Traditional Chinese Medicine Syndrome Differentiation Research Articles

PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning.

Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world clinical scenarios. We emphasize the synergy among diagnosis and treatment procedure in real-world TCM clinical settings to propose the PresRecST model, which effectively combines the key components of symptom collection, syndrome differentiation, treatment method determination, and herb recommendation. This model integrates a self-curated TCM knowledge graph to learn the high-quality representations of TCM biomedical entities and performs 3 stages of clinical predictions to meet the principle of systematic sequential procedure of TCM decision making. To address the limitations of previous datasets, we constructed the TCM-Lung dataset, which is suitable for the simultaneous training of the syndrome differentiation, treatment method determination, and herb recommendation. Overall experimental results on 2 datasets demonstrate that the proposed PresRecST outperforms the state-of-the-art algorithm by significant improvements (eg, improvements of P@5 by 4.70%, P@10 by 5.37%, P@20 by 3.08% compared with the best baseline). The workflow of PresRecST effectively integrates the embedding vectors of the knowledge graph for progressive recommendation tasks, and it closely aligns with the actual diagnostic and treatment procedures followed by TCM doctors. A series of ablation experiments and case study show the availability and interpretability of PresRecST, indicating the proposed PresRecST can be beneficial for assisting the diagnosis and treatment in real-world TCM clinical settings. Our technology can be applied in a progressive recommendation scenario, providing recommendations for related items in a progressive manner, which can assist in providing more reliable diagnoses and herbal therapies for TCM clinical task.

Out-of-set association analysis of lung cancer drugs and symptoms based on clinical case data mining.

There are 1.8 million lung cancer deaths worldwide, accounting for 18% of global cancer deaths, including 710,000 in China, accounting for 23.8% of all cancer deaths in China. To explore the out-of-set association rules of lung cancer symptoms and drugs through text mining of traditional Chinese medicine (TCM) treatment of lung cancer, and form medical case analysis to analyze the experience of TCM syndrome differentiation in its treatment. The medical records of all patients diagnosed with lung cancer in Nanjing Chest Hospital from January to December 2018 were collected, and the out-of-set association analysis was performed using the MedCase v5.2 TCM clinical scientific research auxiliary platform based on the frequent pattern growth enhanced association analysis algorithm. In terms of TCM treatment of lung cancer, the clinical symptoms with high correlation included cough, expectoration, chest distress, and white phlegm; and the drugs with high correlation included Pinellia ternata, licorice root, white Atractylodes rhizome, and Radix Ophiopogonis; with the prescriptions based on Erchen and Maimendong decoctions. This analytical study of the medical cases of TCM treatment for lung cancer was performed using data mining techniques, and the out-of-set association rules between clinical symptoms and drugs were analyzed, including the understanding of lung cancer in TCM. Moreover, the essence of experience in drug use was gathered, providing significant scientific guidance for the clinical treatment of lung cancer.

TCMSSD: A comprehensive database focused on syndrome standardization

BackgroudQuantitative and standardized research on syndrome differentiation has always been at the forefront of modernizing Traditional Chinese Medicine (TCM) theory. However, the majority of existing databases primarily concentrate on the network pharmacology of herbal prescriptions, and there are limited databases specifically dedicated to TCM syndrome differentiation. PurposeIn response to this gap, we have developed the Traditional Chinese Medical Syndrome Standardization Database (TCMSSD, http://tcmssd.ratcm.cn). MethodsTCMSSD is a comprehensive database that gathers data from various sources, including TCM literature such as TCM Syndrome Studies (Zhong Yi Zheng Hou Xue) and TCM Internal Medicine (Zhong Yi Nei Ke Xue) and various public databases such as TCMID and ETCM. In our study, we employ a deep learning approach to construct the knowledge graph and utilize the BM25 algorithm for syndrome prediction. ResultsThe TCMSSD integrates the essence of TCM with the modern medical system, providing a comprehensive collection of information related to TCM. It includes 624 syndromes, 133,518 prescriptions, 8,073 diseases (including 1,843 TCM-specific diseases), 8,259 Chinese herbal medicines, 43,413 ingredients, 17,602 targets, and 8,182 drugs. By analyzing input data and comparing it with the patterns and characteristics recorded in the database, the syndrome prediction tool generates predictions based on established correlations and patterns. ConclusionThe TCMSSD fills the gap in existing databases by providing a comprehensive resource for quantitative and standardized research on TCM syndrome differentiation and laid the foundation for research on the biological basis of syndromes.

