Dataset Information

2019_nCoV/SARS-CoV-2: rapid classification of betacoronaviruses and identification of Traditional Chinese Medicine as potential origin of zoonotic coronaviruses.

ABSTRACT: The current outbreak of a novel severe acute respiratory syndrome-like coronavirus, 2019_nCoV (now named SARS-CoV-2), illustrated difficulties in identifying a novel coronavirus and its natural host, as the coding sequences of various Betacoronavirus species can be highly diverse. By means of whole-genome sequence comparisons, we demonstrate that the noncoding flanks of the viral genome can be used to correctly separate the recognized four betacoronavirus subspecies. The conservation would be sufficient to define target sequences that could, in theory, classify novel virus species into their subspecies. Only 253 upstream noncoding sequences of Sarbecovirus are sufficient to identify genetic similarities between species of this subgenus. Furthermore, it was investigated which bat species have commercial value in China, and would thus likely be handled for trading purposes. A number of coronavirus genomes have been published that were obtained from such bat species. These bats are used in Traditional Chinese Medicine, and their handling poses a potential risk to cause zoonotic coronavirus epidemics. SIGNIFICANCE AND IMPACT OF THE STUDY: The noncoding upstream and downstream flanks of coronavirus genomes allow for rapid classification of novel Betacoronavirus species and correct identification of genetic relationships. Although bats are the likely natural host of 2019_nCoV, the exact bat species that serves as the natural host of the virus remains as yet unknown. Chinese bat species with commercial value were identified as natural reservoirs of coronaviruses and are used in Traditional Chinese Medicine. Since their trading provides a potential risk for spreading zoonoses, a change in these practices is highly recommended.

SUBMITTER: Wassenaar TM

PROVIDER: S-EPMC7165814 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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2019_nCoV/SARS-CoV-2: rapid classification of betacoronaviruses and identification of Traditional Chinese Medicine as potential origin of zoonotic coronaviruses.

Wassenaar T M TM Zou Y Y

Letters in applied microbiology 20200228 5

The current outbreak of a novel severe acute respiratory syndrome-like coronavirus, 2019_nCoV (now named SARS-CoV-2), illustrated difficulties in identifying a novel coronavirus and its natural host, as the coding sequences of various Betacoronavirus species can be highly diverse. By means of whole-genome sequence comparisons, we demonstrate that the noncoding flanks of the viral genome can be used to correctly separate the recognized four betacoronavirus subspecies. The conservation would be su ...[more]

PMID: 32060933

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Project description:Purpose: This study proposes an S-TextBLCNN model for the efficacy of traditional Chinese medicine (TCM) formula classification. This model uses deep learning to analyze the relationship between herb efficacy and formula efficacy, which is helpful in further exploring the internal rules of formula combination. Methods: First, for the TCM herbs extracted from Chinese Pharmacopoeia, natural language processing (NLP) is used to learn and realize the quantitative expression of different TCM herbs. Three features of herb name, herb properties, and herb efficacy are selected to encode herbs and to construct formula-vector and herb-vector. Then, based on 2,664 formulae for stroke collected in TCM literature and 19 formula efficacy categories extracted from Yifang Jijie, an improved deep learning model TextBLCNN consists of a bidirectional long short-term memory (Bi-LSTM) neural network and a convolutional neural network (CNN) is proposed. Based on 19 formula efficacy categories, binary classifiers are established to classify the TCM formulae. Finally, aiming at the imbalance problem of formula data, the over-sampling method SMOTE is used to solve it and the S-TextBLCNN model is proposed. Results: The formula-vector composed of herb efficacy has the best effect on the classification model, so it can be inferred that there is a strong relationship between herb efficacy and formula efficacy. The TextBLCNN model has an accuracy of 0.858 and an F1-score of 0.762, both higher than the logistic regression (acc = 0.561, F1-score = 0.567), SVM (acc = 0.703, F1-score = 0.591), LSTM (acc = 0.723, F1-score = 0.621), and TextCNN (acc = 0.745, F1-score = 0.644) models. In addition, the over-sampling method SMOTE is used in our model to tackle data imbalance, and the F1-score is greatly improved by an average of 47.1% in 19 models. Conclusion: The combination of formula feature representation and the S-TextBLCNN model improve the accuracy in formula efficacy classification. It provides a new research idea for the study of TCM formula compatibility.

Project description:Background:Traditional, complementary, and alternative medicine (TCAM) has attracted increasing attention in developed countries, but its mainstream status in China, the home of TCAM, is unclear. Over the period of 2004-2016, we analyze the health resources and health resource utilization of traditional medicine in traditional Chinese medicine (TCM) hospitals in China. Methods:Over 2004-2016, we obtained data from all TCM hospitals in all Chinese provinces to create a hospital-based, longitudinal dataset. TCM health resources and their utilization were measured by two outcome variables: (1) primary outcome variables comprising the proportion of TCM physicians, TCM pharmacists, revenue from TCM drugs, and TCM prescriptions and (2) the secondary outcome variables, as proxies of westernization for TCM hospitals, comprising the number of medical equipment above RMB 10,000 and the proportion of surgery in inpatient visits. We used linear regression models with hospital-fixed effects to analyze time trends for the outcome variables. Results:The number of public TCM hospitals remained stable from 2004 to 2016, while the number of private TCM hospitals increased from 294 in 2004 to 1560 in 2016. There was a small percentage increase in the proportion of TCM physicians (0.280%), TCM pharmacists (0.298%), and revenue from Chinese medicines (0.331%) and TCM prescriptions (1.613%) per hospital per year. Chinese drugs accounted for less than a half of the total drug prescriptions, and accordingly, just one-third of the drug revenue was from Chinese medicines at TCM hospitals. The proportions of physicians, pharmacists, revenue from Chinese drug sales, and traditional medicine prescriptions never reach the 60% benchmark target for mainstream in TCM hospitals. As proxies for Western medicine practices in TCM hospitals, the number of medical equipment above RMB 10,000 rapidly rose by over 13 percent per hospital per year, but the proportion of inpatient surgeries declined by 0.830 percentage points per hospital per year, reflecting a mixed trend in the use of Western medicine practices. Conclusion:For the 2004-2016 period, traditional medicine, although making progress towards the mainstream benchmark of 60% TCM services, was still not mainstream at TCM hospitals.

Dataset Information

2019_nCoV/SARS-CoV-2: rapid classification of betacoronaviruses and identification of Traditional Chinese Medicine as potential origin of zoonotic coronaviruses.

Publications

2019_nCoV/SARS-CoV-2: rapid classification of betacoronaviruses and identification of Traditional Chinese Medicine as potential origin of zoonotic coronaviruses.

Similar Datasets

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