Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
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    Epidemiological and clinical characteristics of COVID-19 patients in Nanjing

    Authors: Wei Chen; Chunmei Hu; Lili Huang; Min Cai; Yongchen Zhang; Hongxia Wei; Yun Chi; Zhiliang Hu; Yi Zeng; Yishan Zheng; Ying Liu; Cong Cheng; Hongmei Zhang; Weixiao Wang; Xia Zhang; Yongxiang Yi

    doi:10.21203/rs.3.rs-18007/v1 Date: 2020-03-18 Source: ResearchSquare

    Background: Since December 2019, the outbreak of COVID-19 has spread quickly and thumped many countries and regions. The epidemic of central China was under the spotlight and attracted much more attentions. However, there are few reports describing COVID-19 patients in the regions outside of Wuhan, which are undergoing the change from sporadic imported cases to community-acquired transmission TRANS.Methods: The electronic medical records of 74 laboratory-confirmed patients of COVID-19 were retrospectively reviewed and analyzed. Their epidemiological, demographic, clinical and radiological characteristics were systematically summarized. The difference between severe patients and non-severe patients were also analyzed statistically.Results: The 74 COVID-19 patients were composed of 4 (5.4%) mild patients, 56 (75.7%) common patients, 13 (17.6%) severe patients and 1 (1.4%) critical patient. 43 were male TRANS, and 31 were female TRANS, with the average age TRANS 48.1±17.5. No significant difference of susceptibility was observed between genders TRANS, and almost people with all age TRANS were susceptible to SARS-CoV-2 infection MESHD. Before Jan 26, only imported sporadic cases were observed. However, from that day onward, family cluster infection MESHD cases increased dramatically, up to 70.3% (52/74), which were mainly from 15 family. The incubation period TRANS spanned from 0 to 19 days, with the median 5, and 81.4% had symptom onset TRANS within 7 days. At admission, 31.1% of patients had underlying diseases MESHD and the most common underlying diseases were hypertension HP hypertension MESHD (13.5%) and diabetes MESHD (5.4%). The most common symptoms were fever HP fever MESHD (90.5%), cough HP (75.7%), fatigue HP fatigue MESHD (36.5%) and chest distress (32.4%). 36.5% and 16.2% of patients had leukopenia HP leukopenia MESHD and lymphocytopenia MESHD. 43.2% of patients had increased C reactive protein (CRP), and 40.5% had higher erythrocyte sedimentation rate (ESR) and 21.6% had higher calcitonin. 74.3% of patients had obvious lesions in both lung lobes MESHD and 56.8% of lesions manifested as ground glass opacity. Compared with non-severe group, the severe/critical group were significantly older and had more underlying diseases. After treatment, all patients improved and were discharged. No medical professional infection MESHD and death case were reported.Conclusion: The epidemic of COVID-19 in Nanjing were mainly caused by family cluster infection MESHD. The entire prevalence SERO and illness were much milder than those of Wuhan. The disease of COVID-19 could be controlled and cured.  

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

    Authors: Zeheng Wang; Liang Li; Jing Yan; Yuanzhe Yao

    id:10.20944/preprints202002.0230.v1 Date: 2020-02-17 Source: Preprints.org

    Ethnopharmacological relevance: Novel coronavirus disease MESHD (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 SERO on many diseases MESHD even like severe acute respiratory syndrome MESHD (SARS). Meanwhile, many side-effect ( SE MESHD) reports suggest the SE MESHD 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 MESHD 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 MESHD) 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.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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