition in the field of GPL prevention and treatment. Research on TCM mechanisms has yielded a series of important outcomes in the aspects of signaling pathways， gene expression regulation， cellular epigenetics， histone modification， and intestinal microecology. It is proposed that future research on GPL should focus on four key directions， establishing multi-omics data， exploring targeted intervention strategies on key regulatory nodes， advancing the standardization process of integrated traditional Chinese and western medicine prevention and treatment technologies， and constructing stratified screening and intervention platforms. The in-depth integration of TCM microcosmic mechanism of action with its macroscopic syndrome differentiation and treatment system， coupled with interdisciplinary research， will provide valuable references for the clinical treatment and scientific research of GPL.

Objective The locally weighted ensemble based spatial domain identification(LWESDI)method is proposed to explore its application in spatial domain identification in breast cancer spatial transcriptomics data.Methods The LWESDI method is applied to integrate the spatial domain identification results from four methods:BayesSpace,BASS,SpaGCN,and STAGATE,which are used for breast cancer.A locally weighted co-association matrix is constructed by combining the weighted similarity between spots.Obtain a consistent spatial domain identification result by iteratively merging the regions with the highest similarity.Subsequently,differential analysis is performed on the selected highly variable genes,followed by GO enrichment analysis of the differential genes.Results The LWESDI method accurately identifies 20 spatial domains in breast cancer tissue,outperforming the four base clustering methods in terms of accuracy and robustness.The top 3000 highly variable genes(HVGs)were selected,and GO enrichment analysis was performed on the 19 most significantly differentially expressed genes in breast cancer,resulting in 33 enriched GO terms.Conclusion The LWESDI method provides a new strategy for spatial domain identification.The selected potential biomarkers for breast cancer will offer potential therapeutic targets for the study of breast cancer heterogeneity and personalized treatment.

Objective To identify subtypes of lower-grade gliomas based on multi-omics data integration with consensus clustering ensemble(MICCE)method,and further assess prognosis risk across different subtypes and explore differentially expressed biomarkers and pathways.Methods We applied the consensus clustering ensemble method to integrate the subtype results of seven multi-omics data integration methods(SNF,joint SNF,CIMLR,ConsensusClusterPlus,MoCluster,NEMO,iClusterBayes)for mRNA,miRNA,and DNA methylation data from LGG patients,identifying a robust molecular subtyping.Then we performed survival analysis based on the subtype results,and Cox proportional risk models were fitted to assess the prognosis of patients with different subtypes.Differentially expressed genes(DEmiRNAs,DEmRNAs and DMGs)between different subtypes were screened,and GO(gene ontology)analysis and KEGG enrichment analysis were performed for overlapping genes among DEmiRNAs target genes,DEmRNAs,and DMGs.Ultimately,immune infiltration analysis and pathway activity analysis were conducted to quantify the biological differences among different subtypes.Results Patients were classified into three subtypes:a high-risk cluster,a moderate-risk cluster,and a low-risk cluster.The results showed that the high-risk cluster were 7.70 times more likely to die than patients in low-risk cluster.A total of 2512 DEmRNAs,14 DEmiRNAs and 255 DMGs were screened,the combined analysis genes yielded 665 genes which are regulated by mRNA,miRNA and DNA methylation and enriched 62 GO items and 52 KECG pathways with statistical differences.The analysis of immune infiltration and pathway activity indicates that there are two immune cells and four signaling pathways with statistically significant differences.Conclusion MICCE can effectively identify high-risk patients of LGG.Subsequent analysis reveals differential genes and pathways related to the progression of LGG with different subtypes,providing important clues for the personalized treatment of LGG.

Objective The locally weighted ensemble based spatial domain identification(LWESDI)method is proposed to explore its application in spatial domain identification in breast cancer spatial transcriptomics data.Methods The LWESDI method is applied to integrate the spatial domain identification results from four methods:BayesSpace,BASS,SpaGCN,and STAGATE,which are used for breast cancer.A locally weighted co-association matrix is constructed by combining the weighted similarity between spots.Obtain a consistent spatial domain identification result by iteratively merging the regions with the highest similarity.Subsequently,differential analysis is performed on the selected highly variable genes,followed by GO enrichment analysis of the differential genes.Results The LWESDI method accurately identifies 20 spatial domains in breast cancer tissue,outperforming the four base clustering methods in terms of accuracy and robustness.The top 3000 highly variable genes(HVGs)were selected,and GO enrichment analysis was performed on the 19 most significantly differentially expressed genes in breast cancer,resulting in 33 enriched GO terms.Conclusion The LWESDI method provides a new strategy for spatial domain identification.The selected potential biomarkers for breast cancer will offer potential therapeutic targets for the study of breast cancer heterogeneity and personalized treatment.

