Immunomodulatory Effects of a Traditional Chinese Medicine with Potential Antiviral Activity: A Self-Control Study

2006 ◽  
Vol 34 (01) ◽  
pp. 13-21 ◽  
Author(s):  
P. M. K. Poon ◽  
C. K. Wong ◽  
K. P. Fung ◽  
C. Y. S. Fong ◽  
E. L. Y. Wong ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Time Course ◽  
Viral Infections ◽  
Venous Blood ◽  
Self Control ◽  
Test Results ◽  
Immunomodulatory Effects ◽  
T Helper Lymphocyte ◽  
Control Study

Traditional Chinese medicine (TCM) has been used for prevention and treatment of severe acute respiratory syndrome (SARS) in Hong Kong during the outbreak in spring 2003. We investigated the immunomodulating effects of an innovative TCM regimen derived from two herbal formulas (Sang Ju Yin and Yu Ping Feng San) for treating febrile diseases. Thirty-seven healthy volunteers were given the oral TCM regimen daily for 14 days. Peripheral venous blood samples were taken on days 0, 15 and 29 for hematology, biochemistry and immunology tests, including the measurement of blood lymphocyte subsets and plasma T-helper lymphocyte types 1 and 2 cytokines and receptor. After 3 months, 23 of the volunteers participated in a control study without TCM treatment for the same time course of blood tests. Two volunteers withdrew on day 2, due to headache and dizziness. All others remained well without any side effects. No participants showed significant changes in their blood test results, except that the T-lymphocyte CD4/CD8 ratio increased significantly from 1.31 ± 0.50 ( mean ± SD ) on day 0 to 1.41 ± 0.63 on day 15 ( p < 0.02), and reduced to 1.32 ± 0.47 on day 29 ( p < 0.05). In the control study, there were no changes in the CD4/CD8 ratio. The transient increase in CD4/CD8 ratio was likely due to the TCM intake. We postulate that the administration of the innovative TCM may have beneficial immunomodulatory effects for preventing viral infections including SARS.

Transformer + GAN based Traditional Chinese Medicine inpatient prescription recommendation (Preprint)

2021 ◽  
Author(s):  
Hong Zhang
Keyword(s):  
Decision Making ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Electronic Medical Records ◽  
Clinical Diagnosis ◽  
Medical Records ◽  
Training Data ◽  
Diagnosis And Treatment ◽  
Test Results ◽  
Treatment Scheme

BACKGROUND Clinical diagnosis and treatment decision making support is at the core of medical artificial intelligent research, in which Traditional Chinese Medicine (TCM) decision making is an important part. Traditional Chinese Medicine is a traditional medical system originated from China, of which the main clinical model is to conduct individualized diagnosis and treatment by relying on the four-diagnosis information. One of the key tasks of the TCM artificial intelligence research is to develop techniques and methods of clinical prescription decision making which takes all the relevant information of a patient as input, and produces a diagnosis and treatment scheme as output. Given the complexity of TCM clinical diagnosis and treatment schemes, decision making support of clinical diagnosis and treatment schemes remains as a research challenge for lacking of an effective solution. Fortunately, as the volume of the massive clinical data in the form of electronic medical records increases rapidly, it becomes possible for the computer to produce personalized diagnosis and treatment scheme recommendation through machine learning on the basis of the clinical big data. OBJECTIVE The objective of this research is to develop a real-time diagnosis and treatment scheme recommendation model for TCM inpatients. This is accomplished by using historical clinical medical records as training data to train a Transformer network. Furthermore, to alleviate the issue of overfitting, a Generative Adversarial Network is used to generate noise-added samples from the original training data. These noise-added samples along with the original samples form the complete train data set. METHODS valid information, such as the patient’s current sickness situation, medicines taken, nursing care given, vital signs, examinations and test results, is extracted from the patient’s electronic medical records, then the obtained information is sorted chronically, to produce a sequence of data of each patient. These time-sequence data is then used as input to the Transformer network. The output of the network would be the prescription information a physician would give. Overfitting is a common problem in machine learning, and becomes especially server when the network is complex with insufficient training data. In this research, a Generative Adversarial Network, is used to double the number of training samples by producing noise-added samples from the original samples. This, to a great extent, lessens the overfitting problem. RESULTS A total of 21,295 copies of inpatient electronic medical records from Guang’anmen traditional Chinese medicine hospital was used in this research. These records were created between January 2017 and December 2018, covering a total of 6352 kinds of medicines. These medicines were sorted into 829 types of first category medicines based on the class relationships among medicines. As shown by the test results, the performance of a fully trained Transformer model can have an average precision rate of 80.58%，and an average recall rate of 68.49%. CONCLUSIONS As shown by the preliminary test results, the Transformer-based TCM prescription recommendation model outperforms the existing conventional methods. The extra training samples generated by the GAN network helps to overcome the overfitting issue, leading a further improved recall rate and precision rate.

scholarly journals Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Cheng Jiang ◽  
Jie Shen ◽  
Dan Shou ◽  
Nani Wang ◽  
Jing Jing ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
High Risk ◽  
Traditional Chinese Medicine ◽  
Case Control Study ◽  
Financial Burden ◽  
Case Control ◽  
Least Squares Regression ◽  
Basic Characteristics ◽  
Nested Case Control ◽  
Control Study

Abstract The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR was illustrated using a nested case-control study in 123 cases and 123 controls. The partial least squares regression (PLSR) models, which mapped the influence of basic characteristics and routine examinations to ADR, were established to predict the risk of ADR. The software was devised to provide an easy-to-use tool for clinic application. The effectiveness of the method was evaluated through its application to new patients with 95.7% accuracy of cases and 91.3% accuracy of controls. By using the method, the patients at high-risk could be conveniently, efficiently and economically recognized without any extra financial burden for additional examination. This study provides a novel insight into individualized management of the patients who will use TCMI.