Construction and Application of a Traditional Chinese Medicine Syndrome Differentiation Model for Dysmenorrhea Based on Machine Learning.

Dysmenorrhea is one of the most common ailments affecting young and middle-aged women, significantly impacting their quality of life. Traditional Chinese Medicine (TCM) offers unique advantages in treating dysmenorrhea. However, an accurate diagnosis is essential to ensure correct treatment. This research integrates the age-old wisdom of TCM with modern Machine Learning (ML) techniques to enhance the precision and efficiency of dysmenorrhea syndrome differentiation, a pivotal process in TCM diagnostics and treatment planning. A total of 853 effective cases of dysmenorrhea were retrieved from the CNKI database, including patients' syndrome types, symptoms, and features, to establish the TCM information database of dysmenorrhea. Subsequently, 42 critical features were isolated from a potential set of 86 using a selection procedure augmented by Python's Scikit-Learn Library. Various machine learning models were employed, including Logistic Regression, Random Forest Classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN), each chosen for their potential to unearth complex patterns within the data. Based on accuracy, precision, recall, and F1-score metrics, SVM emerged as the most effective model, showcasing an impressive precision of 98.29% and an accuracy of 98.24%. This model's analytical prowess not only highlighted the critical features pivotal to the syndrome differentiation process but also stands to significantly aid clinicians in formulating personalized treatment strategies by pinpointing nuanced symptoms with high precision. The study paves the way for a synergistic approach in TCM diagnostics, merging ancient wisdom with computational acuity, potentially innovating the diagnosis and treatment mode of TCM. Despite the promising outcomes, further research is needed to validate these models in real-world settings and extend this approach to other diseases addressed by TCM.

Comprehensive effects of traditional Chinese medicine treatment on heart failure and changes in B-type natriuretic peptide levels: A meta-analysis.

Heart failure (HF), a common cardiovascular condition, is characterized by significant morbidity and mortality. While traditional Chinese medicine (TCM) is often used as a complementary approach in HF management, systematic evaluations of its impact on clinical outcomes, TCM syndrome scores, and B-type natriuretic peptide (BNP) levels are lacking. This study fills this gap through a comprehensive analysis of randomized controlled trials (RCTs) focusing on TCM for HF treatment. It encompasses an assessment of methodological quality, a meta-analysis, and an evaluation of evidence quality based on established standards. The results offer crucial insights into the potential advantages and constraints of TCM in HF management. To systematically analyze the effects of TCM on the clinical comprehensive outcomes, TCM syndrome scores, and BNP levels in patients with HF and evaluated the quality of evidence for these trials. RCTs on TCM for HF treatment published since the establishment of the database were searched in four Chinese and English databases, including China National Knowledge Infrastructure, Wanfang, VIP Information Chinese Science and Technology Journal, and PubMed. Methodological quality was assessed for the included studies with the Cochrane risk-of-bias assessment tool, and the meta-analysis and publication bias assessment was performed with the RevMan5.3 software. Finally, the quality of evidence was rated according to the GRADE criteria. A total of 1098 RCTs were initially retrieved. After screening, 16 RCTs were finally included in our study, which were published between 2020 and 2023. These RCTs involved 1660 HF patients, including 832 in the TCM group [TCM combined with conventional Western medicine (CMW) treatment] and 828 in the CWM group (CWM treatment). The course of treatments varied from 1 wk to 3 months. TCM syndrome differentiation was analyzed in 11 of the included RCTs. In all included RCTs, outcome indicators included comprehensive clinical outcomes, TCM syndrome scores, and BNP levels. The meta-analysis results showed significant differences between the TCM and CWM groups in terms of comprehensive clinical outcomes [risk ratio = -0.54; 95% confidence interval (CI) = -0.61, -0.47; P < 0.00001], TCM syndrome scores [weighted mean difference (WMD) = -142.07; 95%CI = -147.56, -136.57; P < 0.00001], and BNP levels (WMD = -142.07; 95%CI = -147.56, -136.57; P < 0.00001). According to the GRADE criteria, RCTs where "TCM improves clinical comprehensive outcomes" were rated as low-quality evidence, and RCTs where "TCM reduces TCM syndrome scores" or "TCM decreases BNP levels" were rated as medium-quality evidence. TCM combined with CWM treatment effectively improves comprehensive clinical outcomes and diminishes TCM syndrome scores and BNP levels in HF patients. Given the low and medium quality of the included RCTs, the application of these results should be cautious.

