scholarly journals Clinical Applications of Omics Technologies on ZHENG Differentiation Research in Traditional Chinese Medicine

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ya-Nan Song ◽  
Gui-Biao Zhang ◽  
Yong-Yu Zhang ◽  
Shi-Bing Su
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Applications ◽  
Diagnostic Methods ◽  
Biological Mechanisms ◽  
Omics Technologies ◽  
Functional Activities ◽  
Wide Perspective ◽  
Tcm Theory ◽  
Zheng Differentiation

Traditional Chinese medicine (TCM) ZHENG is the basic concept of TCM theory. The effectiveness of TCM treatment depends on the accuracy of ZHENG differentiation. ZHENG differentiation, using the “four diagnostic methods,” has the drawbacks of subjectivity and variability. Following development of omics technologies, which study the functional activities of human body from a system-wide perspective, it has been more and more applied in study of objectivity differentiating TCM ZHENG and understanding its biological mechanisms. This paper reviewed the literatures of clinical TCM ZHENG differentiation researches, underlying omics technologies, and indicated the increased trends of related articles with four kinds of omics technologies, including genomics, transcriptomics, proteomics and metabolomics, and the correlations between ZHENG differentiation and findings in omics studies. Moreover, the paper summarized the typical omics application in common studied diseases and TCM ZHENGs and discussed the main problems and countermeasure of ZHENG differentiation researches. The work here may provide a reference for further research of TCM ZHENG differentiation using omics technologies.

scholarly journals Utilization Pattern of Traditional Chinese Medicine among Fracture Patients: A Taiwan Hospital-Based Cross-Sectional Study

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Chu-Yao Tseng ◽  
Ching-Wen Huang ◽  
Hsin-Chia Huang ◽  
Wei-Chen Tseng
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Early Period ◽  
Clinic Visit ◽  
Research Database ◽  
Late Period ◽  
Utilization Pattern ◽  
Cross Sectional ◽  
Herbal Products ◽  
Tcm Theory

Traditional Chinese medicine (TCM) divides fracture treatment into three stages. Many TCM herbs and formulas have been used to treat fractures for thousands of years. However, research regarding the Chinese herbal products (CHPs) that should be used at different periods of treatment is still lacking. This study aims to identify the CHPs that should be used at different periods of treatment as well as confirm the TCM theory of fracture periods medicine. We used prescriptions of TCM outpatients with fracture diagnoses analyzed using the Chang Gung Research Database (CGRD) from 2000 to 2015. According to the number of days between the date of the fracture and the clinic visit date, all patients were assigned to one of three groups. Patients with a date gap of 0-13 days were assigned to the early period group; those with a date gap of 14-82 days were assigned to the middle period group; and those with a date gap of 83-182 days were assigned to the late period group. We observed the average number of herbal formulas prescribed by the TCM doctor at each visit was 2.78, and the average number of single herbs prescribed was 6.47. The top three prescriptions in the early fracture period were Zheng-gu-zi-jin-dang, Shu-jing-huo-xue-tang, and Wu-ling-san. In the middle fracture period, the top three formulas were Zheng-gu-zi-jin-dang, Shu-jing-huo-xue-tang, and Zhi-bai-di-huang-wan. In the late fracture period, the top three formulas were Shu-jing-huo-xue-tang, Gui-lu-er-xian-jiao, and Du-huo-ji-sheng-tang. The main single herbs used in the early fracture period were Yan-hu-suo, Gu-sui-bu, and Dan-shen. From the middle to the late period, the most prescribed single herbs were Xu-duan, Gu-sui-bu, and Yan-hu-suo. We concluded that the results showed that the CGRD utilization pattern roughly meets the TCM theory at different fracture periods.

scholarly journals Evidence of TCM Theory in Treating the Same Disease with Different Methods: Treatment of Pneumonia with Ephedra sinica and Scutellariae Radix as an Example

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Liping Sun ◽  
Dandan Wang ◽  
Yan Xu ◽  
Wenxiu Qi ◽  
Yanbo Wang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Future Directions ◽  
Network Construction ◽  
Ephedra Sinica ◽  
Scutellariae Radix ◽  
Tcm Theory ◽  
Global Health Problem

Pneumonia is a serious global health problem and the leading cause of mortality in children. Antibiotics are the main treatment for bacterial pneumonia, but there are serious drug resistance problems. Traditional Chinese medicine (TCM) has been used to treat diseases for thousands of years and has a unique theory. This article takes the treatment of pneumonia with Ephedra sinica as a representative hot medicine and Scutellariae Radix as a representative cold medicine as an example. We explore and explain the theory of treating the same disease with different TCM treatments. Using transcriptomics and network pharmacology methods, GO, KEGG enrichment, and PPI network construction were carried out, demonstrating that Ephedra sinica plays a therapeutic role through the NF-κB and apoptosis signaling pathways targeting PLAU, CD40LG, BLC2L1, CASP7, and CXCL8. The targets of Scutellariae Radix through the IL-17 signaling pathway are MMP9, CXCL8, and MAPK14. Molecular docking technology was also used to verify the results. In short, our results provide evidence for the theory of treating the same disease with different treatments, and we also discuss future directions for traditional Chinese medicine.

