Two-stream vision transformer based multi-label recognition for TCM prescriptions construction.

Traditional Chinese medicine (TCM) observation diagnosis images (including facial and tongue images) provide essential human body information, holding significant importance in clinical medicine for diagnosis and treatment. TCM prescriptions, known for their simplicity, non-invasiveness, and low side effects, have been widely applied worldwide. Exploring automated herbal prescription construction based on visual diagnosis holds vital value in delving into the correlation between external features and herbal prescriptions and offering medical services in mobile healthcare systems. To effectively integrate multi-perspective visual diagnosis images and automate prescription construction, this study proposes a multi-herb recommendation framework based on Visual Transformer and multi-label classification. The framework comprises three key components: image encoder, label embedding module, and cross-modal fusion classification module. The image encoder employs a dual-stream Visual Transformer to learn dependencies between different regions of input images, capturing both local and global features. The label embedding module utilizes Graph Convolutional Networks to capture associations between diverse herbal labels. Finally, two Multi-Modal Factorized Bilinear modules are introduced as effective components to fuse cross-modal vectors, creating an end-to-end multi-label image-herb recommendation model. Through experimentation with real facial and tongue images and generating prescription data closely resembling real samples. The precision is 50.06 %, the recall rate is 48.33 %, and the F1-score is 49.18 %. This study validates the feasibility of automated herbal prescription construction from the perspective of visual diagnosis. Simultaneously, it provides valuable insights for constructing herbal prescriptions automatically from more physical information.

Gynura divaricata (L.) DC. promotes diabetic wound healing by activating Nrf2 signaling in diabetic rats.

THE ETHNOPHARMACOLOGICAL SIGNIFICANCE: Diabetic chronic foot ulcers pose a significant therapeutic challenge as a result of the oxidative stress caused by hyperglycemia. Which impairs angiogenesis and delays wound healing, potentially leading to amputation. Gynura divaricata (L.) DC. (GD), a traditional Chinese herbal medicine with hypoglycemic effects, has been proposed as a potential therapeutic agent for diabetic wound healing. However, the underlying mechanisms of its effects remain unclear. AIM OF THE STUDY: In this study, we aimed to reveal the effect and potential mechanisms of GD on accelerating diabetic wound healing in vitro and in vivo. MATERIALS AND METHODS: The effects of GD on cell proliferation, apoptosis, reactive oxygen species (ROS) production, migration, mitochondrial membrane potential (MMP), and potential molecular mechanisms were investigated in high glucose (HG) stimulated human umbilical vein endothelial cells (HUVECs) using CCK-8, flow cytometry assay, wound healing assay, immunofluorescence, DCFH-DA staining, JC-1 staining, and Western blot. Full-thickness skin defects were created in STZ-induced diabetic rats, and wound healing rate was tracked by photographing them every day. HE staining, immunohistochemistry, and Western blot were employed to investigate the effect and molecular mechanism of GD on wound healing in diabetic rats. RESULTS: GD significantly improved HUVEC survival, decreased apoptosis, lowered ROS production, restored MMP, improved migration ability, and raised VEGF expression. The use of Nrf2-siRNA completely abrogated these effects. Topical application of GD promoted angiogenesis and granulation tissue growth, resulting in faster healing of diabetic wounds. The expression of VEGF, CD31, and VEGFR was elevated in the skin tissue of diabetic rats after GD treatment, which upregulated HO-1, NQO-1, and Bcl-2 expression while downregulating Bax expression via activation of the Nrf2 signaling pathway. CONCLUSION: The findings of this study indicate that GD has the potential to serve as a viable alternative treatment for diabetic wounds. This potential arises from its ability to mitigate the negative effects of oxidative stress on angiogenesis, which is regulated by the Nrf2 signaling pathway. The results of our study offer valuable insights into the therapeutic efficacy of GD in the treatment of diabetic wounds, emphasizing the significance of directing interventions towards the Nrf2 signaling pathway to mitigate oxidative stress and facilitate the process of angiogenesis.

TCM Prescription Generation via Knowledge Source Guidance Network Combined with Herbal Candidate Mechanism.

