A Large-Scale, Multi-Center Urine Biomarkers Identification of Coronary Heart Disease in TCM Syndrome Differentiation

Regulation of optimized new Shengmai powder on cardiomyocyte apoptosis and ferroptosis in ischemic heart failure rats: The mediating role of phosphatidylinositol-3-kinase/protein kinase B/tumor protein 53 signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Optimized New Shengmai Powder (ONSMP) is a sophisticated traditional Chinese medicinal formula renowned for bolstering vital energy, optimizing blood circulation, and mitigating fluid retention. After years of clinical application, ONSMP has shown a significant impact in improving myocardial injury and cardiac function and has a positive effect on treating heart failure. However, many unknowns exist about the molecular biological mechanisms of how ONSMP exerts its therapeutic effects, which require further research and exploration.

AIM OF THE STUDY

Exploring the potential molecular biological mechanisms by which ONSMP ameliorates cardiomyocyte apoptosis and ferroptosis in ischemic heart failure (IHF).

MATERIALS AND METHODS

First, we constructed a rat model of IHF by inducing acute myocardial infarction through surgery and using echocardiography, organ coefficients, markers of heart failure, antioxidant markers, and histopathological examination to assess the effects of ONSMP on cardiomyocyte apoptosis and ferroptosis in IHF rats. Next, we used bioinformatics analysis techniques to analyze the active components, signaling pathways, and core targets of ONSMP and calculated the interactions between core targets and corresponding elements. Finally, we detected the positive expression of apoptosis and ferroptosis markers and core indicators of signaling pathways by immunohistochemistry; detected the mean fluorescence intensity of core indicators of signaling pathways by immunofluorescence; detected the protein expression of signaling pathways and downstream effector molecules by western blotting; and detected the mRNA levels of p53 and downstream effector molecules by quantitative polymerase chain reaction.

RESULTS

ONSMP can activate the Ser83 site of ASK by promoting the phosphorylation of the PI3K/AKT axis, thereby inhibiting the MKK3/6-p38 axis and the MKK4/7-JNK axis signaling to reduce p53 expression, and can also directly target and inhibit the activity of p53, ultimately inhibiting p53-mediated mRNA and protein increases in PUMA, SAT1, PIG3, and TFR1, as well as mRNA and protein decreases in SLC7A11, thereby inhibiting cardiomyocyte apoptosis and ferroptosis, effectively improving cardiac function and ventricular remodeling in IHF rat models.

CONCLUSION

ONSMP can inhibit cardiomyocyte apoptosis and ferroptosis through the PI3K/AKT/p53 signaling pathway, delaying the development of IHF.

Comparative Pharmacokinetic Study of 5 Active Ingredients after Oral Administration of Zuogui Pill in Osteoporotic Rats with Different Syndrome Types

Zuogui Pill is a kidney-yin-tonifying formula in traditional Chinese medicine that is widely used to manage osteoporosis with kidney-yin-deficiency in China. Herein, an efficient and accurate high-performance liquid chromatography-tandem mass spectrometry method was developed to determine the concentrations of 5 bioactive compounds in rat plasma following oral administration of Zuogui Pill. Because drug absorption and distribution differ under physiological and pathological conditions, the established method was used to quantify blood components and dynamic change in osteoporotic rats with different syndrome types. Moreover, integrated pharmacokinetic study was conducted to describe the overall pharmacokinetic characteristics of traditional Chinese medicine. The results showed that the absorption, distribution, and metabolism of Zuogui Pill varied widely under different states. The bioavailability of most active components showed significant advantages in osteoporotic rats with kidney-yin-deficiency, which corresponds to the opinion that Zuogui Pill has the effect of nourishing kidney-yin. It is hoped that this finding could interpret the pharmacodynamic substances and mechanism of Zuogui Pill in the treatment of osteoporosis with kidney-yin-deficiency.

Efficacy and safety of anti-obesity herbal medicine focused on pattern identification: A systematic review and meta-analysis

BACKGROUND

Herbal medicine based on pattern identification (PI) is used widely in Traditional Chinese Medicine. Proper herbal medicine based on PI has been suggested for effective weight reduction and decreasing the adverse events. This systematic review examined the effectiveness and safety of herbal medicine, focusing on PI in treating obesity.

METHODS

Eight electric databases were used for searching randomized controlled trials (RCT) (to August 31, 2021). RCTs which prescribed herbal medicine to obese patients based on PI were included. Body weight (BW) and body mass index (BMI) were the primary outcomes. The risk of bias was assessed using Cochrane risk of bias tool, and the meta-analysis was conducted. Grading the evidence was conducted by using GRADEpro.

RESULTS

Sixteen RCTs (1052 patients) were included: 2 studies compared herbal medicine to placebo (128 patients); 2 studies compared them to western medication (161 patients); 12 studies compared them with usual care (763 patients). The meta-analysis showed that the herbal formulas reduced the BW and BMI without significant Adverse events compared to the control group (BW: mean difference  = -4.10, 95% confidence interval: -5.14 to -3.06, I2 = 2% and BMI: mean difference = -1.53, 95% confidence interval: -1.88 to -1.19, I2 = 25%). Moderate-quality evidence on the primary outcomes was found.

