Development of a Traditional Chinese Medicine Syndrome-Specific Scale for Ulcerative Colitis: The Large Intestine Dampness-Heat Syndrome Questionnaire

Differential proteomic of plasma provides a new perspective on scientific diagnosis and drug screening for dampness heat diarrhea in calves

Dampness heat diarrhea (DHD) is one of the most common syndromes of calf diarrhea. Its complex etiology and lack of objective diagnostic criteria bring great challenges to the diagnosis and treatment of this disease. This study aims to screen some prospective diagnostic biomarkers or therapeutic targets for calves with DHD by investigating the differential protein profiles of plasma between DHD calves and clinically healthy calves by mass spectrometry-based proteomic. A total of 120 DHD calves and 90 clinically healthy calves were divided into two groups randomly, 30 DHD calves and 30 clinically healthy calves in the test group, and 90 DHD calves and 60 clinically healthy calves in the validation group. In the test group, a total of 52 proteins were differentially expressed between calves with DHD and clinically healthy calves, 13 proteins were significantly increased and 39 proteins were significantly decreased. The differentially expressed proteins were associated with the intestinal immune network of IgA production, caffeine metabolism, purine metabolism, and PI3K signaling pathway. In the validation group, 13 proteins were selected from 52 differential expression proteins for parallel reaction monitoring validation to verify their associations with DHD calves. The targeted proteomic results showed that fibronectin precursor (FN1) and apolipoprotein C-IV precursor (APOC4) were significantly associated with DHD in calves, and they were downregulated in sick calves. In conclusion, the differential expression of plasma proteins was associated with DHD pathogenesis in calves, and the FN1 and APOC4 might be the potential clinical biomarkers for diagnosis of DHD in calves, and the intestinal immune network of IgA production, caffeine metabolism, purine metabolism, and PI3K signaling pathway are the candidate targets to treat DHD in calves. Our finding provides a reference for further investigating the pathogenesis, developing techniques of diagnosis, and screening treatment drugs for DHD in calves.

Individualized Diagnosis and Prescription in Traditional Medicine: Decision-Making Process Analysis and Machine Learning-Based Analysis Tool Development

While pattern identification (PI) is an essential process in traditional medicine (TM), it is difficult to objectify since it relies heavily on implicit knowledge. Therefore, this study aimed to propose a machine learning (ML)-based analysis tool to evaluate the clinical decision-making process of PI in terms of explicit and implicit knowledge, and to observe the actual process by which this knowledge affects the choice of diagnosis and treatment in individual TM doctors. Clinical data for the development of the analysis tool were collected using a questionnaire administered to allergic rhinitis (AR) patients and the diagnosis and prescription results of TM doctors based on the completed AR questionnaires. Explicit knowledge and implicit knowledge were defined based on the doctors' explicit scoring and feature evaluations of ML models, respectively. There were many differences between the explicit and implicit importance scores in this study. Implicit importance is more closely related to explicit importance in prescription than in diagnosis. The analysis results for eight doctors showed that our tool could successfully identify explicit and implicit knowledge in the PI process. This is the first study to evaluate the actual process by which explicit and implicit knowledge affect the choice of individual TM doctors and to identify assessment tools for the definition of the decision-making process in diagnosing PI and prescribing herbal treatments by TM clinicians. The assessment tool suggested in this study could be broadly used for the standardization of precision medicine, including TM therapeutics.

Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation

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.

Integrated 16S rDNA Gene Sequencing and Untargeted Metabolomics Analyses to Investigate the Gut Microbial Composition and Plasma Metabolic Phenotype in Calves With Dampness-Heat Diarrhea