Clinical Effect of Modified Shengyang Yiwei Decoction in the Treatment of Diarrhea-Predominant Irritable Bowel Syndrome due to Spleen and Stomach Weakness

Objective: To explore the therapeutic effect of Shengyang Yiwei Decoction in patients with diarrhea-predominant irritable bowel syndrome (IBS) due to spleen and stomach weakness. Methods: 40 patients with diarrhea-predominant IBS who were treated from April 2018 to April 2020 were taken as samples. TCM (traditional Chinese medicine) syndrome differentiation found that they were all due to spleen and stomach weakness. They were randomly divided into two groups. Group A was treated with modified prescriptions of Shengyang Yiwei Decoction, while Group B was treated with Western medicine. The therapeutic effects in the two groups were compared. Results: The treatment efficacy in Group A was higher than that in Group B (P &lt; 0.05); the symptom scores of Group A such as loose stools, chills, physical weakness, poor appetite, and abdominal distension after meals were all lower than those in Group B (P&lt; 0.05); the SF-36 (36-Item Short Form Health Survey) scores of patients with diarrhea-predominant IBS in Group A were higher than those in Group B (P &lt; 0.05); the treatment satisfaction of Group A was higher than that of Group B (P&lt; 0.05). Conclusion: Treatment of diarrhea-predominant IBS patients with spleen and stomach weakness by Shengyang Yiwei Decoction can promote the disappearance of gastrointestinal discomfort symptoms, improve the quality of life, and enhance treatment efficacy. Hence, it is an efficient and feasible treatment for diarrhea-predominant IBS due to spleen and stomach weakness.

Differential clinical characteristics across traditional Chinese medicine (TCM) Syndromes in patients with sickle cell disease.

Pain is a common, debilitating, and poorly understood complication of sickle cell disease (SCD). The need for clinical pain management of SCD is largely unmet and relies on opioids as the main therapeutic option, which leads to a decreased quality of life (QoL). According to the literature, acupuncture has shown certain therapeutic effects for pain management in SCD. However, these clinical studies lack the guidance of Traditional Chinese Medicine (TCM) Syndrome Differentiation principles for treatment. To characterize differences in clinical presentation amongst TCM diagnosed Syndromes in SCD patients. Fifty-two patients with SCD and 28 age- and sex-matched healthy controls (HCs) were enrolled in an ongoing trial of acupuncture. Each participant completed a series of questionnaires on pain, physical function, fatigue, sleep, anxiety, depression and QoL and underwent cold- and pressure-based quantitative sensory testing at baseline. Data on prescription opioid use over the 12 months prior to study enrollment was used to calculate mean daily morphine milligram equivalents (MME). Differences among the three TCM Syndromes were analyzed by one-way ANOVA followed by Tukey post hoc testing. Two-sample t-tests were used to compare SCD and HC groups. TCM diagnosis criteria classified SCD patients into one of three TCM Syndromes: (a) Equal; (b) Deficiency; and (c) Stagnation. The Stagnation group exhibited higher pain interference, physical dysfunction, nociplastic pain, fatigue, anxiety, depression, MME consumption and lower sleep quality and QoL compared to the Equal group. Few differences were observed between HCs and the Equal SCD group across outcomes. Deficiency and Stagnation groups were differentiated with observed- and patient-reported clinical manifestations. These findings suggest that TCM diagnosed Syndromes in SCD can be differentially characterized using validated objective and patient-reported outcomes. Because characteristics of pain and co-morbidities in each SCD patient are unique, targeting specific TCM "Syndromes" may facilitate treatment effectiveness with a Syndrome-based personalized treatment plan that conforms to TCM principles. These findings lay the foundation for the development of tailored acupuncture interventions based on TCM Syndromes for managing pain in SCD. Larger samples are required to further refine and validate TCM diagnostic criteria for SCD.

Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning

BackgroundWith the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment. MethodsWe searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Networks (CNN) models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics. ResultsThe TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, Long Short-Term Memory (LSTM) of Recurrent Neural Network (RNN), and attention mechanism-based LSTM (LSTM-attention) models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes. ConclusionsThe TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM.

A serum metabolomics study of vascular cognitive impairment patients based on Traditional Chinese medicine syndrome differentiation.

Objective: Vascular cognitive impairment (VCI) accounts for approximately 50%-70% of all dementia cases and poses a significant burden on existing medical systems. Identifying an optimal strategy for preventing VCI and developing efficient symptomatic treatments remains a significant challenge. Syndrome differentiation represents a fundamental approach for personalized diagnosis and treatment in Traditional Chinese Medicine (TCM) and aligns with the principles of precision medicine. The objective of this study was to elucidate the metabolic characteristics of VCI based on TCM syndrome differentiation, thus providing novel insights into the diagnosis and treatment of VCI. Methods: A 2-year cross-sectional cognitive survey was conducted in four communities in Beijing between September 2020 and November 2022. The syndrome differentiation of participants was based on the Kidney-Yang Deficiency Syndrome Scale (KYDSS), which was originally developed by Delphi expert consultation. The identification of serum metabolites was performed by Ultra performance liquid chromatography (UPLC) analysis coupled with an electrospray ionization quadruple time-of-flight mass spectrometer (ESI-QTOF MS). Multivariate, univariate, and pathway analyses were used to investigate metabolic changes. Logistic regression models were also used to construct metabolite panels that were capable of discerning distinct groups. Phospholipase A2 (PLA2) levels were measured by a commercial ELISA kit. Results: A total of 2,337 residents completed the survey, and the prevalence of VCI was 9.84%. Of the patients with VCI, those with Kidney-Yang deficiency syndrome (VCIS) accounted for 70.87% of cases and exhibited more severe cognitive impairments. A total of 80 participants were included in metabolomics study, including 30 with VCIS, 20 without Kidney-Yang deficiency syndrome (VCINS), and 30 healthy control participants (C). Ultimately, 45 differential metabolites were identified when comparing the VCIS group with group C, 65 differential metabolites between the VCINS group and group C, and 27 differential metabolites between the VCIS group and the VCINS group. The downregulation of phosphatidylethanolamine (PE), and phosphatidylcholine (PC) along with the upregulation of lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), phosphatidic acid (PA) and phospholipase A2 (PLA2) can be considered as the general metabolic characteristics associated with VCI. Dysfunction of glycerophospholipids, particularly LPEs and PCs, was identified as a key metabolic characteristic of VCIS. In particular Glycerophospho-N-Arachidonoyl Ethanolamine (GP-NArE) was discovered for the first time in VCI patients and is considered to represent a potential biomarker for VCIS. The upregulation of PLA2 expression was implicated in the induction of alterations in glycerophospholipid metabolism in both VCIS and VCINS. Moreover, robust diagnostic models were established based on these metabolites, achieving high AUC values of 0.9322, 0.9550, and 0.9450, respectively. Conclusion: These findings contribute valuable information relating to the intricate relationship between metabolic disorders in VCI, neurodegeneration and vascular/neuroinflammation. Our findings also provide a TCM perspective for the precise diagnosis and treatment of VCI in the context of precision medicine.

TLDA: A transfer learning based dual-augmentation strategy for traditional Chinese Medicine syndrome differentiation in rare disease

The Traditional Chinese Medicine (TCM) has demonstrated its significant medical value over the decades, particularly during the COVID-19 pandemic. TCM-AI interdisciplinary models have been proposed to model TCM knowledge, diagnosis, and treatment experiments in clinical practice. Among them, numerous models have been developed to simulate the syndrome differentiation process of human TCM doctors for automatic syndrome diagnosis. However, these models are designed for normal scenarios and trained using a supervised learning paradigm which needs tens of thousands of training samples. They fail to effectively differentiate syndromes in rare disease scenarios where the available TCM electronic medical records (EMRs) are very limited for each unique syndrome. To address the challenge of rare diseases, this study proposes a simple yet effective method called Transfer Learning based Dual-Augmentation (TLDA). TLDA aims to augment the limited EMRs at both the sample-level and feature-level, enriching the pathological and medical information during training. Extended experiments involving 11 comparison models, including the state-of-the-art model, demonstrate the effectiveness of TLDA. TLDA outperforms all comparison models by a significant margin. Furthermore, TLDA can also be extended to other medical tasks when the EMRs for diagnosis are limited in samples.

