This article is based on modern psychology and TCM emotional theory,combined with facial expression recognition technology,to apply deep learning methods to the digital research of TCM emotions to more accurately capture and analyze patients'emotional states.A cross-disciplinary framework was established by synthesizing facial expression-emotion correlations from psychological and TCM perspectives.The methodology included:Data annotation of TCM-defined emotional expressions using standardized coding systems;Facial expression acquisition,spatiotemporal feature extraction and emotion classification through a 3D convolutional neural network(3D CNN).The framework achieved 91.43%accuracy in video-based emotion classification.High-arousal emotional states demonstrated superior recognition performance,with anger showing optimal recall(1.000 0)and F1-score(0.946 3),while surprise attained the highest precision(0.976 0).These findings aligned with TCM pathological characteristic of"anger induces qi ascending,surprise disrupts qi flow".The digital recognition framework for TCM emotional quantification based on facial expression recognition technology exhibits strong alignment with TCM observation diagnosis,providing clinicians with an objective tool to assess the"seven emotions"and elucidating emotion-facial correlations in classical TCM theory.Future research should focus on longitudinal validation across diverse populations and establish development pathways for AI-assisted TCM diagnostic systems.

This article is based on modern psychology and TCM emotional theory,combined with facial expression recognition technology,to apply deep learning methods to the digital research of TCM emotions to more accurately capture and analyze patients'emotional states.A cross-disciplinary framework was established by synthesizing facial expression-emotion correlations from psychological and TCM perspectives.The methodology included:Data annotation of TCM-defined emotional expressions using standardized coding systems;Facial expression acquisition,spatiotemporal feature extraction and emotion classification through a 3D convolutional neural network(3D CNN).The framework achieved 91.43%accuracy in video-based emotion classification.High-arousal emotional states demonstrated superior recognition performance,with anger showing optimal recall(1.000 0)and F1-score(0.946 3),while surprise attained the highest precision(0.976 0).These findings aligned with TCM pathological characteristic of"anger induces qi ascending,surprise disrupts qi flow".The digital recognition framework for TCM emotional quantification based on facial expression recognition technology exhibits strong alignment with TCM observation diagnosis,providing clinicians with an objective tool to assess the"seven emotions"and elucidating emotion-facial correlations in classical TCM theory.Future research should focus on longitudinal validation across diverse populations and establish development pathways for AI-assisted TCM diagnostic systems.

Objective:To study the quality and stability of commercial ready-to-use tryptone soya agar(TSA)after storing at 2-25 ℃ for different storage duration under dark condition in order to discuss a determination method of validity period for medium.Methods:Three consecutive batches of ready-to-use TSA medium from two manufac-turers were selected and stored at 2-25 ℃ under dark conditions for 30,90 and 180 days,respectively.The appearance,pH,medium suitability and sterility of the medium were tested.Results:The results of appearance,pH,suitability and sterility of TSA medium from two manufacturers for each batch under different storage duration all met the requirements of the Chinese Pharmacopoeia 2020 Volume IV on the quality control of medium.Conclusion:The TSA medium from two manufacturers all met the requirements when stored for 180 days at 2-25 ℃ under dark condition,indicating that the validity period of TSA medium from two manufacturers can reach 180 days.

Female ; Humans ; Breast Neoplasms ; Testosterone ; Prolactin ; Estradiol ; Testosterone Congeners ; China