Dampness-heat diarrhea (DHD), a common syndrome in Chinese dairy farms, is mainly resulted from digestive system disorders, and accompanied with metabolic disorders in some cases. However, the underlying mechanisms in the intestinal microbiome and plasma metabolome in calves with DHD remain unclear. In order to investigate the pathogenesis of DHD in calves, multi-omics techniques including the 16S rDNA gene sequencing and metabolomics were used to analyze gut microbial compositions and plasma metabolic changes in calves. The results indicated that DHD had a significant effect on the intestinal microbial compositions in calves, which was confirmed by changes in microbial population and distribution. A total of 14 genera were changed, including Escherichia-Shigella, Bacteroides, and Fournierella, in calves with DHD (P < 0.05). Functional analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations indicated that 11 metabolic functions (level 2) were significantly enriched in DHD cases. The untargeted metabolomics analysis showed that 440 metabolites including bilineurin, phosphatidylcholine, and glutamate were significantly different between two groups (VIP > 1 and P < 0.05), and they were related to 67 signal pathways. Eight signal pathways including alpha-linolenic acid, linoleic acid, and glycerophospholipid metabolism were significantly enriched (P < 0.05), which may be potential biomarkers of plasma in calves with DHD. Further, 107 pairs of intestinal microbiota-plasma metabolite correlations were determined, e.g., Escherichia-Shigella was significantly associated with changes of sulfamethazine, butyrylcarnitine, and 14 other metabolites, which reflected that metabolic activity was influenced by the microbiome. These microbiota-metabolite pairs might have a relationship with DHD in calves. In conclusion, the findings revealed that DHD had effect on intestinal microbial compositions and plasma metabolome in calves, and the altered metabolic pathways and microorganisms might serve as diagnostic markers and potential therapeutic targets for DHD in calves.

Based on Network Pharmacology to Explore the Potential Bioactive Compounds and Mechanisms of Zuojin Pill for the Treatment of Ulcerative Colitis

Background

Zuojin Pill (ZJP), a classic prescription, has the potential to prevent ulcerative colitis (UC). However, the active components and mechanisms of ZJP are still arcane. This study aimed to use a network pharmacology approach to find the bioactive compounds and potential action mechanisms of ZJP in the treatment of UC.

Methods

Firstly, the components and putative targets of ZJP were collected based on herbal medicine target databases, and a network containing the interaction between the targets of ZJP and the potential therapeutic targets of UC was established. Then, topological parameters were calculated to identify the key targets in the network and, in turn, to import them into the David database to perform path enrichment analysis.

Results

14 potential therapeutic components of ZJP and 26 key targets were obtained. These targets were related to signal transduction, MAPK cascade, inflammatory response, immune response, and the apoptotic process of UC. Moreover, the PI3K-Akt signaling pathway, MAPK signaling pathway, toll-like receptor signaling pathway, and Prolactin signaling pathway were predicted to participate in ZJP treating UC. Among them, 14 active components of ZJP directly regulate these pathways.

Conclusion

ZJP could alleviate UC through the predicted components and mechanisms. The 14 predicted active components of ZJP may mainly play a therapeutic role for UC through synergistic regulation of the PI3K-Akt signaling pathway and MAPK signaling pathway.

Effect of Rhizoma alismatis on the expression of hub genes in the treatment of gastric cancer

BACKGROUND

The aim of this study was to predict the target genes and pathways of Rhizoma alismatis (RA) in the treatment of gastric cancer (GC) by an bioinformatics analysis.

METHODS

The Traditional Chinese Medicine System Pharmacology database was used to obtain the chemical components and target genes of RA. GC-related genes were downloaded from GeneCard website. GO and KEGG enrichment analyses were used to detect the potential mechanisms of RA targets. Hub genes were identified by protein-protein interaction (PPI) network and then verified by quantitative real-time polymerase chain reaction (qRT-PCR) analysis.

RESULTS

Our analyses identified 34 target genes that contribute to the development of GC. GO analysis showed that the biological functions of the target genes mainly included activation of receptors, including the nuclear receptor, steroid hormone receptor, acetylcholine receptor, G-protein coupled serotonin receptor, serotonin receptor and others. According to KEGG analysis, we found that insulin resistance, galactose metabolism, adipocytokine signaling pathway, breast cancer pathway, and cholinergic synapse were the top 5 pathways involving RA target genes. qRT-PCR and immunohistochemical analysis indicated that RA had significant effects on the expression of hub genes, including MYC, CASP3, SP1, MAPK8, PPARG, FOS, and SLC2A1.

CONCLUSIONS

Our study revealed the multi-component, multi-target, and multi-mechanisms of RA on GC, which suggest novel therapeutics for GC.