Traditional Chinese medicine (TCM) prescriptions have made great contributions to the treatment of diseases and health preservation. To alleviate the shortage of TCM resources and improve the professionalism of automatically generated prescriptions, this paper deeply explores the connection between symptoms and herbs through deep learning technology, and realizes the automatic generation of TCM prescriptions. Particularly, this paper considers the significance of referring to similar underlying prescriptions as herbal candidates in the TCM prescribing process. Moreover, this paper incorporates the idea of referring to the potential guidance information of corresponding prescriptions when model extracts symptoms representations. To provide a reference for inexperienced young TCM doctors when they prescribe, this paper proposes a dual-branch guidance strategy combined with candidate attention model (DGSCAM) to automatically generate TCM prescriptions based on symptoms text. The format of the data used this paper is the "symptoms-prescription" data pair. The specific method is as follows. First, DGSCAM realizes the extraction of key information of prescription-guided symptoms through a dual-branch network. Then, herbal candidates in the form of prescriptions that can treat symptoms are proposed in view of the compatibility knowledge of TCM prescriptions. To our knowledge, this is the first attempt to use prescriptions as herbal candidates in the prescription generation process. We conduct extensive experiments on a mixed public and clinical dataset, achieving 37.39% precision, 25.04% recall, and 29.99% F1 score, with an average doctor score of 7.7 out of 10. The experimental results show that our proposed model is valid and can generate more specialized TCM prescriptions than the baseline models. The DGSCAM developed by us has broad application scenarios and greatly promotes the research on intelligent TCM prescribing.

Multitask Interactive Attention Learning Model Based on Hand Images for Assisting Chinese Medicine in Predicting Myocardial Infarction.

Acute myocardial infarction (AMI) is one of the most serious and dangerous cardiovascular diseases. In recent years, the number of patients around the world has been increasing significantly, among which people under the age of 45 have become the high-risk group for sudden death of AMI. AMI occurs quickly and does not show obvious symptoms before onset. In addition, postonset clinical testing is also a complex and invasive test, which may cause some postoperative complications. Therefore, it is necessary to propose a noninvasive and convenient auxiliary diagnostic method. In traditional Chinese medicine (TCM), it is an effective auxiliary diagnostic strategy to complete the disease diagnosis through some body surface features. It is helpful to observe whether the palmar thenar undergoes hypertrophy and whether the metacarpophalangeal joint is swelling in detecting acute myocardial infarction. Combined with deep learning, we propose a depth model based on traditional palm image (MTIALM), which can help doctors of traditional Chinese medicine to predict myocardial infarction. By building the shared network, the model learns information that covers all the tasks. In addition, task-specific attention branch networks are built to simultaneously detect the symptoms of different parts of the palm. The information interaction module (IIM) is proposed to further integrate the information between task branches to ensure that the model learns as many features as possible. Experimental results show that the accuracy of our model in the detection of metacarpophalangeal joints and palmar thenar is 83.16% and 84.15%, respectively, which are significantly improved compared with the traditional classification methods.

Research progress of epigallocatechin-3-gallate (EGCG) on anti-pathogenic microbes and immune regulation activities.

At the end of 2019, the COVID-19 virus spread worldwide, infecting millions of people. Infectious diseases induced by pathogenic microorganisms such as the influenza virus, hepatitis virus, and Mycobacterium tuberculosis are also a major threat to public health. The high mortality caused by infectious pathogenic microorganisms is due to their strong virulence, which leads to the excessive counterattack by the host immune system and severe inflammatory damage of the immune system. This paper reviews the efficacy, mechanism and related immune regulation of epigallocatechin-3-gallate (EGCG) as an anti-pathogenic microorganism drug. EGCG mainly shows both direct and indirect anti-infection effects. EGCG directly inhibits early infection by interfering with the adsorption on host cells, inhibiting virus replication and reducing bacterial biofilm formation and toxin release; EGCG indirectly inhibits infection by regulating immune inflammation and antioxidation. At the same time, we reviewed the bioavailability and safety of EGCG in vivo. At present, the bioavailability of EGCG can be improved to some extent using nanostructured drug delivery systems and molecular modification technology in combination with other drugs. This study provides a theoretical basis for the development of EGCG as an adjuvant drug for anti-pathogenic microorganisms.