Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic ris...Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic risk patients.Their prospective cohort design and dual-feature selection(LASSO+RFE)culminating in an interpretable XGBoost model(area under the curve:0.82)represent a significant methodological advance.The inclusion of TCM diagnostics addresses metabolic dysfunction-associated fatty liver disease(MAFLD’s)multisystem heterogeneity-a key strength that bridges holistic medicine with precision analytics and underscores potential cost savings over imaging-dependent screening.However,critical limitations impede clinical translation.First,the model’s singlecenter validation(n=711)lacks external/generalizability testing across diverse populations,risking bias from local demographics.Second,MAFLD subtyping(e.g.,lean MAFLD,diabetic MAFLD)was omitted despite acknowledged disease heterogeneity;this overlooks distinct pathophysiologies and may limit utility in stratified care.Third,while TCM features ranked among the top predictors in SHAP analysis,their clinical interpretability remains nebulous without mechanistic links to metabolic dysregulation.To resolve these gaps,we propose external validation in multiethnic cohorts using the published feature set(e.g.,aspartate aminotransferase/alanine aminotransferase,low-density lipoprotein cholesterol,TCM tongue markers)to assess robustness.Subtype-specific modeling to capture MAFLD heterogeneity,potentially enhancing accuracy in highrisk subgroups.Probing TCM microbiome/metabolomic correlations to ground tongue phenotypes in biological pathways,elevating model credibility.Despite shortcomings,this work pioneers a low-cost screening paradigm.Future iterations addressing these issues could revolutionize early MAFLD detection in resource-limited settings.展开更多
In recent years, a large number of college students are using educational APPs to learn English. The author has deeply analyzed and explored the difference of learning by APPs and traditional classroom learning with t...In recent years, a large number of college students are using educational APPs to learn English. The author has deeply analyzed and explored the difference of learning by APPs and traditional classroom learning with the background of Constructivism. Learning by APPs and traditional classroom learning have their prospective advantages and disadvantages on learning time,space, contents, methods, efficiency and supervision. Learners can make full use of educational APPs, combining APPs with traditional classroom learning to realize blending learning and achieve high-efficiency.展开更多
OBJECTIVE:To assess the effect of case-based learning(CBL)in the education of Traditional Chinese Medicine(TCM).METHODS:The studies concerning TCM courses designed with CBL were included by searching the databases of ...OBJECTIVE:To assess the effect of case-based learning(CBL)in the education of Traditional Chinese Medicine(TCM).METHODS:The studies concerning TCM courses designed with CBL were included by searching the databases of EBSCO,Pubmed,Science Citation Index,China National Knowledge Infrastructure,Chongqing VIP database.The valid data was extracted in accordance with the included criteria.The quality of the studies was assessed with Gemma Flores-Masteo.RESULTS:A total of 22 articles were retrieved that met the selection criteria:one was of high quality;two were of low quality;the rest were categorized as moderate quality.The majority of the studiesdemonstrated the better effect produced by CBL,while a few studies showed no difference,compared with the didactic format.All included studies confirmed the favorable effect on learners'attitude,skills and ability.CONCLUSION:CBL showed the desirable results in achieving the goal of learning.Compared to didactic approach,it played a more active role in promoting students'competency.Since the quality of the articles on which the study was based was not so high,the findings still need further research to become substantiated.展开更多
Respected leaders,distinguished vips,venerable seniors and comrades, Today,the Meeting Commemorating the 50th Anniversary of Chairman MAO Ze-dong’s Important Instructions on Western Medicine Doctors Learning Tradit...Respected leaders,distinguished vips,venerable seniors and comrades, Today,the Meeting Commemorating the 50th Anniversary of Chairman MAO Ze-dong’s Important Instructions on Western Medicine Doctors Learning Traditional Chinese Medicine was inaugurated by the Chinese Association of Integrative Medicine, and it is also an important occasion to review the past and look forward to the future.展开更多
The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a singl...The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a single catalyst type.In this work,we developed an efficient ML model to predict hydrogen evolution reaction(HER)activity across diverse catalysts.By minimizing features,we introduced a key energy-related featureφ=Nd0^(2)=ψ0,which correlates with HER free energy.Using just ten features,the Extremely Randomized Trees model achieved R^(2)=0.922.We predicted 132 new catalysts from the Material Project database,among which several exhibited promising HER performance.The time consumed by theML model for predictions is one 200,000th of that required by traditional density functional theory(DFT)methods.The model provides an efficient approach for discovering high-performance HER catalysts using a small number of key features and offers insights for the development of other catalysts.展开更多
