Gender differences in Damp-Heat Syndrome: A review

Identification of the therapeutic effect and molecular mechanism of Coptis chinensis Franch. and Magnolia officinalis var. biloba on chronic gastritis

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine (TCM) theory believes that clearing heat and promoting dampness is the main treatment method for chronic gastritis. Coptis chinensis Franch. has the effects of clearing heat, detoxifying, and anti-inflammatory; Magnolia officinalis var. biloba can be used to treat abdominal pain, cough, and asthma. Coptis chinensis Franch. and Magnolia officinalis var. biloba can regulate the balance of intestinal microbiota and inhibit inflammatory reactions.

AIM

This study will verify the therapeutic effect of Coptis chinensis Franch. and Magnolia officinalis var. biloba on chronic gastritis, and explore its mechanism through transcriptome sequencing.

METHODS

Firstly, a rat chronic gastritis model was established, and the anal temperature and body weight changes of the rats before and after modeling were observed. Next, H&E staining, TUNEL assay and ELISA assay were performed on rat gastric mucosal tissues. Subsequently, the key fractions of Coptis chinensis Franch. and Magnolia officinalis var. biloba were obtained by high performance liquid chromatography (HPLC), and a GES-1 cell inflammation model was constructed to select the optimal monomer. Finally, the mechanism of action of Coptis chinensis Franch. and Magnolia officinalis var. biloba was explored through RNA seq.

RESULTS

Compared with the control group, the rats in the administered group were in better condition, with higher anal temperature, reduced inflammatory response in gastric mucosal tissue and reduced apoptosis. The optimal fraction Coptisine was subsequently determined by HPLC and GES-1 cell model. RNA-seq analysis revealed that DEG was significantly enriched in ribosomes, NF-κB signaling pathway, etc. The key genes TPT1 and RPL37 were subsequently obtained.

CONCLUSIONS

This study verified the therapeutic effects of Coptis chinensis Franch. and Magnolia officinalis var. biloba on chronic gastritis by in vivo and in vitro experiments in rats, identified Coptisine as the optimal component, and obtained two potential target genes.

Application of machine learning in Chinese medicine differentiation of dampness-heat pattern in patients with type 2 diabetes mellitus

Background

China has become the country with the largest number of people with type 2 diabetes mellitus (T2DM), and Chinese medicine (CM) has unique advantages in preventing and treating T2DM, while accurate pattern differentiation is the guarantee for proper treatment.

Objective

The establishment of the CM pattern differentiation model of T2DM is helpful to the pattern diagnosis of the disease. At present, there are few studies on dampness-heat pattern differentiation models of T2DM. Therefore, we establish a machine learning model, hoping to provide an efficient tool for the pattern diagnosis of CM for T2DM in the future.

Methods

A total of 1021 effective samples of T2DM patients from ten CM hospitals or clinics were collected by a questionnaire including patients' demographic and dampness-heat-related symptoms and signs. All information and the diagnosis of the dampness-heat pattern of patients were completed by experienced CM physicians at each visit. We applied six machine learning algorithms (Artificial Neural Network [ANN], K-Nearest Neighbor [KNN], Naïve Bayes [NB], Support Vector Machine [SVM], Extreme Gradient Boosting [XGBoost] and Random Forest [RF]) and compared their performance. And then we also utilized Shapley additive explanation (SHAP) method to explain the best performance model.

Results

The XGBoost model had the highest AUC (0.951, 95% CI 0.925-0.978) among the six models, with the best sensitivity, accuracy, F1 score, negative predictive value, and excellent specificity, precision, and positive predictive value. The SHAP method based on XGBoost showed that slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis. The slippery pulse or rapid-slippery pulse, sticky stool with ungratifying defecation also performed an important role in this diagnostic model. Furthermore, the red tongue acted as an important tongue sign for the dampness-heat pattern.

Conclusion

This study constructed a dampness-heat pattern differentiation model of T2DM based on machine learning. The XGBoost model is a tool with the potential to help CM practitioners make quick diagnosis decisions and contribute to the standardization and international application of CM patterns.

A Mouse Model of Damp-Heat Syndrome in Traditional Chinese Medicine and Its Impact on Pancreatic Tumor Growth

Background

Damp-heat syndrome is one of the most important syndrome types in the traditional Chinese medicine (TCM) syndrome differentiation and treatment system, as well as the core pathogenesis of pancreatic cancer (PC) which remains a challenge to medical researchers due to its insidious onset and poor prognosis. Great attention has been given to the impact of damp-heat syndrome on tumorigenesis and progression, but less attention has been given to damp-heat modeling per se. Studying PC in a proper damp-heat syndrome animal model can recapitulate the actual pathological process and contribute to treatment strategy improvement.

Methods

Here, an optimized damp-heat syndrome mouse model was established based on our prior experience. The Fibonacci method was applied to determine the maximum tolerated dosage of alcohol for mice. Damp-heat syndrome modeling with the old and new methods was performed in parallel of comparative study about general appearance, food intake, water consumption and survival. Major organs, including the liver, kidneys, lungs, pancreas, spleen, intestines and testes, were collected for histological evaluation. Complete blood counts and biochemical tests were conducted to characterize changes in blood circulation. PC cells were subcutaneously inoculated into mice with damp-heat syndrome to explore the impact of damp-heat syndrome on PC growth. Hematoxylin-eosin staining, Masson staining and immunohistochemistry were performed for pathological evaluation. A chemokine microarray was applied to screen the cytokines mediating the proliferation-promoting effects of damp-heat syndrome, and quantitative polymerase chain reaction and Western blotting were conducted for results validation.

Results

The new modeling method has the advantages of mouse-friendly features, easily accessible materials, simple operation, and good stability. More importantly, a set of systematic indicators was proposed for model evaluation. The new modeling method verified the pancreatic tumor-promoting role of damp-heat syndrome. Damp-heat syndrome induced the proliferation of cancer-associated fibroblasts and promoted desmoplasia. In addition, circulating and tumor-located chemokine levels were altered by damp-heat syndrome, characterized by tumor promotion and immune suppression.

Conclusions

This study established a stable and reproducible murine model of damp-heat syndrome in TCM with systematic evaluation methods. Cancer associated fibroblast-mediated desmoplasia and chemokine production contribute to the tumor-promoting effect of damp-heat syndrome on PC.