Objective Validate the correlation between tongue features and age and gender by utilizing real-world tongue image data from healthy individuals.Methods Establishing,training,and validating a model using artificial intelligence image acquisition techniques and machine learning tools.Results ①Artificial intelligence has achieved the digitization and standardization of Traditional Chinese Medicine tongue diagnosis data,establishing a database of Traditional Chinese Medicine tongue diagnosis(n=56573).②A highly accurate random forest model has demonstrated the correlation between tongue features and gender and age.③Further selection of key factors that show differences in tongue features between different age groups and genders.④Machine learning artificial neural network models were trained,tested,and validated(training set R2=0.8772,validation set R2=0.8715,test set R2=0.8707,AUC=89.5％),demonstrating excellent accuracy.Conclusion Machine learning validation using real-world data has confirmed that tongue features change with age.Additionally,analysis of large-scale real-world data has revealed that there are different tongue features associated with gender differences.

Objective Validate the correlation between tongue features and age and gender by utilizing real-world tongue image data from healthy individuals.Methods Establishing,training,and validating a model using artificial intelligence image acquisition techniques and machine learning tools.Results ①Artificial intelligence has achieved the digitization and standardization of Traditional Chinese Medicine tongue diagnosis data,establishing a database of Traditional Chinese Medicine tongue diagnosis(n=56573).②A highly accurate random forest model has demonstrated the correlation between tongue features and gender and age.③Further selection of key factors that show differences in tongue features between different age groups and genders.④Machine learning artificial neural network models were trained,tested,and validated(training set R2=0.8772,validation set R2=0.8715,test set R2=0.8707,AUC=89.5％),demonstrating excellent accuracy.Conclusion Machine learning validation using real-world data has confirmed that tongue features change with age.Additionally,analysis of large-scale real-world data has revealed that there are different tongue features associated with gender differences.

Objective To explore the influences of selenium,age,sex,education level,occupation and other factors on cognitive function in elderly of rural areas in Shandong Province.Methods Rural Chinese aged 65 years or older were sampled from Gaomi County and Zichuan County from 2006 to 2007 in Shandong,Province by cluster sampling method.Demographic characteristics were collected,and cognitive functions were surveyed using dementia community survey(CSID),including dementia test,CERAD word list learning,recall test,Indiana University (IU) story recall test and impact test on animals.The nail samples were collected and the selenium content was tested using 2,3-diamino-naphthalene fluorescence assay.The relationship between selenium and other related factors(age,sex,education level,occupation,et al) With cognitive function was analyzed.Results A total of 1 000 people aged 65 years or older were investigated.In which,457 were males,and 543 were females.Most elderly were farmers and illiteracy.The differences of CSID total scores and IU story recall scores between different selenium groups were statistically significant(F =2.56,9.18,P ＜ 0.05 or ＜ 0.01).Multiple linear regression model analysis showed,age,sex,education level,occupation,hypertension and nail selenium content had significant impact on CSID scores(t =-9.942,-6.848,5.391,2.276,-2.863,2.309,all P ＜ 0.05).Age,sex,education level,occupation and nail selenium content had significant impact on IU story recall test (t =-4.252,-2.021,8.203,2.528,4.490,all P ＜ 0.05).While age,sex,education level,occupation were main influence factors to animal fluency test(t =-7.951,-6.166,7.544,2.824,all P＜ 0.05).Conclusions Selenium is a protective factor for cognitive function of elderly in Shandong Province.Besides,age,sex and education level also have impact on cognitive ability of rural elderly.