Despite of the widespread use of hydrogen and methane breath test, the variability in testing protocols, gas measurement techniques, and interpretation criteria continues to challenge the reproducibility and comparability across centers, especially in the Asia-Pacific region. The Asian Neurogastroenterology and Motility Association hence presents the first Asian monograph guiding application and interpretation of breath test. The monograph was formulated according to the framework of indications, preparatory process, performance, and interpretation of results, as well as future direction for research.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Aim To verify the therapeutic effect of the Qushi Huoxue decoction(QSHXF)on a mouse model of non-alcoholic fatty liver disease(NAFLD)using network pharmacology and experimental approaches,to examine the changes in the PI3K-AKT-lipid metabo-lism signaling pathway,and to elucidate its molecular mechanisms.Methods The potential active ingredi-ents and targets of the QSHXF were identified using the TCMSP platform.NAFLD-related genes were sourced from the GeneCards,PharmGkb,TTD,and OMIM data-bases.The intersection of drug targets and NAFLD treatment targets was analyzed to identify the key tar-gets of the QSHXF in treating NAFLD.The STRING database and Cytoscape 3.9.1 software were utilized to construct networks linking traditional Chinese medicine active ingredients to disease targets and PPI networks,allowing for the screening of key active ingredients and core targets.GO and KEGG enrichment analyses of the intersecting targets were conducted using R version 4.2.2.The NAFLD model was established by feeding mice a methionine-choline deficient diet for a duration of five weeks.Following successful modeling,low,me-dium,and high doses of the QSHXF were administered for intervention over a period of six weeks.The efficacy was verified and the underlying mechanisms were ex-plored using methods such as HE staining,Oil Red O staining,and Western blot analysis.Results The net-work pharmacology prediction indicated that QSHXF might effectively treat NAFLD through key components such as quercetin and kaempferol,as well as core tar-gets including STAT3,AKT1,and HIF1A.KEGG en-richment analysis further suggested that QSHXF might exert its therapeutic effects on NAFLD via signaling pathways such as AGE-RAGE and PI3K-AKT.Verifi-cation through animal experiments demonstrated that QSHXF could significantly reduce hepatic steatosis and lipid droplet accumulation in NAFLD mice.Specifical-ly,it markedly decreased serum levels of TC,TG,ALT,AST,and LDL,while increasing HDL levels.Addition-ally,the treatment significantly reduced the protein ex-pression levels of p-PI3K,p-AKT,SREBP-1c,FASN,and ACC1 in the liver.Conclusions QSHXF can sig-nificantly enhance liver function,improve blood lipid levels,and alleviate hepatic steatosis in NAFLD mice,with its mechanism potentially linked to the inhibition of the PI3K-AKT-lipid metabolism signaling pathway.

Aim To verify the therapeutic effect of the Qushi Huoxue decoction(QSHXF)on a mouse model of non-alcoholic fatty liver disease(NAFLD)using network pharmacology and experimental approaches,to examine the changes in the PI3K-AKT-lipid metabo-lism signaling pathway,and to elucidate its molecular mechanisms.Methods The potential active ingredi-ents and targets of the QSHXF were identified using the TCMSP platform.NAFLD-related genes were sourced from the GeneCards,PharmGkb,TTD,and OMIM data-bases.The intersection of drug targets and NAFLD treatment targets was analyzed to identify the key tar-gets of the QSHXF in treating NAFLD.The STRING database and Cytoscape 3.9.1 software were utilized to construct networks linking traditional Chinese medicine active ingredients to disease targets and PPI networks,allowing for the screening of key active ingredients and core targets.GO and KEGG enrichment analyses of the intersecting targets were conducted using R version 4.2.2.The NAFLD model was established by feeding mice a methionine-choline deficient diet for a duration of five weeks.Following successful modeling,low,me-dium,and high doses of the QSHXF were administered for intervention over a period of six weeks.The efficacy was verified and the underlying mechanisms were ex-plored using methods such as HE staining,Oil Red O staining,and Western blot analysis.Results The net-work pharmacology prediction indicated that QSHXF might effectively treat NAFLD through key components such as quercetin and kaempferol,as well as core tar-gets including STAT3,AKT1,and HIF1A.KEGG en-richment analysis further suggested that QSHXF might exert its therapeutic effects on NAFLD via signaling pathways such as AGE-RAGE and PI3K-AKT.Verifi-cation through animal experiments demonstrated that QSHXF could significantly reduce hepatic steatosis and lipid droplet accumulation in NAFLD mice.Specifical-ly,it markedly decreased serum levels of TC,TG,ALT,AST,and LDL,while increasing HDL levels.Addition-ally,the treatment significantly reduced the protein ex-pression levels of p-PI3K,p-AKT,SREBP-1c,FASN,and ACC1 in the liver.Conclusions QSHXF can sig-nificantly enhance liver function,improve blood lipid levels,and alleviate hepatic steatosis in NAFLD mice,with its mechanism potentially linked to the inhibition of the PI3K-AKT-lipid metabolism signaling pathway.