scholarly journals Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Changbo Zhao ◽  
Guo-Zheng Li ◽  
Chengjun Wang ◽  
Jinling Niu
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Machine Learning Techniques ◽  
Syndrome Differentiation ◽  
Reference List ◽  
Patient Classification ◽  
Research Issues

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

scholarly journals Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Ting Shu ◽  
Bob Zhang ◽  
Yuan Yan Tang
Keyword(s):  
Heart Disease ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Diagnostic Methods ◽  
Collaborative Representation ◽  
Disease Detection ◽  
Color Analysis ◽  
Key Block ◽  
Facial Images ◽  
Tomography Scan

At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

scholarly journals Application of Genetic Algorithm for Discovery of Core Effective Formulae in TCM Clinical Data

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Ming Yang ◽  
Josiah Poon ◽  
Shaomo Wang ◽  
Lijing Jiao ◽  
Simon Poon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Genetic Algorithm ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Data ◽  
Cancer Dataset ◽  
Synergy Index ◽  
Average Confidence ◽  
Group A ◽  
Tcm Theory

Research on core and effective formulae (CEF) does not only summarize traditional Chinese medicine (TCM) treatment experience, it also helps to reveal the underlying knowledge in the formulation of a TCM prescription. In this paper, CEF discovery from tumor clinical data is discussed. The concepts of confidence, support, and effectiveness of the CEF are defined. Genetic algorithm (GA) is applied to find the CEF from a lung cancer dataset with 595 records from 161 patients. The results had 9 CEF with positive fitness values with 15 distinct herbs. The CEF have all had relative high average confidence and support. A herb-herb network was constructed and it shows that all the herbs in CEF are core herbs. The dataset was divided into CEF group and non-CEF group. The effective proportions of former group are significantly greater than those of latter group. A Synergy index (SI) was defined to evaluate the interaction between two herbs. There were 4 pairs of herbs with high SI values to indicate the synergy between the herbs. All the results agreed with the TCM theory, which demonstrates the feasibility of our approach.

scholarly journals Dried seahorse in traditional medicine: A narrative review

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Siukan Law
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Biology ◽  
Ribosomal Protein ◽  
Traditional Medicine ◽  
Aging Process ◽  
Protein Gene ◽  
Ribosomal Protein Gene ◽  
Traditional Medicines ◽  
Tcm Theory

Seahorses are classified as members of Syngnathidae family, which includes pipefishes, pipehorses, and seadragons. China, including Hong Kong, uses 250 tons of seahorses as Traditional Chinese Medicine (TCM) every year. It is popular in Traditional Medicines (TM) especially TMC and its derivatives. The TCM formulations of dried seahorse strengthens the kidney and enhances immunity to treat the aging process. Base on the molecular biology analysis, S6 ribosomal protein gene, S7 ribosomal protein gene, and the S20 ribosomal protein gene have been identified in dried seahorses, which help to reduce the cough symptoms. The present mini-review discusses the background of the use of dried seahorses, the TCM theory, the TCM formulations, the molecular biology, and analysis of its usage in traditional medicine.

scholarly journals A Cross-Sectional Study of Traditional Chinese Medicine Practitioner’s Knowledge, Treatment Strategies and Integration of Practice of Chronic Pelvic Pain in Women

2021 ◽  
Author(s):  
Susan Arentz ◽  
Caroline Smith ◽  
Rebecca Redmond ◽  
Jason Abbott ◽  
Mike Armour
Keyword(s):  
Health Care ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Health Care Providers ◽  
Pelvic Pain ◽  
Chronic Pelvic Pain ◽  
Clinical Decision Making ◽  
Diagnostic Methods ◽  
Care Providers ◽  
Cross Sectional

Abstract Background Chronic pelvic pain (CPP) in women is persistent, intermittent cyclical and non-cyclical lower abdominal pain, lasting for more than 6 months. Traditional Chinese Medicine (TCM) is a popular treatment option for women’s health conditions, but little is known about how treatment for CPP is delivered by TCM practitioners. The aim of this survey was to explore practitioners understanding and treatment of women with CPP, and how they integrate their management and care into the health care system. Method An online cross-sectional survey of registered TCM practitioners in Australia and New Zealand. Survey domains included treatment characteristics (e.g. frequency), evaluation of treatment efficacy, referral networks, and sources of information that informed clinical decision making. Results One hundred and twenty-two registered TCM practitioners responded to this survey, 91.7% reported regular treatment of women with CPP. Treatment decisions were most-often guided by a combination of biomedical and TCM diagnosis (77.6%), and once per week was the most common treatment frequency (66.7%) for acupuncture. Meditation (63.7%) and dietary changes (57.8%) were other commonly used approaches to management. The effectiveness of treatment was assessed using multiple approaches, most commonly pain scales, (such as the numeric rating scale) and any change in use of analgesic medications. Limitations to TCM treatment were reported by over three quarters (83.7%) of practitioners, most commonly due to cost (56.5%) and inconvenience (40.2%) rather than safety or lack of efficacy. Integration within the wider healthcare system was common with over two thirds (67.9%) receiving referrals from health care providers. Conclusion TCM practitioners seeing women with various CPP symptoms, commonly incorporate both traditional and modern diagnostic methods to inform their treatment plan, monitor treatment progress using commonly accepted approaches and measures and often as a part of multidisciplinary healthcare for women with CPP.