CONCLUSIONS

Herbal medicine - has good clinical efficacy and safety in treating obesity. This study has limitations that some literatures with high risk of bias in blinding or without using a standardized diagnosis of PI were included. However, the current evidence suggests the possibility of precision medicine using PI.

Exploring the "gene-protein-metabolite" network of coronary heart disease with phlegm and blood stasis syndrome by integrated multi-omics strategy

Background: According to the theory of traditional Chinese medicine, phlegm and blood stasis (PBS) is the pathological basis for coronary heart disease (CHD). This study aimed to explore the biological basis of PBS syndrome in CHD. Methods: Using a strategy that integrated RNA-seq, DIA-based proteomics, and untargeted metabolomics on 90 clinic samples, we constructed a "gene-protein-metabolite" network for CHD-PBS syndrome. We expanded the sample size and validated the differential genes and metabolites in the network through enzyme-linked immunosorbent assay. Results: Our findings revealed that the "gene-protein-metabolite" network of CHD-PBS syndrome included 33 mRNAs, four proteins, and 25 metabolites. JNK1, FOS, CCL2, CXCL8, PTGS2, and CSF1 were all poorly expressed in the PBS group during the sequencing stage, whereas arachidonic acid (AA) was highly expressed. During the validation stage, JNK1, AP-1, CCL2, and CXCL8 were poorly expressed, whereas PTGS2, CSF1, and AA were highly expressed. The area under the receiver operating curve was as follows: CSF1 [0.9635, 95%CI (0.9295, 0.9976)] >JNK1 [0.9361, 95% CI (0.8749, 0.9972)] >CXCL8 [0.8953, 95% CI (0.8222, 0.9684)] > CCL2 [0.8458, 95% CI (0.7676, 0.9241)] >AP-1 [0.7884, 95%CI (0.6869, 0.8899)]. The logistic regression model composed of CSF1 and JNK1 showed the greatest diagnostic value and significance for PBS syndrome. Conclusion: PBS syndrome is characterized by low levels of FOS, AP-1, CCL2, CXCL8, and JNK1 and elevated levels of PTGS2 and CSF1, implying that the AA metabolism is abnormal and that the JNK/AP-1 pathway is inhibited. PBS syndromes, as a subtype of CHD, may have unique molecular changes. Background. Globally, coronary heart disease (CHD) is the leading cause of death, and this would likely continue until 2030 (Mirzaei et al., 2009, 95, 740-746). According to the disease course, CHD can be classified as chronic stable CHD (or chronic coronary syndrome) and acute coronary syndrome (ACS) (Katus et al., 2017; Knuuti, 2019). Although stable CHD is not as lethal as ACS, it has a varied incidence range and patients with CHD have prolonged angina. Some symptoms of stable angina are alleviated with pharmacological therapy, but it cannot eliminate recurrent angina (Rousan et al., 2017). The clinical outcomes were not significantly improved in patients who underwent revascularization compared with those who received optimal pharmacological therapy (Shaw et al., 2008; Antman and Braunwald, 2020). A bottleneck appears to exist in CHD treatment, and traditional Chinese medicine (TCM) can act as a favorable complement. Because of its individualized treatment approach, TCM is widely practiced in eastern civilizations (Teng et al., 2016). TCM has become a principal complement in western countries (Wieland et al., 2013). Like "disease" is used in western medicine, "syndrome" is used in TCM to comprehend anomalous human conditions on the basis of patients' symptoms, tongue, and pulse (Li et al., 2012). On the basis of disease-syndrome diagnose, a TCM doctor can subclassify CHD patients into various categories, such as phlegm and blood stasis (PBS) syndrome, cold congealing and Qi stagnation syndrome, and Qi stagnation and blood stasis syndrome. PBS syndrome has recently emerged as a hot research topic in the TCM field. Objective diagnosis, expert consultations, and efficacy evaluation scales have been developed for PBS syndrome (Ren et al., 2020; Liu et al., 2021; Zheng et al., 2022). The concept of "omics" originates from the genome. It refers to the vocabulary generated by biological molecules at different levels to describe high-sequence molecular biological data resources (Dai and Shen, 2022). RNA, protein, and metabolites decipher the essence of complex etiologies, and the integration of transcriptomics, proteomics, and metabolomics are becoming a promising research mode (Pan et al., 2022). Multi-omics studies have revealed the biological characteristics of APOE transgenic mice, bronchopulmonary dysplasia, and plant tolerant to heavy metals (Singh et al., 2016; Lal et al., 2018; Mohler et al., 2020). Over the past few years, many academic achievements related to CHD-PBS syndrome have been accrued in the single-omic area. For example, Zhou identified the differential metabolites between PBS syndrome and Qi and Yin deficiency syndrome by using the urine samples of 1072 volunteers. Some of the specific metabolites of PBS syndrome are pyroglutamic acid, glutaric acid, glucose, mannitol, and xanthine (Zhou et al., 2019). Li's metabolomic study suggested that valine, leucine, isoleucine, and glycerol phospholipid metabolism could represent PBS syndrome (Zheng et al., 2022). Although some progress has been made in the understanding of PBS syndrome in CHD through the studies conducted, some issues still exist, such as a single-omics level, a lack of in-depth research, an inability to verify each other's research results, and a lack of validation of research conclusions. Overall, a systematic description of the biological foundation of PBS syndrome is lacking. Thus, the present study utilizes system biology methodologies and constructs a multi-omics network by integrating differential genes, proteins, and metabolites to systematically and comprehensively reveal the biological basis of CHD-PBS syndrome. The current study explored 1) the characteristics of the transcriptome, proteome, and metabolome for CHD-PBS syndrome; 2) the "gene-protein-metabolite" network based on differential genes (DGs), differential proteins (DPs), and differential metabolites (DMs); 3) the key biological process and metabolic pathway most related to PBS syndrome; and 4) quantitative results and the diagnostic potential of biomarkers for PSB syndrome. Materials and methods. Multi-omics sequencing, bioinformatics analysis, and clinical validation research strategy. We collected the blood samples from healthy subjects as well as CHD patients with PBS and non-phlegm and blood stasis (NPBS) syndrome to compare the differences between them by subjecting the samples to the transcriptome, proteome, and metabolomics analyses. Bioinformatics analysis identified differential molecules as well as related biological processes and pathways. Next, the "gene-protein-metabolite" network was constructed using the MetaboAnalyst database, String database, and Cytoscape software. We selected molecules with strong centrality and biological association as potential PBS syndrome biomarkers and recruited more volunteers for further validation by enzyme-linked immunosorbent assay (ELISA). Finally, the ROC curve was utilized to assess the level and diagnostic efficacy of various molecules (Figure 1).