Proteomics Analysis of Serum Reveals Potential Biomarkers for Heart Failure Patients with Phlegm-Blood Stasis Syndrome.

Heart failure (HF), a complex clinical syndrome, has become a global burden on health and economics around the world. Phlegm-blood stasis syndrome, one of the Traditional Chinese Medicine (TCM) syndrome differentiation, is the core pathogenesis dynamically throughout the occurrence, development, and prognosis of HF. Biomarkers having high sensitivity and specificity are highly demanded to facilitate the accurate differentiation of HF patients with phlegm-blood stasis syndrome. In the present study, serum samples were collected from 20 healthy controls and 40 HF patients (20 with and 20 without phlegm-blood stasis syndrome). We implemented data-independent acquisition mass spectrometry (DIA-MS) for discovery and parallel reaction monitoring (PRM) for validation of biomarkers for heart failure with phlegm-blood stasis syndrome. A total of 84 different proteins were found in the HF with phlegm-blood stasis syndrome (HF-TY) group compared with healthy controls. 37 candidate proteins were selected for the PRM assay, and five validated proteins with high sensitivity and specificity, including insulin-like growth factor-binding protein 4 (IGFBP4), β-2-microglobulin (B2M), dystroglycan (DAG1), immunoglobulin J chain (JCHAIN), and kallikrein B1 (KLKB1), were considered potential biomarkers for heart failure patients with phlegm-blood stasis syndrome. Newly identified biomarkers might provide insights into the diagnosis and treatment of HF with TCM syndrome differentiation.

Correlation between the Distribution of Traditional Chinese Medicine Syndromes and Molecular Types of Breast Cancer in Perichemotherapy Period

Abstract Objectives The objective of this study was to explore the correlation between the distribution of traditional Chinese medicine (TCM) syndromes and molecular types of breast cancer in the perichemotherapy period. Methods A total of 325 cases with perichemotherapy breast cancer was classified according to syndrome differentiation in TCM , and R × C table χ2 test was used to examine and analyze the relationship between TCM syndromes and molecular types of breast cancer in the perichemotherapy period. Results (1) In the early stage of chemotherapy, there was no significant difference in the distribution of different TCM syndromes among molecular types, mainly liver depression syndrome and liver depression and phlegm coagulation syndrome (p &gt; 0.05). (2) In the middle stage of chemotherapy, there were significant differences in the distribution of spleen deficiency and phlegm-dampness syndrome among HER-2 positive (HR positive), HER-2 positive (HR negative), and Luminal A type, Luminal B type (HER-2 negative), and triple-negative type (p &lt; 0.01). (3) After chemotherapy, there were significant differences in the distribution of spleen and kidney yang deficiency syndrome and marrow sea insufficiency syndrome among HER-2 positive (HR negative), triple-negative type, and HER-2 positive (HR positive), Luminal A type, Luminal B type (HER-2 negative), and triple-negative type (p &lt; 0.01). Conclusion (1) In the middle stage of chemotherapy, HER-2 positive (HR positive) and HER-2 positive (HR negative) are more likely to show spleen deficiency and phlegm-dampness syndrome than other molecular types. (2) In the late stage of chemotherapy, the HER-2 positive (HR negative) and triple-negative type is more likely to show spleen-kidney yang deficiency syndrome than other molecular types, and the triple-negative type is more likely to show marrow sea insufficiency syndrome than other molecular types.

[Identification and validation of novel biomarkers for cold-dampness syndrome of rheumatoid arthritis based on integration of multiple bioinformatics methods].

