scholarly journals Evaluating the Traditional Chinese Medicine (TCM) Officially Recommended in China for COVID-19 Using Ontology-Based Side-Effect Prediction Framework (OSPF) and Deep Learning

2020 ◽  
Author(s):  
Zeheng Wang ◽  
Liang Li ◽  
Jing Yan ◽  
Yuanzhe Yao
Keyword(s):  
Deep Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Side Effect ◽  
Ann Model ◽  
The Public ◽  
Supplementary Treatment ◽  
Artificial Neural Network Ann ◽  
Novel Coronavirus ◽  
Effective Drugs

Ethnopharmacological relevance: Novel coronavirus disease (COVID-19) outbroke in Wuhan has imposed a huge influence onto the society in term of the public heath and economy. However, so far, no effective drugs or vaccines have been developed. Whereas, the Traditional Chinese Medicine (TCM) has been considered as a promising supplementary treatment for the disease owing to its clinically proven performance on many diseases even like severe acute respiratory syndrome (SARS). Meanwhile, many side-effect (SE) reports suggest the SE of the TCM prescriptions cannot be ignored in curing the COVID-19, especially because COVID-19 always simultaneously leads to dramatic degradation of the patients’ physical condition. How to evaluate the TCM regarding to their latent SE is a urgent challenge. Aim of the study: In this study, we use an ontology-based side-effect prediction framework (OSPF) developed in our previous work and Artificial Neural Network (ANN)-based deep learning to evaluate the TCM prescriptions that are officially recommended in China for novel coronavirus (COVID-19). Materials and methods: Firstly, we adopted the OSPF developed in our previous work, where an ontology-based model separate all the ingredients in a TCM prescription into two categories: hot and cold. Then, we established a database by converting each TCM prescription into a vector containing the ingredient dosage and the according hot/cold attribution as well as the safe/unsafe label. And, we trained the ANN model using this database, after which a safety indicator (SI), as the complementary percentage of side-effect (SE) possibility, is then given for each TCM prescription. According to the proposed SI from high to low, we re-organize the recommended prescription list. Secondly, by using this method, we also evaluate the safety indicators of some other famous TCM prescriptions that are not in the recommended list but are used traditionally to cure flu-like diseases for extending the potential treatments. Results: Based on the SI generated in the ANN model, FTS, PMSP, and SF are the safest ones in recommended list, which all own a more-than-0.8 SI. Whereas, JHQG, LHQW, SFJD, XBJ, and SHL are the prescriptions that are most likely unsafe, where the indicators are all below 0.2. In the extra list, the indicators of XC, XQRS, CC, and CHBX are all above 0.8, and at the meantime, XZXS, SJ, QW, and KBD’s indicators are all below 0.2. Conclusions: In total, there are seven TCM prescriptions which own the indicators more than 0.8, suggesting these prescriptions should be considered firstly in curing COVID-19, if suitable. We believe this work will provide a reasonable suggestion for the society to choose proper TCM as the supplementary treatment for COVID-19. Besides, this work also introduces a pilot and enlightening method for creating a more reasonable recommendation list of TCM to other diseases.

scholarly journals Correlations Between Traditional Chinese Medicine Syndromes and Clinical Indexes in Health Check-Up Population

2020 ◽  
Author(s):  
Pei-Hung Liao ◽  
Chia-Ling Tu ◽  
Chi-Feng Liu
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Laboratory ◽  
Cost Effective ◽  
Health Check ◽  
Syndrome Differentiation ◽  
Health Examination ◽  
Ann Model ◽  
Artificial Neural Network Ann ◽  
Tcm Syndrome

Abstract BackgroundRoutine health check-up is associated with improved lifespan and reduced medical cost. Traditional Chinese medicine (TCM) serves as a cost-effective modality in healthcare system. We examined the correlations of TCM syndromes with modern medical indicators in health check-up population.MethodsWe studied 5231 subjects undergoing health check-up between January 1st 2008 and December 31, 2016. Physical indexes such as body weight and blood pressure and biomedical indicators like live function and tumour markers were measured. All subjects underwent colonoscopy. All subjects were classified and differentiated into five different TCM syndromes. An artificial neural network (ANN) was employed to evaluate the predictive value of TCM syndrome differentiation.ResultsOf enrolled subjects, SADH accounted for 85.8% and IDSIBSB was found in 4576 subject (87.5%). YaDSK and YiDLK accounted for 99.5% (5207) and 80.9% (4232) respectively. We found that YiDLK is correlated with abnormality of liver function indexes. The results showed that SADH is correlated with level of cholesterol in health check-up population. The results showed that the predictive ANN model showed a good fitting with an accuracy of 100%.ConclusionThe results demonstrated that TCM syndromes were closely correlated with clinical laboratory indexes regardless of health status. TCM syndrome differentiation is suggested to contribute to routine health examination as screening measure with its non-invasive nature.

scholarly journals Therapeutic Effects of Traditional Chinese Medicine on Spinal Cord Injury: A Promising Supplementary Treatment in Future

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Qian Zhang ◽  
Hao Yang ◽  
Jing An ◽  
Rui Zhang ◽  
Bo Chen ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Epigallocatechin Gallate ◽  
Therapeutic Effects ◽  
Cord Injury ◽  
Supplementary Treatment ◽  
Natural Drugs ◽  
Doctoral Dissertations