Distinct common signatures of gut microbiota associated with damp-heat syndrome in patients with different chronic liver diseases

Background: Chronic hepatitis B (CHB) and non-alcoholic fatty liver disease (NAFLD) are prevalent in China. According to traditional Chinese medicine (TCM) theory, damp-heat (DH) syndrome is common in chronic liver disease. However, the biological characteristics related to quantitative diagnosis remain to be determined. This study aimed to identify the consistent alterations in the gut microbiota associated with DH syndrome in patients with CHB or NAFLD. Methods: A total of 405 individuals were recruited, of which 146 were participants who met the consistent TCM diagnosis by three senior TCM physicians and were typical syndromes. All participants were required to provide fresh stool and serum samples. The gut microbiota was assessed by fecal 16S rRNA gene sequencing, and the serum metabolite profiles of participants were quantified by an ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system. DH syndrome-related bacteria taxa were identified based on the 146 individuals with typical syndromes and validated in all 405 volunteers. Results: The results showed that CHB and NAFLD patients with typical TCM DH syndrome had consistently elevated serum total bile acid (TBA) levels. Significant alterations in microbial community were observed according to TCM syndromes identification. A total of 870 microbial operational taxonomic units and 21 serum metabolites showed the same variation trends in both the CHB and NAFLD DH syndrome groups. The functional analysis predicts consistent dysregulation of bile acid metabolism. Five genera (Agathobacter, Dorea, Lachnospiraceae_NC2004_group, Subdoligranulum, and unclassified_c__Clostridia) significantly decreased in abundance in patients with DH syndrome. We utilize these five genera combined with TBA to construct a random forest classifier model to predict TCM diagnosis. The diagnostic receiver-operator characteristic (ROC) areas for DH syndrome were 0.818 and 0.791 in internal tenfold cross-validation and the test set based on all 405 individuals, respectively. Conclusion: There are common signatures of gut microbiota associated with DH syndrome in patients with different chronic liver diseases. Serum TBA combined with DH-related genera provides a good diagnostic potential for DH syndrome in chronic liver disease.

Appraisal of treatment outcomes in integrative medicine using metabonomics: Taking non-alcoholic fatty liver disease with spleen deficiency syndrome as an example

OBJECTIVE

Appraisal of treatment outcomes in integrative medicine is a challenge due to a gap between the concepts of Western medicine (WM) disease and traditional Chinese medicine (TCM) syndrome. This study presents an approach for the appraisal of integrative medicine that is based on targeted metabolomics. We use non-alcoholic fatty liver disease with spleen deficiency syndrome as a test case.

METHODS

A patient-reported outcome (PRO) scale was developed based on literature review, Delphi consensus survey, and reliability and validity test, to quantitatively evaluate spleen deficiency syndrome. Then, a metabonomic foundation for the treatment of non-alcoholic fatty liver disease with spleen deficiency syndrome was identified via a longitudinal interventional trial and targeted metabolomics. Finally, an integrated appraisal model was established by identifying metabolites that responded in the treatment of WM disease and TCM syndrome as positive outcomes and using other aspects of the metabonomic foundation as independent variables.