Objective To establish a multidisciplinary diagnosis and treatment quality evaluation index system,provi-ding reference for the evaluation of multidisciplinary diagnosis and treatment work by medical management departments.Methods A framework of evaluation indicators for multidisciplinary diagnosis and treatment was formed through literature research and ex-pert interviews,and the content of the indicators was improved using the Delphi expert consultation method.Results After two rounds of expert consultation,an evaluation index system was constructed with 3 primary indicators,8 secondary indicators,and 29 tertiary indicators.The positive coefficient of the two rounds of consultation was 97.3％and 100％respectively,the authority coefficient was 0.89,and the overall Kendall's harmony coefficient was between 0.3 and 1.Conclusion The index system de-veloped in this study is in line with the practicality of the multidisciplinary diagnosis and treatment model in current tertiary com-prehensive hospitals and specialized hospitals.It has strong operability and can provide reference for the evaluation and improve-ment of multidisciplinary diagnosis and treatment quality.

To investigate the transcriptionally regulatory mechanism of the senp8 promoter in yellow cat-fish(Pelteobagrus fulvidraco);this study used P.fulvidraco as the research subject.Dual-luciferase re-porter assay and electrophoretic mobility shift assay were employed to analyze the functional activity of the promoter;coupled with in vivo experiments.The results indicated that the 2 045 bp senp8 promoter se-quence contained key transcription factor binding sites such as SP1;TATA-Box;CCAAT-Box;SREBP1;PPARα;and PPARγ.The binding sites of SREBP1(-901/-910 bp);PPARα(-1 291/-1 308 bp);and PPARγ(-1 292/-1 306 bp)in the senp8 promoter positively regulate its activity;and oleic acid or palmitic acid promote this binding.Furthermore;high-fat feeding promoted the expression of the senp8 gene and its protein in the liver of P.fulvidraco;oleic acid or palmitic acid treatment significantly en-hanced the activity of the senp8 promoter;and this enhancement could be achieved through the regulatory effects of SREBP1;PPARα;and PPARγ response elements.Additionally;high-fat feeding influenced the mRNA and protein expression levels of genes related to deSUMOylation modification in the liver of P.fulvidraco.This study provides new insights into the relationship between deSUMOylation modification and the regulation of lipid metabolism in the vertebrates.

Serine protease inhibitor Kazal-type (SPINK) is a skin keratinizing protease inhibitor, which was initially found in animal serum and is widely present in plants, animals, bacteria, and viruses, and they act as key regulators of skin keratinizing proteases and are involved in the regulation of keratinocyte proliferation and inflammation, primarily through the inhibition of deregulated tissue kinin-releasing enzymes (KLKs) in skin response. This process plays a crucial role in alleviating various skin problems caused by hyperkeratinization and inflammation, and can greatly improve the overall condition of the skin. Specifically, the different members of the SPINK family, such as SPINK5, SPINK6, SPINK7, and SPINK9, each have unique biological functions and mechanisms of action. The existence of these members demonstrates the diversity and complexity of skin health and disease. First, SPINK5 mutations are closely associated with the development of various skin diseases, such as Netherton’s syndrome and atopic dermatitis, and SPINK5 is able to inhibit the activation of the STAT3 signaling pathway, thereby effectively preventing the metastasis of melanoma cells, which is important in preventing the invasion and migration of malignant tumors. Secondly, SPINK6 is mainly distributed in the epidermis and contains lysine and glutamate residues, which can act as a substrate for epidermal transglutaminase to maintain the normal structure and function of the skin. In addition, SPINK6 can activate the intracellular ERK1/2 and AKT signaling pathways through the activation of epidermal growth factor receptor and protease receptor-2 (EphA2), which can promote the migration of melanoma cells, and SPINK6 further deepens its role in stimulating the migration of malignant tumor cells by inhibiting the activation of STAT3 signaling pathway. This process further deepens its potential impact in stimulating tumor invasive migration. Furthermore, SPINK7 plays a role in the pathology of some inflammatory skin diseases, and is likely to be an important factor contributing to the exacerbation of skin diseases by promoting aberrant proliferation of keratinocytes and local inflammatory responses. Finally, SPINK9 can induce cell migration and promote skin wound healing by activating purinergic receptor 2 (P2R) to induce phosphorylation of epidermal growth factor and further activating the downstream ERK1/2 signaling pathway. In addition, SPINK9 also plays an antimicrobial role, preventing the interference of some pathogenic microorganisms. Taken as a whole, some members of the SPINK family may be potential targets for the treatment of dermatological disorders by regulating multiple biological processes such as keratinization metabolism and immuno-inflammatory processes in the skin. The development of drugs such as small molecule inhibitors and monoclonal antibodies has great potential for the treatment of dermatologic diseases, and future research on SPINK will help to gain a deeper understanding of the physiopathologic processes of the skin. Through its functions and regulatory mechanisms, the formation and maintenance of the skin barrier and the occurrence and development of inflammatory responses can be better understood, which will provide novel ideas and methods for the prevention and treatment of skin diseases.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.