Methods: Data from students at Chengdu University of Traditional Chinese Medicine were collected and organized according to the 24 solar terms from January 21, 2020, to April 6, 2022. The data were used to identify nine TCM constitutions, including balanced constitution, Qi deficiency constitution, Yang deficiency constitution, Yin deficiency constitution, phlegm dampness constitution, damp heat constitution, stagnant blood constitution, Qi stagnation constitution, and specific-inherited predisposition constitution. Deep learning algorithms were employed to construct multi-layer perceptron (MLP), long short-term memory (LSTM), and deep belief network (DBN) models for the prediction of TCM constitutions based on the nine constitution types. To optimize these TCM constitution prediction models, this study introduced the attention mechanism (AM), grey wolf optimizer (GWO), and particle swarm optimization (PSO). The models’ performance was evaluated before and after optimization using the F1-score, accuracy, precision, and recall. Results: The research analyzed a total of 31 655 pieces of data. (i) Before optimization, the MLP model achieved more than 90% prediction accuracy for all constitution types except the balanced and Qi deficiency constitutions. The LSTM model's prediction accuracies exceeded 60%, indicating that their potential in TCM constitutional prediction may not have been fully realized due to the absence of pronounced temporal features in the data. Regarding the DBN model, the binary classification analysis showed that, apart from slightly underperforming in predicting the Qi deficiency constitution and damp heat constitution, with accuracies of 65% and 60%, respectively. The DBN model demonstrated considerable discriminative power for other constitution types, achieving prediction accuracy rates and area under the receiver operating characteristic (ROC) curve (AUC) values exceeding 70% and 0.78, respectively. This indicates that while the model possesses a certain level of constitutional differentiation ability, it encounters limitations in processing specific constitutional features, leaving room for further improvement in its performance. For multi-class classification problem, the DBN model’s prediction accuracy rate fell short of 50%. (ii) After optimization, the LSTM model, enhanced with the AM, typically achieved a prediction accuracy rate above 75%, with lower performance for the Qi deficiency constitution, stagnant blood constitution, and Qi stagnation constitution. The GWO-optimized DBN model for multi-class classification showed an increased prediction accuracy rate of 56%, while the PSO-optimized model had a decreased accuracy rate to 37%. The GWO-PSO-DBN model, optimized with both algorithms, demonstrated an improved prediction accuracy rate of 54%. Conclusion This study constructed MLP, LSTM, and DBN models for predicting TCM constitution and improved them based on different optimisation algorithms. The results showed that the MLP model performs well, the LSTM and DBN models were effective in prediction but with certain limitations. This study also provided a new technology reference for the establishment and optimisation strategies of TCM constitution prediction models,and a novel idea for the treatment of non-disease.

Aiming at the continuous advancement and deepening of curriculum ideological and political education in medical courses, taking the recent outbreak of public health emergency as an example, this paper expounds the feasibility of taking public health emergencies as typical cases of ideological and political education in public health courses in medical colleges from medical ethics, national spirit, policy guidelines, medical and health administrative system, social benefits and economic evaluation, scientific research collaboration, international cooperation and other aspects, in order to realize the simultaneous development of professional education and ideological and political education, and cultivate more high-quality medical talents with both merit and talent.

Interpretation of syndrome differentiation is a necessary topic for the research on intelligent syndrome differentiation. The syndrome differentiation system of traditional Chinese medicine （TCM） is huge， with diverse and interrelated interpretations， and non-standardized linguistic expressions， while detailed interpretations involve complex basic theories and literature of TCM， resulting in a challenging organization of the system. From the perspective of text mining， this article analyzed the limitations of existing intelligent syndrome differentiation research， clarified the importance and necessity of constructing a syndrome differentiation interpretation system， and proposed the idea of constructing an interpretation system including conceptual system， logical system， evaluation system， and visualization presentation. Future research can continue to refine the structure， logical and semantic relationships， as well as evaluation methods of the interpretation system based on this ideas， present concrete examples， and build the interpretation corpus by utilizing various data sources such as textbooks， medical records， medical discourse， and papers， to provide technicians with standard reference and data support for TCM syndrome differentiation interpretation.

By applying "homeostasis" to the study of the meridian and collateral system， the concept of meridian and collateral homeostasis has been proposed which refers to a balanced and stable state of meridian and collateral system， and plays an important role in maintaining body health and can provide a reference for the diagnosis and treatment of diseases. Phenomics realizes the cross-scale correlation from micro-phenotypic data， such as genome， proteome， and metabolome， to macro-phenotypic data， such as physiological state， behavioral activities， and external manifestations. From the perspective of phenomics， this paper proposes a meridian and collateral homeostasis dynamic mapping model of "macroscopic signs and microscopic expression". This model combines macro signs such as the four examinations of traditional Chinese medicine （TCM）， biophysical indicators of acupoints， and micro expression information such as genes， proteins， and metabolism， and systematically investigates the relationship between meridian and collateral homeostasis and health and disease， thereby providing ideas and references for the identification of pre-disease states as well as precise diagnosis and treatment in TCM.