RESULTS

Ten symptoms and signs were included in the spleen deficiency PRO scale. The internal reliability, content validity, discriminative validity and structural validity of the scale were all qualified. Based on treatment responses to treatments for WM disease (homeostasis model assessment of insulin resistance) or TCM syndrome (spleen deficiency PRO scale score) from a previous randomized controlled trial, two cohorts comprised of 30 participants each were established for targeted metabolomics detection. Twenty-five metabolites were found to be involved in successful treatment outcomes to both WM and TCM, following quantitative comparison and multivariate analysis. Finally, the model of the integrated appraisal system was exploratively established using binary logistic regression; it included 9 core metabolites and had the prediction probability of 83.3%.

CONCLUSION

This study presented a new and comprehensive research route for integrative appraisal of treatment outcomes for WM disease and TCM syndrome. Critical research techniques used in this research included the development of a TCM syndrome assessment tool, a longitudinal interventional trial with verified TCM treatment, identification of homogeneous metabolites, and statistical modeling.

Integrated Metabolomics and Lipidomics Approach for the Study of Metabolic Network and Early Diagnosis in Cerebral Infarction

Cerebral infarction (CI) remains a major cause of high mortality and long-term disability worldwide. The exploration of biomarkers and pathogenesis is crucial for the early diagnosis of CI. Although the understanding of metabolic perturbations underlying CI has increased in recent years, the relationship between altered metabolites and disease pathogenesis has only been partially elucidated and requires further investigation. In this study, we performed an integrated metabolomics and lipidomics analysis on 59 healthy subjects and 47 CI patients. Ultimately, 49 metabolite and 68 lipid biomarkers were identified and enriched in 24 disturbed pathways. The metabolic network revealed a significant interaction between altered lipids and other metabolites. Using receiver operating characteristic curve (ROC) analysis, a panel of three polar metabolites and seven lipids was optimized in the training set, which included taurine, oleoylcarnitine, creatinine, PE(22:6/P-18:0), Cer 34:2, GlcCer(d18:0/18:0), DG 44:0, LysoPC(16:0), 22:6-OH/LysoPC, and TAG58:7-FA22:4. Subsequently, a support vector machine (SVM) model was constructed and validated, which showed excellent predictive ability in the validation set. Thereby, the integrated metabolomics and lipidomics approach could contribute to a comprehensive understanding of the metabolic dyshomeostasis associated with the pathogenesis of underlying CI. The present research may promote a deeper understanding and early diagnosis of CI in the clinic. All raw data were deposited in PRIDE (PXD036199).

A multi-step approach for tongue image classification in patients with diabetes

BACKGROUND

In China, diabetes is a common, high-incidence chronic disease. Diabetes has become a severe public health problem. However, the current diagnosis and treatment methods are difficult to control the progress of diabetes. Traditional Chinese Medicine (TCM) has become an option for the treatment of diabetes due to its low cost, good curative effect, and good accessibility.

OBJECTIVE

Based on the tongue images data to realize the fine classification of the diabetic population, provide a diagnostic basis for the formulation of individualized treatment plans for diabetes, ensure the accuracy and consistency of the TCM diagnosis, and promote the objective and standardized development of TCM diagnosis.

METHODS

We use the TFDA-1 tongue examination instrument to collect the tongue images of the subjects. Tongue Diagnosis Analysis System (TDAS) is used to extract the TDAS features of the tongue images. Vector Quantized Variational Autoencoder (VQ-VAE) extracts VQ-VAE features from tongue images. Based on VQ-VAE features, K-means clustering tongue images. TDAS features are used to describe the differences between clusters. Vision Transformer (ViT) combined with Grad-weighted Class Activation Mapping (Grad-CAM) is used to verify the clustering results and calculate positioning diagnostic information.

RESULTS

Based on VQ-VAE features, K-means divides the diabetic population into 4 clusters with clear boundaries. The silhouette, calinski harabasz, and davies bouldin scores are 0.391, 673.256, and 0.809, respectively. Cluster 1 had the highest Tongue Body L (TB-L) and Tongue Coating L (TC-L) and the lowest Tongue Coating Angular second moment (TC-ASM), with a pale red tongue and white coating. Cluster 2 had the highest TC-b with a yellow tongue coating. Cluster 3 had the highest TB-a with a red tongue. Group 4 had the lowest TB-L, TC-L, and TB-b and the highest Per-all with a purple tongue and the largest tongue coating area. ViT verifies the clustering results of K-means, the highest Top-1 Classification Accuracy (CA) is 87.8%, and the average CA is 84.4%.

CONCLUSIONS

The study organically combined unsupervised learning, self-supervised learning, and supervised learning and designed a complete diabetic tongue image classification method. This method does not rely on human intervention, makes decisions based entirely on tongue image data, and achieves state-of-the-art results. Our research will help TCM deeply participate in the individualized treatment of diabetes and provide new ideas for promoting the standardization of TCM diagnosis.

Exploring the pivotal variables of tongue diagnosis between patients with acute ischemic stroke and health participants

Background and aim

Stroke is a major cause of disability worldwide, and ischemic stroke is the most common type of stroke. The prevention and treatment of ischemic stroke remain a challenge worldwide. Traditional Chinese medicine (TCM) is often sought to provide an alternative therapy for the prevention and rehabilitation intervention of ischemic stroke in Taiwan. Therefore, this study explored the pivotal variables of tongue diagnosis among acute ischemic stroke and healthy participants in middle and older age.

