eHealth Literacy of Chinese Residents During the Coronavirus Disease 2019 Pandemic: A Cross-sectional Survey.

The Coronavirus Disease 2019 (COVID-19) pandemic has become a leading societal concern. eHealth literacy is important in the prevention and control of this pandemic. The purpose of this study is to identify eHealth literacy of Chinese residents about the COVID-19 pandemic and factors influencing eHealth literacy. A total of 15 694 individuals clicked on the link to the questionnaire, and 15 000 agreed to participate and completed the questionnaire for a response rate of 95.58%. Descriptive statistics, χ2 test, and logistic regression analysis were conducted to analyze participants' level of eHealth literacy about COVID-19 and its influencing factors. The results showed 52.2% of participants had relatively lower eHealth literacy regarding COVID-19 (eHealth literacy score ≤ 48). The scores of the information judgment dimension (3.09 ± 0.71) and information utilization dimension (3.18 ± 0.67) of the eHealth literacy scale were relatively lower. The logistics regression showed that sex, age, education level, level of uncertainty, having people around the respondent diagnosed with COVID-19, relationship with family, and relationship with others were associated to eHealth literacy (χ2 = 969.135, P < .001). The public's eHealth literacy about COVID-19 needs to be improved, especially the ability to judge and utilize online information. Close collaboration among global health agencies, governments, healthcare institutions, and media is needed to provide reliable online information to the public. Interventions to improve eHealth literacy should take into account and accentuate the importance of sex, age, educational background, level of uncertainty, exposure to disease, and social support.

The roles of primary care doctors in the COVID-19 pandemic: consistency and influencing factors of doctor's perception and actions and nominal definitions.

BACKGROUND: At the end of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic broke out. As front-line health professionals, primary care doctors play a significant role in screening SARS-CoV-2 infection and transferring suspected cases. However, the performance of primary care doctors is influenced by their knowledge and role perception. A web-based cross-sectional survey was conducted to assess the consistency and influencing factors of primary care doctor's role perception and expert advice in the guidelines (regulatory definition). METHODS: We designed the questionnaire using "Wenjuanxing" platform, distributed and collected the questionnaire through WeChat social platform, and surveyed 1758 primary care doctors from 11 community health service stations, community health service centers and primary hospitals in Zhejiang Province, China. After the questionnaire was collected, descriptive statistics were made on the characteristics of participants, and univariate analysis and multivariate analysis were used to determine the relevant factors affecting their role cognition. RESULTS: In the reporting and referral suspected cases and patients receiving treatment, most participants' cognition of their roles were consistent with the requirements of guidelines. However, 49.54% and 61.43% of participant doctors were not in line with the government guidelines for diagnosing and classifying COVID-19 and treating suspected cases, respectively. Having a middle or senior professional title and participating in front-line COVID-19 prevention and control work is beneficial to the accurate role perception of diagnosis and classification of COVID-19, the reporting and transfer of suspected cases, and the treatment of suspected cases. CONCLUSIONS: Primary care doctors' role perceptions in the COVID-19 pandemic are not always consistent with government guidelines in some aspects, such as transferring and diagnosing suspected cases. Therefore, it is essential to guide primary care doctors in performing their duties, especially those with lower professional titles.

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile.

Background and Aims: The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. Methods: Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. Results: Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. Conclusions: XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

Background and Aims The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. Methods Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. Results Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. Conclusions XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

When health care workers became patients with COVID-19: A qualitative study.

AIMS: This study aimed to describe the experiences of nurses and other health care workers who were infected with coronavirus disease 2019. METHODS: An empirical phenomenological approach was used. Sixteen participants were recruited in Wuhan using purposive and snowball sampling. Semistructured, in-depth interviews were conducted by telephone in February 2020. Interviews were transcribed verbatim and analysed following Colaizzi's method. RESULTS: Two themes emerged: (1) Intense emotional distress since becoming infected. Participants were fearful of spreading the virus to family and overwhelmed by a lack of information, experienced uncertainty and worried about treatment, felt lonely during isolation and reported moral distress about inadequate health care staffing. (2) Coping strategies were needed. They tried their best to address negative psychological reactions using their professional knowledge and gaining support from others and community resources. CONCLUSIONS: Preparedness for catastrophic events and providing timely and accurate information are major considerations in government policy development, related to pandemics and adequacy of health care personnel. Mental health resources and support, both short- and long-term should be anticipated for health care providers to alleviate their fear and anxiety.