This study aims to identify the novel biomarkers of cold-dampness syndrome(RA-Cold) of rheumatoid arthritis(RA) by gene set enrichment analysis(GSEA), weighted gene correlation network analysis(WGCNA), and clinical validation. Firstly, transcriptome sequencing was carried out for the whole blood samples from RA-Cold patients, RA patients with other traditional Chinese medicine(TCM) syndromes, and healthy volunteers. The differentially expressed gene(DEG) sets of RA-Cold were screened by comparison with the RA patients with other TCM syndromes and healthy volunteers. Then, GSEA and WGCNA were carried out to screen the key DEGs as candidate biomarkers for RA-Cold. Experimentally, the expression levels of the candidate biomarkers were determined by RT-qPCR for an independent clinical cohort(not less than 10 cases/group), and the clinical efficacy of the candidates was assessed using the receiver operating characteristic(ROC) curve. The results showed that 3 601 DEGs associated with RA-Cold were obtained, including 106 up-regulated genes and 3 495 down-regulated genes. The DEGs of RA-Cold were mainly enriched in the pathways associated with inflammation-immunity regulation, hormone regulation, substance and energy metabolism, cell function regulation, and synovial pannus formation. GSEA and WGCNA showed that recombinant proteasome 26S subunit, ATPase 2(PSMC2), which ranked in the top 50% in terms of coefficient of variation, representativeness of pathway, and biological modules, was a candidate biomarker of RA-Cold. Furthermore, the validation results based on the clinical independent sample set showed that the F1 value, specificity, accuracy, and precision of PSMC2 for RA-Cold were 70.3%, 61.9%, 64.5%, and 81.3%, respectively, and the area under the curve(AUC) value was 0.96. In summary, this study employed the &quot;GSEA-WGCNA-validation&quot; integrated strategy to identify novel biomarkers of RA-Cold, which helped to improve the TCM clinical diagnosis and treatment of core syndromes in RA and provided an experimental basis for TCM syndrome differentiation.

[Pharmacological research progress of five classical prescriptions in treatment of chronic heart failure].

Chronic heart failure(CHF) is a comprehensive clinical syndrome caused by multiple factors that result in structural and/or functional abnormalities of the heart, leading to impaired ventricular contraction and/or relaxation functions. This medical condition represents the final stage of various cardiovascular diseases. In the treatment of CHF, multiple clinical studies have demonstrated the benefits of using traditional Chinese medicine(TCM) to control oxidative stress, inflammation, and apoptosis, thereby delaying ventricular remodeling and reducing myocardial fibrosis. In this study, common TCM syndromes in the diagnosis and treatment of CHF in recent years were reviewed and summarized. Five common treatment methods including benefiting Qi and activating blood circulation, enhancing Qi and nourishing Yin, warming Yang for diuresis, eliminating phlegm and dampness, rescuing from collapse by restoring Yang, and corresponding classic prescriptions in prevention and treatment of CHF were concluded under the guidance of TCM syndrome differentiation thinking. Meanwhile, research progress on the modern pharmacological effects of these classic prescriptions was systematically discussed, so as to establish a unique treatment system for CHF by classic prescriptions under the guidance of TCM syndrome differentiation theory and provide innovative diagnosis and treatment strategies for clinical CHF.

A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer

ObjectiveTo explore the potential imaging biomarkers for predicting Traditional Chinese medicine (TCM) deficiency and excess syndrome in prostate cancer (PCa) patients by radiomics approach based on MR imaging. MethodsA total of 121 PCa patients from 2 centers were divided into 1 training cohort with 84 PCa patients and 1 validation cohort with 37 PCa patients. The PCa patients were divided into deficiency and excess syndrome group according to TCM syndrome differentiation. Radiomic features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging and apparent diffusion coefficient images originated from diffusion-weighted imaging. A radiomic signature was constructed after reduction of dimension in training group by the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. ResultsThe radiomic scores of PCa with TCM excess syndrome group were statistically higher than those of PCa with TCM deficiency syndrome group among T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models. The area under ROC curves for T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models were 0.824, 0.824, 0.847 in the training cohort and 0.759, 0.750, 0.809 in the validation cohort, respectively. The apparent diffusion coefficient imaging model had the best discrimination in separating patients with TCM excess syndrome and deficiency syndrome, and its accuracy was 0.788, 0.778 in the training and validation cohort, respectively. The calibration curve demonstrated that there was a high consistency between the prediction of radiomic scores and the actual classification of TCM's deficiency and excess syndrome in PCa. ConclusionThe radiomic signature based on MR imaging can be performed as a non-invasive, potential approach to discriminate TCM deficiency syndrome from excess syndrome in PCa, in which apparent diffusion coefficient imaging model has the best diagnostic efficiency.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment.