Experimental procedure

This was a cross-sectional and case-controlled study. Data were collected from 99 patients with acute ischemic stroke and 286 healthy participants who received tongue diagnoses at Changhua Christian Hospital (CCH) from September 1, 2014, to December 31, 2016. Tongue features were extracted using the automatic tongue diagnosis system. Nine tongue features, including tongue shape, tongue color, fur thickness, fur color, saliva, tongue fissures, ecchymoses, teeth marks, and red spots were analyzed.

Results and conclusion

Objective image analysis techniques were used to identify significant differences in the many tongue features between patients with acute ischemic stroke and individuals without stroke. According to the logistic regression analysis, pale tongue color (OR:5.501, p = 0.001), bluish tongue color (OR:4.249, p = 0.014), ecchymoses (OR:1.058, p < 0.001), and tongue deviation angle (OR:1.218, p < 0.001) were associated with significantly increased odds ratios for acute ischemic stroke. The research revealed that tongue feature abnormalities were significantly related to the occurrence of ischemic stroke.

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TCM provides a complementary therapy for stroke.

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ATDS serves as a non-invasive, objective, and reliable tool in TCM.

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A significantly higher prevalence of abnormal tongue features in acute ischemic stroke.

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Tongue diagnosis could serve as a feasible predictor of stroke.

The Use of Artificial Intelligence in Complementary and Alternative Medicine: A Systematic Scoping Review

Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status.

Objective: To categorize and seek the current usage of AI in CAM.

Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies.

Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians.

Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.

Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.

Study protocol: Traditional Chinese Medicine (TCM) syndrome differentiation for heart failure patients and its implication for long-term therapeutic outcomes of the Qiliqiangxin capsules

Background

Syndrome differentiation is a commonly used methodology and practice in Traditional Chinese Medicine (TCM) guiding the diagnosis and treatment of diseases including heart failure (HF). However, previous clinical trials seldom consider the impact of syndrome patterns on the outcome evaluation of TCM formulae. Qiliqiangxin (QLQX) capsule is a TCM formula with cardiotonic effect to improve the cardiovascular function for heart failure with proven efficacy from well-designed clinical trials. Though, there is no clinical trial with a large sample size and long assessment period that considers the relationship between TCM syndrome differentiation and the treatment efficacy of QLQX. In the present study, we design a study protocol to evaluate the relationship between TCM syndrome differentiation and the severity of heart failure as well as its progression. Furthermore, we will evaluate the impact of the TCM syndrome patterns on the efficacy of QLQX in the outcome of heart failure.

Methods

This is a clinical study conducted in conjunction with an ongoing clinical trial (QUEST Study) by sharing the parent patient populations but with different aims and independent designed roadmaps to investigate the TCM syndrome pattern distributions and the impacts of syndrome pattern types on the efficacy of QLQX in HF treatment. The clinical trial involves over 100 hospitals in mainland China and Hong Kong SAR with 3080 HF patients. By assessing the morbidity and re-hospitalization, we will verify and apply a modified TCM Questionnaire to collect the clinical manifestations of HF and acquire the tongue images of the patients to facilitate the syndrome differentiation. We will base on the “2014 Consensus from TCM experts on diagnosis and treatment of chronic heart failure” to evaluate the TCM syndromes for the patients. A pilot study with at least 600 patients will be conducted to evaluate the reliability, feasibility and validity of the modified TCM questionnaire for syndrome differentiation of HF and the sample size is calculated based on the confidence level of 95%, population size of 3080 and 5% margin of error. Secondly, we will investigate the characteristic of TCM syndrome distribution of HF patients and its correlation with the functional and biochemical data. Furthermore, we will evaluate the relationship between the TCM syndrome patterns and the efficacy of QLQX in the treatment of heart failure. Lastly, we will investigate the implication of tongue diagnosis in the severity and therapeutic outcome of HF.

Expect outcomes

To our knowledge, this is the first large scale clinical trial to evaluate the impacts of TCM syndrome differentiation on the progression and therapeutic outcome of HF patients and explore the diagnostic value of TCM Tongue Diagnosis in HF patients. We expect to obtain direct clinical evidence to verify the importance of TCM syndrome differentiation for the diagnosis and treatment of HF.

Trial Registration: The trial was registered at Chinese Clinical Trial Registry, http://www.chictr.org.cn. (Registration No.: ChiCTR1900021929); Date: 2019-03-16.

Artificial Intelligence Predicts Severity of COVID-19 Based on Correlation of Exaggerated Monocyte Activation, Excessive Organ Damage and Hyperinflammatory Syndrome: A Prospective Clinical Study

Background

Prediction of the severity of COVID-19 at its onset is important for providing adequate and timely management to reduce mortality.

Objective

To study the prognostic value of damage parameters and cytokines as predictors of severity of COVID-19 using an extensive immunologic profiling and unbiased artificial intelligence methods.