Critically Ill vs. Non-Critically Ill Patients With COVID-19 Pneumonia: Clinical Features, Laboratory Findings, and Prediction.

Objectives: The objective of this study was to investigate the clinical features and laboratory findings of patients with and without critical COVID-19 pneumonia and identify predictors for the critical form of the disease. Methods: Demographic, clinical, and laboratory data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Laboratory parameters were also collected within 3-5 days, 7-9 days, and 11-14 days of hospitalization. Outcomes were followed up until March 12, 2020. Results: Twenty-two patients developed critically ill pneumonia; one of them died. Upon admission, older patients with critical illness were more likely to report cough and dyspnoea with higher respiration rates and had a greater possibility of abnormal laboratory parameters than patients without critical illness. When compared with the non-critically ill patients, patients with serious illness had a lower discharge rate and longer hospital stays, with a trend towards higher mortality. The interleukin-6 level in patients upon hospital admission was important in predicting disease severity and was associated with the length of hospitalization. Conclusions: Many differences in clinical features and laboratory findings were observed between patients exhibiting non-critically ill and critically ill COVID-19 pneumonia. Non-critically ill COVID-19 pneumonia also needs aggressive treatments. Interleukin-6 was a superior predictor of disease severity.

Screening of potent neutralizing antibodies against SARS-CoV-2 using convalescent patients-derived phage-display libraries.

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to threaten public health worldwide, the development of effective interventions is urgently needed. Neutralizing antibodies (nAbs) have great potential for the prevention and treatment of SARS-CoV-2 infection. In this study, ten nAbs were isolated from two phage-display immune libraries constructed from the pooled PBMCs of eight COVID-19 convalescent patients. Eight of them, consisting of heavy chains encoded by the immunoglobulin heavy-chain gene-variable region (IGHV)3-66 or IGHV3-53 genes, recognized the same epitope on the receptor-binding domain (RBD), while the remaining two bound to different epitopes. Among the ten antibodies, 2B11 exhibited the highest affinity and neutralization potency against the original wild-type (WT) SARS-CoV-2 virus (KD = 4.76 nM for the S1 protein, IC50 = 6 ng/mL for pseudoviruses, and IC50 = 1 ng/mL for authentic viruses), and potent neutralizing ability against B.1.1.7 pseudoviruses. Furthermore, 1E10, targeting a distinct epitope on RBD, exhibited different neutralization efficiency against WT SARS-CoV-2 and its variants B.1.1.7, B.1.351, and P.1. The crystal structure of the 2B11-RBD complexes revealed that the epitope of 2B11 highly overlaps with the ACE2-binding site. The in vivo experiment of 2B11 using AdV5-hACE2-transduced mice showed encouraging therapeutic and prophylactic efficacy against SARS-CoV-2. Taken together, our results suggest that the highly potent SARS-CoV-2-neutralizing antibody, 2B11, could be used against the WT SARS-CoV-2 and B.1.1.7 variant, or in combination with a different epitope-targeted neutralizing antibody, such as 1E10, against SARS-CoV-2 variants.

Syndrome differentiation and treatment of Severe acute respiratory syndrome Coronavirus 2 infected pneumonia from cold and dampness epidemic virus

Severe acute respira tory syndrome coronavirus 2 (SARS-CoV-2) infected pneumonia is a new acute infectious pneumonia, which outbroke in Wuhan City, Hubei Province at the end of 2019. It is highly consistent with the epidemic disease of TCM. According to the understanding of TCM epidemic disease, this article analyze d that cold and dampness epidemic virus is the important cause of SARS-CoV-2 infected pneumonia. Based on the general treatment principle of eliminating cold and dampness, avoiding filth and turbidity, this article discussed the TCM syndrome differentiation and treatment in different stages and protective measures of this disease, with the purpose to provide references and help for prevention and treatment of TCM.

Epidemiological characteristics of 147 patients infected with SARS-CoV-2 in Chengdu

Objective: Analysis of the epidemiological characteristics and trend of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were conducted in Chengdu.

Mental Health Status of the General Public, Frontline, and Non-frontline Healthcare Providers in the Early Stage of COVID-19.

