ABSTRACT
Objective: To summarize and compare the main traditional Chinese medicineTCMsyndromes of Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2SARS-CoV-2 carriers to provide references for the syndrome evolution and syndrome differentiation of SARS-CoV-2 infection. Method(s):The TCM medical records of imported and local cases of infection with Delta and Omicron variants of SARS-CoV-2 in Changsha since September 23,2021 to March 27,2022 were collected,including 18 Delta variant cases and 36 Omicron variant cases. Their TCM diagnosis information and TCM pathogenesis were analyzed and compared. Result(s): The common manifestations in Delta variant cases were cough,fever,chest distress/shortness of breath,sore muscles,nausea,dry mouth,dry or sore throat,thick and greasy tongue coating,and rapid and slippery pulse. The predominant pathogenesis was dampness-heat in the upper-energizer and heat stagnation in the lesser Yang combined with dampness. The occurrence of chest distress/shortness of breath,greasy tongue coating,slippery pulse,and the proportion of dampness-heat in the upper-energizer syndrome were higher in Delta variant cases than in Omicron variant cases P<0.05. The common manifestations in Omicron variant cases were itchy and sore throat,nasal congestion,running nose,fever,mild aversion to cold,dry mouth,dizziness,slightly reddish tongue with thin white coating,and rapid or wiry pulse. The predominant pathogenesis was wind-dryness invading defensive exterior,and heat stagnation in the lesser Yang. The occurrence of white-coated tongue and the proportion of wind-dryness invading defensive exterior syndrome were higher in Omicron variant cases than in Delta variant casesP<0.05. Conclusion(s): There are certain differences in TCM syndromes and the corresponding pathogenesis between Delta variant and Omicron variant cases in Changsha,Hunan. The Delta variant of SARS-COV-2 tends to induce dampness-heat syndrome, whereas Omicron variant infection tends to elicit wind-dampness syndrome,which is expected to provide a reference for the pathogenesis evolution of SARS-COV-2 infection.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.
ABSTRACT
ETHNOPHARMACOLOGICAL RELEVANCE: Tongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms based on TCM theory. AIM OF THE STUDY: The present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19. MATERIALS AND METHODS: Herein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doctors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19. RESULTS: The overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and accuracy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors' examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet. CONCLUSIONS: Our framework may provide an important research paradigm for differentiating tongue characteristics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.