scholarly journals A Prediction Model for High Risk of Positive RT-PCR Test Results in COVID-19 Patients Discharged From Wuhan Leishenshan Hospital, China

2021 ◽  
Vol 9 ◽  
Author(s):  
Yawei Qian ◽  
Guang Zeng ◽  
Yue Pan ◽  
Yang Liu ◽  
Limao Zhang ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
High Risk ◽  
Traditional Chinese Medicine ◽  
Test Results ◽  
High Risk Factors ◽  
Discharged Patients ◽  
Hospital Stays ◽  
Reduce Risk ◽  
Chinese Medicine Treatment

Several recent studies have reported that a few patients had positive SARS-CoV-2 RNA tests after hospital discharge. The high-risk factors associated with these patients remain to be identified. A total of 463 patients with COVID-19 discharged from Leishenshan Hospital in Wuhan, China, between February 8 and March 8, 2020 were initially enrolled, and 351 patients with at least 2 weeks of follow-up were finally included. Seventeen of the 351 discharged patients had positive tests for SARS-CoV-2 RNA. Based on clinical characteristics and mathematical modeling, patients with shorter hospital stays and less oxygen desaturation were at higher risk of SARS-CoV-2 RNA reoccurrence after discharge. Notably, traditional Chinese medicine treatment offered extensive benefits to reduce risk. Particular attention should be paid to those patients with high risk, and traditional Chinese medicine should be advocated.

scholarly journals Traditional Chinese Medicine (TCM) in the treatment of viral infections: Efficacies and mechanisms

2021 ◽  
pp. 107843
Author(s):  
Kai Huang ◽  
Pan Zhang ◽  
Zhenghao Zhang ◽  
Ji Youn Youn ◽  
Hongchun Zhang ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Viral Infections

scholarly journals Unraveling the Molecular Mechanism of Traditional Chinese Medicine: Formulas Against Acute Airway Viral Infections as Examples

Molecules ◽  
2019 ◽  
Vol 24 (19) ◽  
pp. 3505 ◽  
Author(s):  
Yi Shin Eng ◽  
Chien Hsing Lee ◽  
Wei Chang Lee ◽  
Ching Chun Huang ◽  
Jung San Chang
Keyword(s):  
Chinese Medicine ◽  
Herbal Medicine ◽  
Traditional Chinese Medicine ◽  
Adverse Effects ◽  
Drug Interactions ◽  
Clinical Studies ◽  
Molecular Mechanisms ◽  
Viral Infections ◽  
Herbal Medicines ◽  
Clinical Effects

Herbal medicine, including traditional Chinese medicine (TCM), is widely used worldwide. Herbs and TCM formulas contain numerous active molecules. Basically, they are a kind of cocktail therapy. Herb-drug, herb-food, herb-herb, herb-microbiome, and herb-disease interactions are complex. There is potential for both benefit and harm, so only after understanding more of their mechanisms and clinical effects can herbal medicine and TCM be helpful to users. Many pharmacologic studies have been performed to unravel the molecular mechanisms; however, basic and clinical studies of good validity are still not enough to translate experimental results into clinical understanding and to provide tough evidence for better use of herbal medicines. There are still issues regarding the conflicting pharmacologic effects, pharmacokinetics, drug interactions, adverse and clinical effects of herbal medicine and TCM. Understanding study validation, pharmacologic effects, drug interactions, indications and clinical effects, adverse effects and limitations, can all help clinicians in providing adequate suggestions to patients. At present, it would be better to use herbs and TCM formulas according to their traditional indications matching the disease pathophysiology and their molecular mechanisms. To unravel the molecular mechanisms and understand the benefits and harms of herbal medicine and TCM, there is still much work to be done.

scholarly journals The Classification ofSiniDecoction Pattern in Traditional Chinese Medicine by Gene Expression Profiling

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Hung-Tsu Cheng ◽  
Chaang-Ray Chen ◽  
Chia-Yang Li ◽  
Chao-Ying Huang ◽  
Wun-Yi Shu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clustering Algorithm ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
City Hospital ◽  
Test Results ◽  
Peripheral Blood Mononuclear

We investigated the syndromes of theSinidecoction pattern (SDP), a common ZHENG in traditional Chinese medicine (TCM). The syndromes of SDP were correlated with various severeYang deficiencyrelated symptoms. To obtain a common profile for SDP, we distributed questionnaires to 300 senior clinical TCM practitioners. According to the survey, we concluded 2 sets of symptoms for SDP: (1) pulse feels deep or faint and (2) reversal cold of the extremities. Twenty-four individuals from Taipei City Hospital, Linsen Chinese Medicine Branch, Taiwan, were recruited. We extracted the total mRNA of peripheral blood mononuclear cells from the 24 individuals for microarray experiments. Twelve individuals (including 6 SDP patients and 6 non-SDP individuals) were used as the training set to identify biomarkers for distinguishing the SDP and non-SDP groups. The remaining 12 individuals were used as the test set. The test results indicated that the gene expression profiles of the identified biomarkers could effectively distinguish the 2 groups by adopting a hierarchical clustering algorithm. Our results suggest the feasibility of using the identified biomarkers in facilitating the diagnosis of TCM ZHENGs. Furthermore, the gene expression profiles of biomarker genes could provide a molecular explanation corresponding to the ZHENG of TCM.

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