In order to investigate the mechanism of action of immunoenhancer, the effects of the traditional Chinese medicine immunopromoter on the quantity and the transformation rates of T lymphocytes in the chicken blood were...In order to investigate the mechanism of action of immunoenhancer, the effects of the traditional Chinese medicine immunopromoter on the quantity and the transformation rates of T lymphocytes in the chicken blood were determined. Total 120 chickens were randomly assigned into three groups. The 1% and the 0.5% of the Chinese medicine immunopromoter were added to the chicken drinking water, respectively. The quantity of T lymphocytes in each group was measured by a-Naphthyl acetate esterase （ANAE） staining. The results showed that the percentages of T lymphocytes of the treatment groups were significantly higher than that of the control group （P〈0.05）, and the percentage of the 1% group significantly higher than that of the 0.5% group （P〈0.05）. In conclusion, the transformation rates of T lymphocytes showed that the Chinese medicine immunopromoter had the significant enhancing effect on the transformation rates of T lymphocytes of the treated chickens. The traditional Chinese medicine immunopromoter had the distinct function to promote the quantity and the transformation rate of T lymphocytes.展开更多
Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of s...Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.展开更多
基金Hebei Science and Technology Office Programme (07220401D)Guangdong Science and Technology Office Programme (2006B203010 1020)
文摘In order to investigate the mechanism of action of immunoenhancer, the effects of the traditional Chinese medicine immunopromoter on the quantity and the transformation rates of T lymphocytes in the chicken blood were determined. Total 120 chickens were randomly assigned into three groups. The 1% and the 0.5% of the Chinese medicine immunopromoter were added to the chicken drinking water, respectively. The quantity of T lymphocytes in each group was measured by a-Naphthyl acetate esterase （ANAE） staining. The results showed that the percentages of T lymphocytes of the treatment groups were significantly higher than that of the control group （P〈0.05）, and the percentage of the 1% group significantly higher than that of the 0.5% group （P〈0.05）. In conclusion, the transformation rates of T lymphocytes showed that the Chinese medicine immunopromoter had the significant enhancing effect on the transformation rates of T lymphocytes of the treated chickens. The traditional Chinese medicine immunopromoter had the distinct function to promote the quantity and the transformation rate of T lymphocytes.
基金National Natural Science Foundation of China(82274411)Science and Technology Innovation Program of Hunan Province(2022RC1021)Leading Research Project of Hunan University of Chinese Medicine(2022XJJB002).
文摘Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.