Methods

Sixty hospitalized COVID-19 patients (30 moderate and 30 severe) and 17 healthy controls were included in the study. The damage indicators high mobility group box 1 (HMGB1), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), alanine aminotransferase (ALT), extensive biochemical analyses, a panel of 47 cytokines and chemokines were analyzed at weeks 1, 2 and 7 along with clinical complaints and CT scans of the lungs. Unbiased artificial intelligence (AI) methods (logistic regression and Support Vector Machine and Random Forest algorithms) were applied to investigate the contribution of each parameter to prediction of the severity of the disease.

Results

On admission, the severely ill patients had significantly higher levels of LDH, IL-6, monokine induced by gamma interferon (MIG), D-dimer, fibrinogen, glucose than the patients with moderate disease. The levels of macrophage derived cytokine (MDC) were lower in severely ill patients. Based on artificial intelligence analysis, eight parameters (creatinine, glucose, monocyte number, fibrinogen, MDC, MIG, C-reactive protein (CRP) and IL-6 have been identified that could predict with an accuracy of 83−87% whether the patient will develop severe disease.

Conclusion

This study identifies the prognostic factors and provides a methodology for making prediction for COVID-19 patients based on widely accepted biomarkers that can be measured in most conventional clinical laboratories worldwide.

A Traditional Chinese Medicine Syndrome Classification Model based on Cross-FGCNN: Model Development and Validation (Preprint)

Background

Nowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy.

Objective

This study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases.

Methods

The relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data.

Results

The data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%.

Conclusions

The main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM.

Serum metabolic profiling of traditional Chinese medicine syndromes in patients with diarrhea-predominant irritable bowel syndrome

OBJECTIVE

The clinical symptoms of diarrhea-predominant irritable bowel syndrome (IBS-D) can be effectively improved by traditional Chinese medicine (TCM) treatment, based on the usage of specific therapies for different TCM syndromes. However, in the stage of diagnosis, the standard criteria for the classification of TCM syndrome were still deficient. Through serum metabolic profiling, this study aimed to explore potential biomarkers in IBS-D patients with different TCM syndromes, which can assist in diagnosis of the disease.

METHODS

Serum samples were collected from healthy controls (30 cases), IBS-D patients with Liver-Stagnation and Spleen-Deficiency syndrome (LSSD, 30 cases), Yang Deficiency of Spleen and Kidney syndrome (YDSK, 11 cases) and Damp Abundance due to Spleen-Deficiency syndrome (DASD, 22 cases). Serum metabolic profiling was conducted by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The potential biomarkers were screened by orthogonal partial least square-discriminate analysis, while metabolic pathways undergoing alterations were identified by pathway enrichment analysis in MetaboAnalyst 4.0.

RESULTS

Overall, 34 potential biomarkers were identified in LSSD group, 36 in YDSK group and 31 in DASD group. And the 13 metabolites shared by three groups were determined as the potential biomarkers of IBS-D. Glycerophospholipid metabolism was disturbed significantly in IBS-D patients, which may play a role in IBS-D through inflammation. What's more, three TCM syndromes have the specific potential biomarkers in glycerophospholipid metabolism.

CONCLUSION

The serum metabolomics revealed that different TCM syndrome types in IBS-D may have different metabolic patterns during disease progression and glycerophospholipid metabolism was one of the pathways, whose metabolism was disturbed differently among three TCM syndromes in IBS-D. Therefore, the specific potential biomarkers in glycerophospholipid metabolism of three TCM syndromes in IBS-D can serve as the objective indicators, which can facilitate the TCM-syndrome objective classification of IBS-D.

Metabonomics Study on Serum Characteristic Metabolites of Psoriasis Vulgaris Patients With Blood-Stasis Syndrome

Psoriasis is a chronic, refractory, systemic inflammatory skin disease. Traditional Chinese medicine (TCM) shows unique advantage in the treatment of psoriasis based on syndrome differentiation. An untargeted high-throughput metabonomics method based on liquid chromatography coupled to mass spectrometry was applied to study the serum metabolic characteristics in different TCM syndrome types in patients with psoriasis vulgaris (PV), and to discover potential serum biomarkers for its pathogenesis on the endogenous metabolite differentiation basis. The serum metabolic profiles of 45 healthy controls and 124 patients with PV (50 in the blood-stasis group, 30 in the blood-heat group, and 44 in the blood-dryness group) were acquired. The raw spectrometric data were processed using multivariate statistical analysis, and 14 biomarkers related to TCM syndrome differentiation and psoriasis types were screened and identified. The blood-stasis syndrome group showed abnormal lipid metabolism, which was characterized by a low level of phosphatidylcholine (PC) and a high level of lysophosphatidylcholine (LPC). We propose that platelet-activating factor can be applied as a potential biomarker in clinical diagnosis and differentiation of PV with blood-stasis syndrome. The difference in the serum metabolites among PV types with different TCM syndromes and healthy control group illustrated the objective material basis in TCM syndrome differentiation and classification of psoriasis.