文摘Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic risk patients.Their prospective cohort design and dual-feature selection(LASSO+RFE)culminating in an interpretable XGBoost model(area under the curve:0.82)represent a significant methodological advance.The inclusion of TCM diagnostics addresses metabolic dysfunction-associated fatty liver disease(MAFLD’s)multisystem heterogeneity-a key strength that bridges holistic medicine with precision analytics and underscores potential cost savings over imaging-dependent screening.However,critical limitations impede clinical translation.First,the model’s singlecenter validation(n=711)lacks external/generalizability testing across diverse populations,risking bias from local demographics.Second,MAFLD subtyping(e.g.,lean MAFLD,diabetic MAFLD)was omitted despite acknowledged disease heterogeneity;this overlooks distinct pathophysiologies and may limit utility in stratified care.Third,while TCM features ranked among the top predictors in SHAP analysis,their clinical interpretability remains nebulous without mechanistic links to metabolic dysregulation.To resolve these gaps,we propose external validation in multiethnic cohorts using the published feature set(e.g.,aspartate aminotransferase/alanine aminotransferase,low-density lipoprotein cholesterol,TCM tongue markers)to assess robustness.Subtype-specific modeling to capture MAFLD heterogeneity,potentially enhancing accuracy in highrisk subgroups.Probing TCM microbiome/metabolomic correlations to ground tongue phenotypes in biological pathways,elevating model credibility.Despite shortcomings,this work pioneers a low-cost screening paradigm.Future iterations addressing these issues could revolutionize early MAFLD detection in resource-limited settings.
文摘In recent years, a large number of college students are using educational APPs to learn English. The author has deeply analyzed and explored the difference of learning by APPs and traditional classroom learning with the background of Constructivism. Learning by APPs and traditional classroom learning have their prospective advantages and disadvantages on learning time,space, contents, methods, efficiency and supervision. Learners can make full use of educational APPs, combining APPs with traditional classroom learning to realize blending learning and achieve high-efficiency.
基金Supported by "Twelve-five" Scientific Research Study on Education from Chinese Academy of Higher Education(No.11YB032)by Scientific Research Study on Education from Sichuan Academy of Higher Education(No.11SC-007)by Key research project on teaching reform from Chengdu University of Traditional Chinese Medicine(No.JGZD201001)
文摘OBJECTIVE:To assess the effect of case-based learning(CBL)in the education of Traditional Chinese Medicine(TCM).METHODS:The studies concerning TCM courses designed with CBL were included by searching the databases of EBSCO,Pubmed,Science Citation Index,China National Knowledge Infrastructure,Chongqing VIP database.The valid data was extracted in accordance with the included criteria.The quality of the studies was assessed with Gemma Flores-Masteo.RESULTS:A total of 22 articles were retrieved that met the selection criteria:one was of high quality;two were of low quality;the rest were categorized as moderate quality.The majority of the studiesdemonstrated the better effect produced by CBL,while a few studies showed no difference,compared with the didactic format.All included studies confirmed the favorable effect on learners'attitude,skills and ability.CONCLUSION:CBL showed the desirable results in achieving the goal of learning.Compared to didactic approach,it played a more active role in promoting students'competency.Since the quality of the articles on which the study was based was not so high,the findings still need further research to become substantiated.
文摘Respected leaders,distinguished vips,venerable seniors and comrades, Today,the Meeting Commemorating the 50th Anniversary of Chairman MAO Ze-dong’s Important Instructions on Western Medicine Doctors Learning Traditional Chinese Medicine was inaugurated by the Chinese Association of Integrative Medicine, and it is also an important occasion to review the past and look forward to the future.
基金supported by the National Key R&D Program of China(Grant No.2021YFB3500403)the Youth Fund of the National Natural Science Foundation of China(Grant no.52305443).We gratefully acknowledge HZWTECH for providing computational facilities.
文摘The vast chemical compositional space presents challenges in catalyst development using traditional methods.Machine learning(ML)offers new opportunities,but current ML models are typically limited to screening a single catalyst type.In this work,we developed an efficient ML model to predict hydrogen evolution reaction(HER)activity across diverse catalysts.By minimizing features,we introduced a key energy-related featureφ=Nd0^(2)=ψ0,which correlates with HER free energy.Using just ten features,the Extremely Randomized Trees model achieved R^(2)=0.922.We predicted 132 new catalysts from the Material Project database,among which several exhibited promising HER performance.The time consumed by theML model for predictions is one 200,000th of that required by traditional density functional theory(DFT)methods.The model provides an efficient approach for discovering high-performance HER catalysts using a small number of key features and offers insights for the development of other catalysts.