Objective. Spinal cord injury (SCI) is a devastating neurological disorder caused by trauma. Pathophysiological events occurring after SCI include acute, subacute, and chronic phases, while complex mechanisms are comprised. As an abundant source of natural drugs, Traditional Chinese Medicine (TCM) attracts much attention in SCI treatment recently. Hence, this review provides an overview of pathophysiology of SCI and TCM application in its therapy.Methods. Information was collected from articles published in peer-reviewed journals via electronic search (PubMed, SciFinder, Google Scholar, Web of Science, and CNKI), as well as from master’s dissertations, doctoral dissertations, and Chinese Pharmacopoeia.Results. Both active ingredients and herbs could exert prevention and treatment against SCI, which is linked to antioxidant, anti-inflammatory, neuroprotective, or antiapoptosis effects. The detailed information of six active natural ingredients (i.e., curcumin, resveratrol, epigallocatechin gallate, ligustrazine, quercitrin, and puerarin) and five commonly used herbs (i.e., Danshen, Ginkgo, Ginseng, Notoginseng, and Astragali Radix) was elucidated and summarized.Conclusions. As an important supplementary treatment, TCM may provide benefits in repair of injured spinal cord. With a general consensus that future clinical approaches will be diversified and a combination of multiple strategies, TCM is likely to attract greater attention in SCI treatment.

scholarly journals An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as an Example

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Yuanzhe Yao ◽  
Zeheng Wang ◽  
Liang Li ◽  
Kun Lu ◽  
Runyu Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Prediction Model ◽  
Side Effect ◽  
Treatment Model ◽  
Proposed Model ◽  
Main Components

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of the proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book, and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction.

Becoming a Faithful Defender: Traditional Chinese Medicine against Coronavirus Disease 2019 (COVID-19)

2020 ◽  
Vol 48 (04) ◽  
pp. 763-777 ◽  
Author(s):  
Leyin Zhang ◽  
Jieru Yu ◽  
Yiwen Zhou ◽  
Minhe Shen ◽  
Leitao Sun
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Viral Entry ◽  
Meta Analysis ◽  
Chinese Herb ◽  
Chinese Herbs ◽  
World Health ◽  
Symptom Improvement ◽  
Novel Coronavirus ◽  
Health Organization

The outbreak caused by COVID-19 is causing a major challenge to clinical management and a worldwide threat to public health. So far, there is no specific anti-coronavirus therapy approved for the treatment of COVID-19. Recently, as the efficacy and safety of traditional Chinese medicine (TCM) is widely acknowledged, it has been brought to a crucial status by the public, governments, and World Health Organization (WHO). For a better popularization of TCM, governments have made several advances in regulations and policies for treatment and measures of novel coronavirus pneumonia (NCP). Therefore, on the basis of epidemiology and virology information, we reviewed relevant meta-analysis and clinical studies of anti-coronavirus therapeutics by TCM, in the aspect of mortality, symptom improvement, duration and dosage of corticosteroid, incidence of complications and the like. In addition, we also summarized preclinical rationale for anti-coronavirus activity by TCM in terms of virion assembly and release, as well as viral entry and replication, which could be a useful contribution for figuring out effective Chinese herbal medicine (CHM) for coronavirus, including ingredients from single monomeric compounds, Chinese herbs, Chinese herb extracts and Chinese herb formulas, or potential targets for medicine. We would like to see these relevant studies, ranging from basic researches to clinical application, could provide some idea on effects of CHM to combat COVID-19 or other coronaviruses, and also offer new thinking for the exploration of therapeutic strategies under the guidance of TCM.

Development of Rapid Advice Guidelines for the Treatment of Coronavirus Disease 2019 with Traditional Chinese Medicine

2020 ◽  
Vol 48 (07) ◽  
pp. 1511-1521
Author(s):  
Ning Liang ◽  
Huizhen Li ◽  
Jingya Wang ◽  
Liwen Jiao ◽  
Yanfang Ma ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Observational Studies ◽  
The Body ◽  
Assessment Development ◽  
Evidence Collection ◽  
Novel Coronavirus ◽  
Grade Methodology ◽  
Evidence Based Guidelines

The worldwide spread of the 2019 novel coronavirus has become a profound threat to human health. As the use of medication without established effectiveness may result in adverse health consequences, the development of evidence-based guidelines is of critical importance for the clinical management of coronavirus disease (COVID-19). This research presents methods used to develop rapid advice guidelines on treating COVID-19 with traditional Chinese medicine (TCM). We have followed the basic approach for developing WHO rapid guidelines, including preparing, developing, disseminating and updating each process. Compared with general guidelines, this rapid advice guideline is unique in formulating the body of evidence, as the available evidence for the treatment of COVID-19 with TCM is from either indirect or observational studies, clinical first-hand data together with expert experience in patients with COVID-19. Therefore, our search of evidence not only focuses on clinical studies of treating COVID-19 with TCM but also of similar diseases, such as pneumonia and influenza. Grading of recommendations assessment, development and evaluation (GRADE) methodology was adopted to rate the quality of evidence and distinguish the strength of recommendations. The overall certainty of the evidence is graded as either high, moderate, low or very low, and to give either “strong” or “weak” recommendations of each TCM therapy. The output of this paper will produce the guideline on TCM for COVID-19 and will also provide some ideas for evidence collection and synthesis in the future development of rapid guidelines for COVID-19 in TCM as well as other areas.

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