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

The Relationship between Golgi Protein 73, Alpha-Fetoprotein, Liver Function Indicators, and Traditional Chinese Medicine Syndrome Types of Primary Liver Cancer

Abstract Objective Our objective was to analyze the correlation between Golgi protein 73 (GP73), alpha-fetoprotein (AFP), liver function indicators, and traditional Chinese medicine (TCM) syndrome types of primary liver cancer (hereinafter referred to as “liver cancer”). Methods In total, 156 liver cancer patients (liver cancer group) and 52 healthy individuals (health group) were selected as the research subjects to detect their GP73, AFP expression, and liver function-related indicators. The obtained data were statistically analyzed using SPSS 21.0 software. Results (1) The positive expression rate of GP73 in the liver cancer group was 50%; the positive expression rates of qi stagnation and blood stasis syndrome, heat toxin in liver and gallbladder syndrome, and yin deficiency of liver and kidney syndrome were 37.9, 54.3, and 59.6%, respectively. There was no statistically significant difference between the groups (p &gt; 0.05). The positive expression rate of AFP was 50%. The positive expression rates of qi stagnation and blood stasis syndrome, heat-toxin in liver and gallbladder syndrome, and yin deficiency of liver and kidney syndrome were 41.7, 54.3, and 59.6%, respectively. There was a statistically significant difference between the groups (p &lt; 0.05). (2) The GP73 levels of patients with different syndrome types in the liver cancer group were ranked from high to low as yin deficiency of liver and kidney syndrome, heat toxin in liver and gallbladder syndrome, and qi stagnation and blood stasis syndrome. The differences between the groups were statistically significant (p &lt; 0.05). The AFP levels of patients with different syndrome types in the liver cancer group were ranked from high to low as heat toxin in liver and gallbladder syndrome, yin deficiency of liver and kidney syndrome, and qi stagnation and blood stasis syndrome. There was no statistically significant difference between the groups (p &gt; 0.05). (3) Analysis of liver function indicators in the liver cancer group: the alanine transaminase (ALT) levels of patients with different syndrome types were in descending order from high to low, including yin deficiency of liver and kidney syndrome, heat toxin in liver and gallbladder syndrome, and qi stagnation and blood stasis syndrome. The differences between groups were statistically significant (p &lt; 0.05). The aspartate aminotransferase (AST) levels of patients with different syndrome types were ranked from high to low as follows: heat toxin in liver and gallbladder syndrome, yin deficiency of liver and kidney syndrome, and qi stagnation and blood stasis syndrome. The difference between groups was statistically significant (p &lt; 0.05). The levels of albumin (ALB) in patients with different syndrome types were ranked from high to low, including heat toxin in liver and gallbladder syndrome, qi stagnation and blood stasis syndrome, and yin deficiency of liver and kidney syndrome. The differences between groups were statistically significant (p &lt; 0.05). Conclusion GP73 and indicators such as ALT, AST, and ALB are of great significance in the diagnosis of TCM syndrome differentiation and classification of liver cancer patients.

Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation

Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the experience of doctors. The gratifying progress of machine learning research in recent years has brought new ideas for TCM syndrome differentiation. In this paper, we propose a deep network model for TCM syndrome differentiation, which improves network performance by injecting TCM syndrome differentiation knowledge in the form of first-order logic into the deep network. Experimental results show that the accuracy of our proposed model reaches 89%, which is significantly better than the deep learning model MLP and other traditional machine learning models. In addition, we present the collected and formatted TCM syndrome differentiation (TSD) dataset, which contains more than 40,000 TCM clinical records. Moreover, 45 symptoms (“▪”), 322 patterns(“▪”), and more than 500 symptoms are labeled in TSD respectively. To the best of our knowledge, this is the first TCM syndrome differentiation dataset labeling diseases, syndromes and pattern. Such detailed labeling is helpful to explore the relationship between various elements of syndrome differentiation.

Study on the difference and regularity of tongue images in 309 patients with different pathological stages of non-small cell lung cancer.