Exploration of chemical markers using a metabolomics strategy and machine learning to study the different origins of Ixeris denticulata (Houtt.) Stebb

As a generally edible plant, Ixeris denticulata (Houtt.) Stebb is widely distributed in China. Its medicinal value has attracted much attention in recent years. However, the chemical markers that cause quality and taste differences in I. denticulata from different regions are currently unclear. In this study, samples from 8 different origins were collected and analysed by UPLC-Q-TOF/MS. A metabolomics data processing strategy and machine learning method were established to explore the reasons for the difference in quality and taste of different origins from the perspective of chemical composition. With the established strategy, 10 characteristic chemical markers were identified that could be used to distinguish the origins of I. denticulata. The strategy proposed in this study could provide a certain basis for quality control and reasonable consumption of I. denticulata and additional food and medicinal homologous species.

Untargeted Metabolomics Reveals the Protective Effect of a Traditional Chinese Herbal Decoction on Cisplatin-Induced Acute Kidney Injury

Our previous studies have demonstrated that Jian-Pi-Yi-Shen formula (JPYSF), a traditional Chinese herbal decoction, has a renoprotective effect in 5/6 nephrectomy-induced chronic kidney injury. However, the role and potential mechanisms of JPYSF in the treatment of acute kidney injury (AKI) remain unknown. This study was designed to test the beneficial effect of JPYSF in an AKI mouse model and to investigate the underlying mechanism by using metabolomics analysis. The AKI mouse model was induced by a single intraperitoneal injection of cisplatin at a dose of 20 mg/kg. The mice in the treatment group were pretreated orally with JPYSF (18.35 g/kg/d) for 5 days before cisplatin injection. Seventy-two hours after cisplatin injection, serum and kidney samples were collected for biochemical and histological examination. Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) was applied to analyze metabolic profiling variations in the kidney. The results showed that pretreatment with JPYSF obviously reduced the levels of serum creatinine and blood urea nitrogen and alleviated renal pathological injury in AKI mice. Orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot revealed a clear separation between the AKI and AKI + JPYSF group. A total of 68 and 87 significantly differentially expressed metabolites were identified in the kidney of AKI mice responding to JPYSF treatment in negative and positive ion mode, respectively. The pivotal pathways affected by JPYSF included vitamin B6 metabolism, alanine, aspartate and glutamate metabolism, lysine biosynthesis, and butanoate metabolism. In conclusion, JPYSF can protect the kidney from cisplatin-induced AKI, which may be associated with regulating renal metabolic disorders.

The RIGHT Extension Statement for Traditional Chinese Medicine: Development, Recommendations, and Explanation

Nowadays, the number of traditional Chinese medicine (TCM) guidelines is constantly increasing, but its reporting quality remains unsatisfactory. One of the main reasons is that there is a lack of suitable reporting standard to guide it. In response to this long-standing problem, the Reporting Items for practice Guidelines in HealThcare (RIGHT) Working Group has invited a group of TCM clinical experts, methodologists and epidemiology, and developed the RIGHT Extension Statement for TCM (RIGHT-TCM) through a multi-staged development process, including systematic review, reporting quality evaluation and online Delphi expert consensus. The RIGHT-TCM extends two sections of the RIGHT Statement, includes basic information and recommendations section. Seven strong recommendation sub-items were added to RIGHT Statement and formed the final RIGHT-TCM. The group hopes that the RIGHT-TCM may assist TCM guideline developers in reporting guidelines, support journal editors and peer reviewers when considering TCM guideline reports, and help health care practitioners understand and implement a TCM guideline. This article will introduce its background, development, recommendations and explanation.

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

Investigation of Invigorating Qi and Activating Blood Circulation Prescriptions in Treating Qi Deficiency and Blood Stasis Syndrome of Ischemic Stroke Patients: Study Protocol for a Randomized Controlled Trial

Ischemic stroke (IS) is characterized by high morbidity and high mortality. The integration of Traditional Chinese medicine (TCM) and western medicine has shown promising benefits in relieving symptoms, promoting neurological recovery, and improving the quality of life of patients with IS. In TCM, Qi-deficiency along with blood-stasis (QDBS) syndrome is one of the common types of IS that is treated by invigorating Qi and activating blood circulation. In TCM theory, improving the corresponding degree of prescription-syndrome correlation (PSC) is helpful to improve clinical efficacy. In this study, we intend to use similar prescriptions that invigorate Qi and activate blood circulation: Buyang Huanwu granules (BHG), Naoxintong capsules (NXTC), and Yangyin Tongnao granules (YTG). The goal is to evaluate their level of PSC inpatients with IS with QDBS syndrome and find relevant biomarkers to provide an objective basis for precise treatment of TCM and improve the clinical therapeutic effects. A multicenter, randomized, double-blinded, and placebo-controlled intervention trial will be conducted in IS patients with QDBS syndrome, followed by an add-on of Chinese patent medicine. A total of 160 subjects will be randomly assigned to the BHG, NXTC, YTG, and placebo groups in a 1:2:1:1 allocation ratio. All subjects will undergo 28 days of treatment and then followed for another 180 days. The primary outcome is the changes in the National Institutes of Health Stroke Scale score after 28 days of medication. The secondary outcomes include the modified Rankin scale score, activity of daily living scale score, and TCM symptom score. Data will be analyzed in accordance with a predefined statistical analysis plan. Ethical approval of this trial has been granted by the Research Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University (ID: 2017-Y-004-02). Written informed consent of patients will be required. This trial is registered in the Chinese Clinical Trial Registry (ChiCTR1800015189), and the results will be disseminated to the public through peer-reviewed journals and academic conferences.