Evaluating traditional Chinese medicine diagnostic instruments for functional dyspepsia: systematic review on measurement properties

Background

Pattern diagnosis-guided treatments in Traditional Chinese Medicine (TCM) has been recognised by the eleventh revision of the International Classification of Diseases (ICD-11). Accurate pattern diagnosis requires reliable and valid diagnostic instruments that guide the collection of TCM clinical data without bias. This study synthesised the existing TCM diagnostic instruments for functional dyspepsia (FD) and appraised their quality regarding their development process and measurement properties.

Methods

Seven electronic databases were searched for validation studies on TCM diagnostic instruments for FD. Synthesis and appraisal of the included studies were performed following the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) Initiative guidelines adapted for TCM diagnostic instruments. Risk of bias assessment was conducted using the COSMIN Risk of Bias Checklist.

Results

Five studies were included, with five unique TCM diagnostic instruments for FD identified. All five diagnostic instruments were of inadequate quality in terms of their development process, implying a shortcoming in their relevance, comprehensibility, and comprehensiveness. Only the criterion validity of Stomach Qi Deficiency Pattern Assessment Scale was of sufficient quality and had no risk of bias in its validation.

Conclusion

The quality of TCM diagnostic instruments for FD warrants urgent improvements. None of them was considered reliable or valid for guiding TCM pattern diagnosis. To support the evidence base of the standardization of TCM patterns in ICD-11, TCM diagnostic instruments should be developed and validated rigorously under the COSMIN guidelines. Amendments should be made on the guidelines to accommodate the features and uniqueness of TCM diagnostic process.

Efficacy and Safety of Sophora flavescens (Kushen) Based Traditional Chinese Medicine in the Treatment of Ulcerative Colitis: Clinical Evidence and Potential Mechanisms

Background: Radix Sophorae flavescentis (Kushen), a Chinese herb, is widely used in the treatment of ulcerative colitis (UC) with damp-heat accumulation syndrome (DHAS) according to traditional Chinese medicine (TCM) theory. Objective: The aim of this study was to illuminate the clinical efficacy and potential mechanisms of Kushen-based TCM formulations in the treatment of UC with DHAS. Materials and Methods: A systematic literature search was performed in the PubMed, EMBASE, Chinese Biomedical Literature database, China National Knowledge Infrastructure database, Chongqing VIP Information database, and Wanfang database for articles published between January 2000 and July 2020 on randomized controlled trials (RCTs) that used Kushen-based TCM formulations in the treatment of UC with DHAS. A network pharmacology approach was conducted to detect the potential pathways of Kushen against UC with DHAS. Results: Eight RCTs with a total of 983 subjects were included in the meta-analysis. Compared with the control subjects (5-aminosalicylic acid therapy), those who received Kushen-based TCM formulations for the treatment of UC showed a significantly higher clinical remission rate (RR = 1.20, 95% CI: [1.04, 1.38], p = 0.02) and lower incidence of adverse events (RR = 0.63, 95% CI [0.39, 1.01], p = 0.06). A component-target-pathway network was constructed, indicating five main components (quercetin, luteolin, matrine, formononetin, and phaseolin), three major targets (Interleukin-6, Myc proto-oncogene protein, and G1/S-specific cyclin-D1) and one key potential therapeutic pathway (PI3K-Akt signaling) of Kushen against UC with DHAS. Conclusion: Kushen-based TCM formulations provide good efficacy and possess great potential in the treatment of UC. Large-scale and high-quality clinical trials and experimental verification should be considered for further confirmation of the efficacy of Kushen-based formulations.

Development of a Diagnostic Questionnaire for Damp Phlegm Pattern and Blood Stasis Pattern in Coronary Heart Disease Patients (CHD-DPBSPQ)

Background

The aim was to develop a diagnostic questionnaire for damp phlegm pattern and blood stasis pattern in coronary heart disease patients (CHD-DPBSPQ).

Methods

The standard procedures of questionnaire development were carried out to develop and assess CHD-DPBSPQ. The patients were assessed using the CHD-DPBSPQ, CHD-DPPQ, and CHD-BSPQ. Four methods were used to select the items on the CHD-DPBSPQ in a pilot study based on data from a Guizhou tertiary grade A hospital. Cronbach's alpha and the split-half reliability, test-retest reliability, content validity, criterion validity, construct validity, and convergent validity were determined in a validation study using a nationwide sample.