Tongue diagnosis is a crucial traditional Chinese medicine (TCM) inspection method for TCM syndrome differentiation and treatment. The primary research focus was on tongue image characteristic parameters of patients with non-small cell lung cancer (NSCLC). Analysis of the tongue image parameters of various pathological stages of NSCLC provides technical support for establishing an integrated Chinese and Western auxiliary diagnosis and efficacy evaluation medicine system for lung cancer that integrates tongue image features. Tongue image characteristics of 309 patients with NSCLC and 206 controls were collected and analyzed clinically. The T-test or rank sum test and logistic regression analysis were applied to analyze the characteristics of tongue image indicators of different pathological stages of NSCLC. There were differences in tongue image characteristics in the NSCLC group compared to the control group. The tongue quality and brightness of the tongue coating in the NSCLC group increased, the red component was reduced, the tongue coating thickened, and the yellow component increased compared to the healthy control group. A comparison of tongue image indexes of NSCLC in different pathological stages showed that stage IV had lower TB-b and higher TB-a than stage I. In addition, stage IV had lower TB-b than stage II + III, showing an increase in the blue and red components of the tongue in stage IV and the appearance of cyanotic tongue features. The tongue image characteristics of NSCLC patients differed from those of the control group. Tongue imaging indicators can reflect the characteristics of tongue images of patients with NSCLC. The tongue image characteristics of patients with stage IV lung cancer are bluish and purple compared with those with stage I, II, and III. It is suggested that the tongue's image characteristics can be used as a reference for the pathological classification of NSCLC and judgment of the disease process.

TCM syndrome differentiation in colorectal cancer patients assisted by differences in gut microbiota: An exploratory study

ObjectiveTo explore the difference in gut microbiota between different traditional Chinese Medicine (TCM) syndromes in patients with colorectal cancer (CRC) and its internal relationship. MethodsFrom June 2020 to August 2021, 109 colorectal cancer patients with a clear pathological diagnosis who had not yet undergone surgery or chemotherapy were classified according to the TCM syndrome classification, and the feces samples of 109 patients with preoperative colorectal cancer were collected. 16s rRNA gene sequencing was used to determine gut microbiota diversity and abundance in CRC patients with different TCM syndrome, and LEfSe analysis was made to screen different TCM syndrome for differential representative microbiota. Results109 patients were divided into 5 syndromes by TCM syndrome classification, which were Liver and Kidney Yin Deficiency Syndrome (LKYDS, n = 19), Spleen Deficient Qi Stagnation Syndrome (SDQSS, n = 30), Stasis and Poison Obstruction Syndrome (SPOS, n = 17), Damp-Heat Syndrome (DHS, n = 30), Qi and Blood Deficiency Syndrome (QBDS, n = 13). Alpha diversity index showed significant differences among the five groups of TCM syndromes, with Shannon index being highest in the SDQSS group and lowest in the LKYDS (p = 0.003). ACE index being highest in the SDQSS group and lowest in the SPOS (p = 0.010). PD whole tree index being highest in the SDQSS group and lowest in the SPOS (p = 0.017). Similarly, beta diversity showed significant differences among the five groups of TCM syndromes, with principal coordinate analysis (PCo1 = 31.86 %, PCo2 = 5.62 %) showing separation and coincidence between the groups, and Adonis group differences showing coincidence between the QBDS-LKYDS (p = 0.702), QBDS-DHS (p = 0.133), and SDQSS-DHS (p = 0.260) groups. LEfSe analysis revealed that the representative microbiota of DHS patients was Dialister sp Marseille P5638 (LDA = 3.05, p＜0.001), the representative microbiota of SPOS patients was Oscillospirales (LDA = 4.78, p = 0.029), the representative microbiota of SDQSS patients was Selenomonadaceae (LDA = 3.94, p = 0.003), the representative microbiota of LKYDS patients was Dialister (LDA = 4.19, p = 0.001), and the representative microbiota of QBDS patients was Akkermansia muciniphila (LDA = 4.23, p = 0.006). ConclusionsThere are significant differences in gut microbiota between different TCM syndromes in CRC patients. The five microbiota, Dialister sp Marseille P5638, Oscillospirales, Selenomonadaceae, Dialister, and Akkermansia muciniphila, may be differential markers of TCM syndrome in CRC and are expected to be one of the bases for accurate TCM syndrome differentiation of CRC.