scholarly journals Xin Su Ning—A Review of Basic and Clinical Pharmacology Integrated With Traditional Chinese Medicine Antiarrhythmic Theory

2021 ◽  
Vol 12 ◽  
Author(s):  
Xuan Wang ◽  
Taiyi Wang ◽  
Shuwen Ding ◽  
Yu-Ling Ma
Keyword(s):  
Clinical Trial ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Pharmacological Study ◽  
Viral Myocarditis ◽  
Herbal Medicines ◽  
Premature Ventricular Contraction ◽  
Weight Coefficient ◽  
Double Blind ◽  
Tcm Theory

Xin Su Ning (XSN) is a patented multicomponent medicine, which was certified in 2005 by the China State Food and Drug Administration to be produced pharmaceutically and to be used clinically. The XSN capsule was developed from an effective formula composed by Prof. Shuwen Ding of Shandong University of Traditional Chinese Medicine. Through more than 30 years of clinical observation, Prof. Ding concluded that XSN has a significant effect on arrhythmia with phlegm-heat heart-disturbed syndrome according to the traditional Chinese medicine (TCM) diagnosis. XSN, derived from a classical TCM formula Huanglian Wen Dan Decoction, is formulated with 11 Chinese herbal medicines to treat cardiac ventricular arrhythmia. Clinical evidence suggests that it is particularly efficacious for the arrhythmias induced by cardiac ischemia and viral myocarditis without obvious adverse reactions being reported. Cellular electrophysiological studies in ventricular myocytes revealed that XSN prolongs the duration and suppresses the amplitude of the action potential (AP), which is supported by the blockage of sodium and potassium channels indicating the characteristics of class I and III antiarrhythmic drugs. A recently reported double-blind, placebo-controlled, multicenter clinical trial of XSN enrolled 861 patients (ChiCTR-TRC-14004180) and showed that XSN significantly inhibited premature ventricular contraction (PVC). The cellular electrophysiological discoveries provided the mechanistic evidence for the clinical efficacy on inhibition of PVC by XSN as demonstrated in the clinical trial. These studies, for the first time, provided exclusive evidence that multicomponent TCM antiarrhythmic medicine can be evaluated using conventional research methods that have been used for antiarrhythmic drug discoveries for decades. We aimed to give a comprehensive review on XSN including its origin with the support of TCM theory, its pre-licensing clinical use and development, and its pharmacological and clinical study discoveries. The review will be summarized with the discoveries reported in a novel network pharmacological study that introduced a weight coefficient, which made it possible to evaluate the pharmacological properties of the TCM formula with regard to its formation based on TCM theory. Limitations regarding XSN’s basic and clinical research and possible future studies are listed. We hope that the advances in how XSN was studied may offer useful guidance on how other TCM could be studied with respect to the integrity of the TCM formulas.

The Impact of Artificial Intelligence on Traditional Chinese Medicine

2021 ◽  
pp. 1-18
Author(s):  
Yulin Wang ◽  
Xiuming Shi ◽  
Li Li ◽  
Thomas Efferth ◽  
Dong Shang
Keyword(s):  
Artificial Intelligence ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Modern Science ◽  
Diagnostic Methods ◽  
Digital Data ◽  
Syndrome Differentiation ◽  
Efficacy Evaluation ◽  
Combination Rules ◽  
The Impact

Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diagnostics, thus increasing the use of effective therapeutic methods for patients. This systematic review provides an updated overview on the major breakthroughs in the field of AI-assisted TCM four diagnostic methods, syndrome differentiation, and treatment. AI-assisted TCM diagnosis is mainly based on digital data collected by modern electronic instruments, which makes TCM diagnosis more quantitative, objective, and standardized. As a result, the diagnosis decisions made by different TCM doctors exhibit more consistency, accuracy, and reliability. Meanwhile, the therapeutic efficacy of TCM can be evaluated objectively. Therefore, AI is promoting TCM from experience to evidence-based medicine, a genuine scientific revolution. Furthermore, huge and non-uniform knowledge on formula-syndrome relationships and the combination rules of herbal TCM formulae could be better standardized with the help of AI analysis, which is necessary for the clinical efficacy evaluation and further optimization on the standardized TCM formulae. AI bridges the gap between TCM and modern science and technology. AI may bring clinical TCM diagnostics closer to western medicine. With the help of AI, more scientific evidence about TCM will be discovered. It can be expected that more unified guidelines for specific TCM syndromes will be issued with the development of AI-assisted TCM therapies in the future.

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