Omics strategies decipher therapeutic discoveries of traditional Chinese medicine against different diseases at multiple layers molecular-level

Traditional Chinese medicine (TCM) has been broadly used for the personalized treatment of many diseases in China for thousands of years. In the past century, TCM was also introduced to other Asian countries and even the Western world. Increasing evidence has shown that TCM has the capacity to treat numerous complex diseases in the clinic, such as cardiovascular diseases (CVDs), infectious diseases, metabolic diseases, and neurodegenerative diseases. However, the earlier lack of analytical strategies to annotate the chemical complexity has severely impeded the modern study and translational application of TCM. This critical review aims to explore and exploit applications of systems biology-driven omics methods in TCM against a diversity of diseases, toward the specific use of TCM to treat patients with different diseases. Such effort shall enhance the applicability of systems biology-driven omics strategies in deciphering the mechanisms by which TCM treats different diseases and may lead to the discovery of new therapeutic directions. In addition, we proposed the possible strategies to innovate the applicable pattern of omics technologies in TCM niches, such as precision-modification metabolomics and chinmedomics methods, allowing to unveil the complexity of TCM, which must enable TCM to serve better for the population-health. Taken together, this review eventually shall highlight the core value of omics technologies in innovating TCM to combat the diseases in a new horizon.

Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

Application of metabolomics for unveiling the therapeutic role of traditional Chinese medicine in metabolic diseases

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional medicine has been practiced for thousands of years in China and some Asian countries. Traditional Chinese Medicine (TCM) is characterized as multi-component and multiple targets in disease therapy, and it is a great challenge for elucidating the mechanisms of TCM.

AIM OF THE REVIEW

Comprehensively summarize the application of metabolomics in biomarker discovery, stratification of TCM syndromes, and mechanism underlying TCM therapy on metabolic diseases.

METHODS

This review systemically searched the publications with key words such as metabolomics, traditional Chinese medicine, metabolic diseases, obesity, cardiovascular disease, diabetes mellitus in "Title OR Abstract" in major databases including PubMed, the Web of Science, Google Scholar, Science Direct, CNKI from 2010 to 2019.

RESULTS

A total of 135 papers was searched and included in this review. An overview of articles indicated that metabolic characteristics may be a hallmark of different syndromes/models of metabolic diseases, which provides a new perspective for disease diagnosis and therapeutic optimization. Moreover, TCM treatment has significantly altered the metabolic perturbations associated with metabolic diseases, which may be an important mechanism for the therapeutic effect of TCM.

CONCLUSIONS

Until now, many metabolites and differential biomarkers related to the pathogenesis of metabolic diseases and TCM therapy have been discovered through metabolomics research. Unfortunately, the biological role and mechanism of disease-related metabolites were largely unclarified so far, which warrants further investigation.

Metabolic Profiling Analysis of Patients With Coronary Heart Disease Undergoing Xuefu Zhuyu Decoction Treatment

Coronary heart disease (CHD) remains the leading cause of morbidity and mortality worldwide. Traditional Chinese medicine (TCM) is one of the effective complementary and alternative therapies used to improve the prognosis of CHD patients. Xuefu Zhuyu (XFZY) decoction, a classical traditional Chinese medication for regulating Qi and promoting blood circulation, has a clinical benefit in CHD; however, the underlying mechanism is not clear. Recently, it was found that the metabolites involved in amino acid metabolism and the tricarboxylic acid cycle were altered in CHD patients with Qi and Yin deficiency syndrome. To understand the material foundation of Qi, it is of great significance to study the differential metabolites involved in Qi during treatment of CHD with Qi-regulating and blood-promoting herbs. In this study, we investigated the metabolic profiles of serum in CHD patients by nontargeted metabolomics analysis to detect differential metabolites between the XFZY decoction group and placebo group. Ten CHD patients were enrolled and treated with placebo granules or XFZY decoction granules in a random and double-blind manner. Serum samples of all patients were evaluated by untargeted high-performance liquid chromatography with tandem mass spectrometry-based metabolomics. In total, 513 metabolites were detected in the serum of CHD patients, and six of these metabolites participating in seven metabolic pathways were significantly different between CHD patients treated with XFZY decoction and the placebo group. Among the six differential metabolites, FA (20:2)-H and tetracarboxylic acid (24:0), involved in fatty acid metabolism; cis-aconitic acid, which participates in the tricarboxylic acid cycle; 2-deoxy-D-glucose, involved in glucose metabolism; and N-acetylglycine, involved in amino acid metabolism, were decreased, whereas spermine, which participates in amino acid metabolism, was increased as compared with the placebo group. Our findings, combined with the perspective of biological functions, indicate that 2-deoxy-D-glucose and spermine might constitute the partial material foundation of Qi in CHD patients treated with XFZY decoction.