Objective To identify subtypes of lower-grade gliomas based on multi-omics data integration with consensus clustering ensemble(MICCE)method,and further assess prognosis risk across different subtypes and explore differentially expressed biomarkers and pathways.Methods We applied the consensus clustering ensemble method to integrate the subtype results of seven multi-omics data integration methods(SNF,joint SNF,CIMLR,ConsensusClusterPlus,MoCluster,NEMO,iClusterBayes)for mRNA,miRNA,and DNA methylation data from LGG patients,identifying a robust molecular subtyping.Then we performed survival analysis based on the subtype results,and Cox proportional risk models were fitted to assess the prognosis of patients with different subtypes.Differentially expressed genes(DEmiRNAs,DEmRNAs and DMGs)between different subtypes were screened,and GO(gene ontology)analysis and KEGG enrichment analysis were performed for overlapping genes among DEmiRNAs target genes,DEmRNAs,and DMGs.Ultimately,immune infiltration analysis and pathway activity analysis were conducted to quantify the biological differences among different subtypes.Results Patients were classified into three subtypes:a high-risk cluster,a moderate-risk cluster,and a low-risk cluster.The results showed that the high-risk cluster were 7.70 times more likely to die than patients in low-risk cluster.A total of 2512 DEmRNAs,14 DEmiRNAs and 255 DMGs were screened,the combined analysis genes yielded 665 genes which are regulated by mRNA,miRNA and DNA methylation and enriched 62 GO items and 52 KECG pathways with statistical differences.The analysis of immune infiltration and pathway activity indicates that there are two immune cells and four signaling pathways with statistically significant differences.Conclusion MICCE can effectively identify high-risk patients of LGG.Subsequent analysis reveals differential genes and pathways related to the progression of LGG with different subtypes,providing important clues for the personalized treatment of LGG.

This study used the Appraisal of Guidelines Research & Evaluation-Health Systems （AGREE-HS） to demonstratively compare 34 global coronavirus disease-2019 （COVID-19） health systems guidance documents （HSGs） and 6 World Health Organization （WHO） standard HSGs. The comparison involved topic， participants， methods， recommendations， and implementability， with the aim of exploring the characteristics of emergency HSGs. The results showed that the emergency HSGs had an overall average score of 49%， with topic having the highest score， recommendations having the second highest score， and participants having the lowest score. The standard HSGs had an overall average score of 79%， with high scores in all items. The emergency HSGs had lower scores in participants， methods， recommendations， and implementability than the standard HSGs （P<0.001）， while the COVID-19 emergency HSGs developed by the WHO had higher score in topic than the standard HSGs （P<0.05）. Compared with those released by countries， the COVID-19 emergency HSG developed by the WHO showed superiority in all items and overall scores （P=0.000 2）. This indicates that emergency HSGs， represented by the COVID-19 emergency HSG， place equal emphasis on topic and recommendations as standard HSGs but have low requirements in terms of expert participation， evidence support， and comprehensive consideration in the time- and resource-limited context. They have the characteristics of prominent topics， clear purposes， orientation to demand， keeping up with the latest evidence， flexible adjustment， and timeliness， emphasizing immediate implementation effects， weakening long-term effects， and focusing on comprehensive benefits. Additionally， developers， types， and report completeness are important influencing factors.

