The judgment of traditional Chinese medicine(TCM)morphology typically relies on the subjective cognition and experi-ential knowledge of doctors,which lacks objectivity.Enhancing the objectification of TCM inspection is crucial for disease diagnosis and efficacy evaluation.With advancements in computer vision technology,morphological feature extraction methods have been exten-sively applied in the field of TCM inspection.Through research on these methods,the objectivity and accuracy of TCM inspection can be further enhanced.This paper systematically organizes the meaning and role of facial,tongue and full-body morphological features,puts forward the research idea of the extraction method of morphological features of TCM inspection,analyzes and summarizes the ex-traction method of morphological features of TCM inspection,in order to provide reference for the intelligent research of TCM inspec-tion.

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions,thereby offering optimized guidance for clinical diagnosis and treatment plan-ning,and ultimately enhancing medical efficiency and treatment outcomes. Methods First,TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people,from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ),and a dataset encompassing labelled constitutions was constructed.Second,heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition,a dual-branch deep network was employed to collect deep features from the full-body standing images.Last,the random forest(RF)algorithm was utilized to learn the extracted multifea-tures,which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy,precision,and F1 score were the three measures selected to assess the perfor-mance of the model. Results It was found that the accuracy,precision,and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842,0.868,and 0.790,respectively.In comparison with the identification models that encompass a single feature,either a single facial complexion feature,a body shape feature,or deep features,the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105,0.105,and 0.079,the precision increased by 0.164,0.164,and 0.211,and the F1 score rose by 0.071,0.071,and 0.084,respectively. Conclusion The research findings affirmed the viability of the proposed model,which incor-porated multifeatures,including the facial complexion feature,the body shape feature,and the deep feature.In addition,by employing the proposed model,the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

The judgment of traditional Chinese medicine(TCM)morphology typically relies on the subjective cognition and experi-ential knowledge of doctors,which lacks objectivity.Enhancing the objectification of TCM inspection is crucial for disease diagnosis and efficacy evaluation.With advancements in computer vision technology,morphological feature extraction methods have been exten-sively applied in the field of TCM inspection.Through research on these methods,the objectivity and accuracy of TCM inspection can be further enhanced.This paper systematically organizes the meaning and role of facial,tongue and full-body morphological features,puts forward the research idea of the extraction method of morphological features of TCM inspection,analyzes and summarizes the ex-traction method of morphological features of TCM inspection,in order to provide reference for the intelligent research of TCM inspec-tion.

Objective To observe the effect of curcumin on behavior,blood brain barrier(BBB)and ex-pression of glial fibrillary acidic protein(GFAP)and cyclic nucleotide 3′phosphohydrolase(CNPase)in hippocam-pus of radiation injured brain(RIB)rats. Methods SD rats were divided into radiation group,treatment group and negative control group. RIB rats model were established by X ray,and rats in treatment group were treated by curcumin. Morris water maze test were taken to study learning memory of rats in each group. The expression of Ev-ans blue(EB)in brain tissue and the expression of GFAP and CNPase in hippocampus were detected to observe the effect of curcumin on the BBB of RIB rats. Results In RIB rats,learning memory were decreased significant-ly,permeability of BBB were increased. GFAP expression in brain tissue was increased,and CNPase was de-creased(P < 0.05). After the treatment of curcumin,learning memory of rats were improved,the permeability of BBB was decreased,GFAP was decreased,and CNPase expression was increased(P < 0.05). Conclusion Cur-cumin can significantly reduce the damage of BBB in RIB rats,decrease the expression of GFAP and increase the expression of CNPase in hippocampal,which indicate that curcumin has curative effect on radiation injured brain.

OBJECTIVE: To analyze the current situation and developing trend of drugs used in 10 hospitals of Hunan Province from 1996 to 2000, and to serve as reference for drug production, marketing and clinical application .METHODS:The sum of money and percentages of different kinds of drugs used each year were added up separately.RESULTS: The sum of money of drugs consumed was increasing at a rate of 18% each year.About 40% of the money was spent on anti - infective drugs and 20% on imported drugs.CONCLUSION: The sum of money of drugs consumed will grow year by year .The clinical use of drugs will be more rational and of more choices,but the sum of money spent on imported drugs will decrease.