Background: The outbreak of COVID-19 occurred in 2020 which resulted in high levels of psychological stress in both the general public and healthcare providers. Purpose: The study aimed to address the mental health status of people in China in the early stage of the COVID-19 outbreak, and to identify differences among the general public, frontline, and non-frontline healthcare providers. Method: A cross-sectional study was used to identify the mental health status of the general public and healthcare providers between Jan 29 and Feb 11, 2020. Data were collected using an online survey from a convenience sample. The instruments used included: Patient Health Questionnaire, Generalized Anxiety Disorder scale, Insomnia Severity Index, and Impact of Event Scale-Revised. Descriptive statistics were used to describe the data. Kruskal-Wallis H tests were performed to assess differences in measurements among the three groups; P < 0.05 (two-sided) was considered to be statistically significant. Results: Results showed that a majority of participants experienced post-traumatic stress (68.8%), depression (46.1%), anxiety (39.8%), and insomnia (31.4%). Significant changes in the mental health status of frontline providers was found as compared to those of the other groups (P < 0.001). Interestingly, the scores of the general public were significantly higher than those of the non-frontline healthcare providers (P < 0.001). Conclusion: These findings provide information to evaluate outbreak associated psychological stress for the general public and healthcare providers, and assist in providing professional support and actionable guidance to ease psychological stress and improve mental health.

Epidemic analysis of COVID-19 in Italy based on spatiotemporal geographic information and Google Trends.

Since the first two novel coronavirus cases appeared in January of 2020, the outbreak of the COVID-19 epidemic seriously threatens the public health of Italy. In this article, the distribution characteristics and spreading of COVID-19 in various regions of Italy were analysed by heat maps. Meanwhile, spatial autocorrelation, spatiotemporal clustering analysis and kernel density method were also applied to analyse the spatial clustering of COVID-19. The results showed that the Italian epidemic has a temporal trend and spatial aggregation. The epidemic was concentrated in northern Italy and gradually spread to other regions. Finally, the Google Trends index of the COVID-19 epidemic was further employed to build a prediction model combined with machine learning algorithms. By using Adaboost algorithm for single-factor modelling,the results show that the AUC of these six features (mask, pneumonia, thermometer, ISS, disinfection and disposable gloves) are all >0.9, indicating that these features have a large contribution to the prediction model. It is also implied that the public's attention to the epidemic is increasing as well as the awareness of the need for protective measures. This increased awareness of the epidemic will prompt the public to pay more attention to protective measures, thereby reducing the risk of coronavirus infection.

How do primary community hospitals enact early response to COVID-19? the experience from Chengdu, China.

Our hospital is a primary hospital in Chengdu, China. Since February 5, our hospital has been listed as the primary designated medical unit for treating new patients with coronavirus disease 2019 (COVID-19) in Jinniu District. In this letter, we share our COVID-19 experience with readers.

Lianhua Qingwen prescription for Coronavirus disease 2019 (COVID-19) treatment: Advances and prospects.

BACKGROUND: An outbreak of Coronavirus Disease 2019 (COVID-19) which was infected by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is still spreading and has led to unprecedented health emergency over the world. Though no specific drug has been developed so far, emerging agents have been confirmed effective or potentially beneficial to restrain it. Lianhua Qingwen (LHQW) is a commonly used Chinese medical preparation to treat viral influenza, including in the fight against SARS in 2002-2003 in China. Recent data also showed that LHQW played a vigorous role in COVID-19 treatment. PURPOSE: This review will elucidate the pre-clinical and clinical evidence of LHQW in lung protection and antiviral activities, and provide timely data delivery for the exploration of effective treatment strategies in the therapy of COVID-19. STUDY DESIGN AND METHOD: The research data were obtained from the academic databases (up to August 8, 2020) including Pubmed, CNKI and Web of Science, on ethnobotany and ethno medicines. The search keywords for screening the literature information were "virus", "COVID-19", or "SARS-CoV-2", and "Lianhua Qingwen". The documents were filtered and summarized for final evaluation. RESULTS: The collected evidence demonstrated that LHQW exhibited benefits against COVID-19. Impressively, LHQW in conjunction with conventional treatment could significantly improve COVID-19 patients as a synergetic strategy. The mechanisms were mainly involved the antiviral activity, and regulation of inflammation response as well as immune function. CONCLUSION: Although the data were far from adequate, the latest advances had shown the benefits of LHQW in COVID-19, especially in combination with other antiviral drugs. This review provides comprehensive evidence of LHQW as a complementary strategy for treating COVID-19. Nevertheless, imperious researches should be conducted to clarify the unconfirmed effects, regulatory mechanisms and adverse reactions of LHQW in treating COVID-19 by means of well designed randomized controlled trials.