Objective To evaluate the risk of bias and reporting quality in randomized controlled trials(RCTs)of the Chinese medicine injection for acute cerebral infarction in the last five years.Methods RCTs literature on Chinese medicine injection in the treatment of acute cerebral infarction was systematically searched in CNKI,Wanfang Data,VIP,China Biology Medicine Database(CBM),PubMed,Embase and Cochrane Library from April 20,2018 to April 20,2023.The risk of bias and reporting quality of included RCTs were evaluated using the Cochrane Risk of Bias Tool(ROB 1.0)and CONSORT-CHM Formulas 2017,respectively.Results A total of 4 301 articles were retrieved,and 408 RCTs were included according to inclusion and exclusion criteria.The ROB evaluation results showed that the the majority of studies were rated as having an unclear risk of bias due to the lack of reporting on allocation concealment,blind method,trial registration information,and funding sources.The evaluation results of CONSORT-CHM Formulas 2017 showed that the number of reported papers of 17 items was greater than or equal to 50%,and the number of reported papers of 25 items was less than 10%,and most of the RCTs did not show the characteristics of TCM syndrome differentiation and treatment.Conclusion The quality of Chinese medicine injection in the treatment of acute cerebral infarction RCTs is generally low.It is recommended that researchers refer to the methodology design of RCTs and international reporting standards,improve the trial design,standardize the trial report,and highlight the characteristics of TCM syndrome differentiation and treatment.

ObjectiveTo investigate the identification of kidney Yang deficiency syndrome of patients with osteoporosis（OP）， and to form the clinical syndrome identification rules of traditional Chinese medicine（TCM）. MethodBasic information， etiology， clinical symptoms and other characteristics of 982 OP patients were included， and statistical tests were used to screen the variables associated with kidney Yang deficiency syndrome. Taking the decision tree as the base model， bootstrap aggregation algorithm（Bagging algorithm） was utilized to establish the classification model of kidney Yang deficiency syndrome in OP， generating numerous rules and removing redundancy. Combining least absolute shrinkage and selection operator（LASSO） regression to screen key rules and integrate them to construct an identification model， achieving the identification of kidney Yang deficiency syndrome in OP patients. ResultEighteen key identification rules were screened out， and of these， where 11 rules with regression coefficients>0 correlated positively with the kidney Yang deficiency syndrome， the rule with the highest coefficient was chilliness（present）&feverish sensation over the palm and sole（absent）. The other 7 rules with regression coefficients<0 correlated negatively with the syndrome， the rule with the lowest coefficient was reddish tongue（present）&diarrhea（absent）&deficiency of endowment（absent）. According to the regression coefficients of each key rule， variables with importance>0.2 were ranked as chilliness， reddish tongue， feverish sensation over the palm and sole， cold limbs， clear urine， diarrhea， deficiency of endowment， prolonged illness. The results of the partial dependence analysis of the identification model showed that compared to OP patients without chilliness， those with chilliness（present） had a 0.266 8 higher probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identification of the syndrome. Similarly， compared to OP patients without reddish tongue， those with reddish tongue had a 0.141 9 lower probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identifying non-kidney Yang deficiency syndrome. The accuracy， sensitivity， specificity and area under receiver operating characteristic curve（AUC） of the established kidney Yang deficiency syndrome identification model in the test set were 0.865 9， 0.853 7， 0.872 0 and 0.931 5， respectively. ConclusionA precise identification model of OP kidney Yang deficiency syndrome is conducted basing on the rule ensemble method of Bagging combining LASSO regression， and the screened key rules can explain the identification process of kidney Yang deficiency syndrome. In this research， according to the regression coefficients of rules， the importance and partial dependence of variables， combined with the thinking of TCM， the influence of patient characteristics on the identification of syndromes is described， so as to reveal the primary and secondary syndromes of identification and assist the clinical identification of kidney Yang deficiency syndrome.

Recently, the situation of the preventing and controlling of the novel coronavirus infection has changed. After patients with mental disorders are infected with the new coronavirus (hereinafter referred to as the new coronavirus), they will face the following problems: whether the drugs related to the new coronavirus infection can be used at the same time as the psychiatric drugs, and whether there will be pharmacokinetic or pharmacodynamic interactions between the medicines How to avoid the safety risks brought by drug interactions? Focusing on the above issues, this article puts forward some suggestions based on the summary of existing evidence, hoping to help front-line doctors.

Recently, the situation of the preventing and controlling of the novel coronavirus infection has changed. After patients with mental disorders are infected with the new coronavirus (hereinafter referred to as the new coronavirus), they will face the following problems: whether the drugs related to the new coronavirus infection can be used at the same time as the psychiatric drugs, and whether there will be pharmacokinetic or pharmacodynamic interactions between the medicines How to avoid the safety risks brought by drug interactions? Focusing on the above issues, this article puts forward some suggestions based on the summary of existing evidence, hoping to help front-line doctors.