Results

After item selection, the CHD-DPBSPQ contained 15 items in two domains: the phlegm domain (9 items) and the blood stasis domain (6 items). For the CHD-DPBSPQ, the alpha coefficient was 0.88, the split-half coefficient was 0.90, and the intraclass correlation coefficient was 0.83. The range of the item-level content validity index (I-CVI) was 0.71 to 1.0 and that of the scale-level content validity index/average (Scale-CVI/Ave) was 0.97. The domain scores on the CHD-DPBSPQ were in close relation to the scores on a questionnaire for damp phlegm pattern in coronary heart disease patients (CHD-DPPQ) and a questionnaire for blood stasis pattern in coronary heart disease patient (CHD-BSPQ) (P < 0.01). The root mean square error of approximation (RMSEA) was equal to 0.05 (90% CI: 0.044, 0.059). Convergent validity was demonstrated with a moderate correlation.

Conclusion

The CHD-DPBSPQ is a reliable and valid instrument.

Mechanisms of Paeonia lactiflora in Treatment of Ulcerative Colitis: A Network Pharmacological Study

BACKGROUND Paeonia lactiflora is the main active ingredient of peony decoction, which is used to treat ulcerative colitis (UC) in traditional Chinese medicine (TCM). Network pharmacology indicates the multiple interactions among genes, proteins, and metabolites associated with diseases and drugs from the network perspective, which shows the multi-component and multi-target attributes of TCM. This study predicted the pharmacological mechanism of Paeonia lactiflora in the treatment of UC by network pharmacological method. MATERIAL AND METHODS Chemical constituents of Paeonia lactiflora were searched from TCMSP data, gene names of target sites were extracted from UniProt database, and disease targets of ulcerative colitis were obtained from the CTD disease database. Use Venny online tools to obtain common targets for drugs and diseases. The DAVID database was used to enrich GO and KEGG for the common target, and the related functions and pathways were obtained. Cytoscape 3.7.1 was used to construct the 'drug-compound-target-disease' network. RESULTS There are 70 common target genes between Paeonia lactiflora and UC. GO analysis showed that the biological functions of the common target genes of Paeonia lactiflora and UC include response to lipopolysaccharide, response to estradiol, response to drug, positive regulation of nitric oxide biosynthetic process, and steroid hormone-mediated signaling pathway. Enrichment of the KEGG signaling pathway mainly involves signaling pathways, including Pathways in cancer, TNF signaling pathway, Tuberculosis, Hepatitis B, and Toxoplasmosis. CONCLUSIONS The network pharmacology intuitively shows the multi-component, multi-target, and multi-channel pharmacological effects of Paeonia lactiflora on UC, and provides a scientific basis for studying the mechanism of the effect of Paeonia lactiflora on UC.

Expert Consensus of Syndrome Differentiation for Phlegm Turbidity Syndrome for Coronary Heart Disease

Objective

The purpose of the study was to form a questionnaire of expert consensus about phlegm turbidity syndrome of coronary heart disease (CHD) using literature method and Delphi method, which could provide the objective evidences for the clinical diagnosis and treatment for CHD.

Method

The CBM, CNKI, VIP, and PubMed were searched. The articles about phlegm turbidity syndrome for CHD with the definite related four diagnostic data were included. Based on the results of the literature method, two rounds of Delphi method were conducted. The TCM experts about CHD were enrolled. Concentration and coordination index of the experts were used to select the items.

Results

Literature method: A total of 118 articles were included. Greasy fur, slippery pulse, chest fullness or chest pain, anorexia, nausea and vomiting, vertigo, excessive phlegm, abdominal fullness, head heaviness, obesity, stringy pulse, physical heaviness, soft pulse, somnolence, fatigue, and pale tongue (16 items) had the relatively high proportion, and they were eligible for Delphi process. Delphi method: A total of 93 experts (22 for the first round, 71 for the second round) were included. The reliability of the items was 0.885 for all the experts. The 16 items were not significantly different between the two rounds (P>0.05). According to the results of mean, frequency, sum of ranks and coefficient of variation, the item of nausea and vomiting, somnolence, pale tongue, and soft pulse were deleted.

Conclusions

The questionnaire of phlegm turbidity syndrome of CHD was established, with good reliability. The sensitivity and specificity of the questionnaire are still necessary to further validate for clinical or scientific use.