Associations of habitual glucosamine use with SARS-CoV-2 infection and hospital admission and death with COVID-19: Evidence from a large population based cohort study.

The coronavirus disease 2019 (COVID-19) pandemic has led to a fundamental number of morbidity and mortality worldwide. Glucosamine was indicated to help prevent and control RNA virus infection preclinically, while its potential therapeutic effects on COVID-19-related outcomes are largely unknown. To assess the association of habitual glucosamine use with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, hospital admission, and mortality with COVID-19 in a large population based cohort. Participants from UK Biobank were reinvited between June and September 2021 to have SARS-CoV-2 antibody testing. The associations between glucosamine use and the risk of SARS-CoV-2 infection were estimated by logistic regression. Hazard ratios (HRs) and 95% confidence intervals (CIs) for COVID-19-related outcomes were calculated using COX proportional hazards model. Furthermore, we carried out propensity-score matching (PSM) and stratified analyses. At baseline, 42 673 (20.7%) of the 205 704 participants reported as habitual glucosamine users. During median follow-up of 1.67 years, there were 15 299 cases of SARS-CoV-2 infection, 4214 cases of COVID-19 hospital admission, and 1141 cases of COVID-19 mortality. The fully adjusted odds ratio of SARS-CoV-2 infection with glucosamine use was 0.96 (95% CI: 0.92-1.01). The fully adjusted HR were 0.80 (95% CI: 0.74-0.87) for hospital admission, and 0.81 (95% CI: 0.69-0.95) for mortality. The logistic regression and Cox proportional hazard analyses after PSM yielded consistent results. Our study demonstrated that habitual glucosamine use is associated with reduced risks of hospital admission and death with COVID-19, but not the incidence of SARS-CoV-2 infection.

Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries.

BACKGROUND: The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. OBJECTIVE: The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. METHODS: We first used core terms and symptom-related keyword-based methods to extract COVID-19-related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis. RESULTS: Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=-8.722, P<.001; model 2, t198=-5.000, P<.001, model 3, t198=-1.882, P=.06; model 4, t198=-4.644, P<.001; model 5, t198=-4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=-1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks. CONCLUSIONS: Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.

Correction: Vital Signs During the COVID-19 Outbreak: A Retrospective Analysis of 19,960 Participants in Wuhan and Four Nearby Capital Cities in China.

[This corrects the article DOI: 10.5334/gh.913.].

Connectedness between crude oil, coal, rare earth, new energy and technology markets: a GARCH-vine-copula-EVT analysis

In recent years, climate change has attracted great attention from governments and promoted the booming of the new energy market indirectly. However, this market will be influenced by traditional energy, rare earth and technology markets. Hence, it is necessary to incorporate these markets into an analytical framework simultaneously and analyse their relationships. Based on the GARCH-vine-copula-EVT model considering extreme risks, we investigate the connectedness between crude oil, coal, rare earth, new energy, and technology markets. The results show that the technology market is most closely associated with the new energy market;the rare earth market reacts as an intermediary market between the new energy market and fossil fuel markets. When taking the rare earth market as the conditional market, the connectedness between the new energy and the other four markets weakens and even becomes negative. Besides, we find that the COVID-19 epidemic has increased the connectedness between these target markets. Finally, the backtesting results of value at risk and expected shortfall show that the GARCH-vine-copula-EVT model considering extreme risks can depict the risk dependence structure between these target markets well. Our study has important reference significance for market participants, risk managers and investors.

Modeling the Linkages between Bitcoin, Gold, Dollar, Crude Oil, and Stock Markets: A GARCH-EVT-Copula Approach

This paper aims to analyze and compare the ability of bitcoin, gold, and dollar to diversify the risk of traditional market such as crude oil and stock markets. Specifically, we model the linkages between bitcoin, gold, dollar, crude oil, and stock markets using the GARCH-EVT-copula approach. The results show that the gold market is in the central position among these markets, which is consistent with the status of gold as a major safe asset. Before the outbreak of COVID-19, bitcoin and the dollar also had the ability to diversify risks, although less effective than gold. However, during the COVID-19 period, gold loses its dominant position and gold, bitcoin, and dollar can no longer act as a hedge. We measure the value at risk (VaR) and expected shortfall (ES) of simulated portfolios constructed based on these five markets and use several backtesting methods to check the validity of the risk measures. The backtesting results show that our model can provide accurate risk measures before and within the COVID-19 period, which may help investors and risk managers construct the optimal portfolios. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The epidemiology and therapeutic options for the COVID-19.

An outbreak of coronavirus disease 2019 (COVID-19), a disease caused by a novel pneumonia virus, has affected over 200 countries and regions worldwide. With the increasing number of patients and deaths, WHO have declared it as a global pandemic currently, indicating a third large-scale epidemic coronavirus has appeared since the emergence of severe acute respiratory syndrome coronavirus (SARS) and Middle-East respiratory syndrome (MERS) in the twenty-first century. Considering the great harm it has caused, researchers throughout the world have been chasing to exploit the pathophysiology, characteristics, and potential remedies for COVID-19 to better battle the outbreak. Therefore, the current study revisits advances of the virology, epidemiology, clinical features, therapeutic options, and prevention of COVID-19. The features of asymptomatic carriers are also been explored.

Polymorphisms and mutations of ACE2 and TMPRSS2 genes are associated with COVID-19: a systematic review.

OBJECTIVE: To determine the effect of polymorphisms and mutations in angiotensin-converting enzyme 2 (ACE2) and Type 2 transmembrane serine proteases (TMPRSS2) genes on susceptibility to corona virus disease 2019 (COVID-19) and patient prognosis. INTRODUCTION: From December 2019 to the current time, an outbreak of epidemic of COVID-19, characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has occurred around the world. It is now clear that SARS-CoV-2 binds to human ACE2 receptors, with expression of these receptors correlated with the rate of SARS-CoV-2 infection and mortality. Polymorphisms in individual patient factors, such as ACE2 and TMPRSS2 genes have been linked with an increase in negative outcomes, although evidence to affirm remains debatable. METHODS: Here, we performed a systematic review, based on guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, with the aim of assessing whether polymorphisms in ACE2 and TMPRSS2 genes affect the COVID-19 condition. We extensively searched PubMed, MEDLINE, Embase, the Cochrane Library, and Web of Science databases, for relevant articles and reports published in English between December 2019 and December 2021. RESULTS: A total of 495 full-text articles were downloaded, of which 185 were excluded after preliminary examination as they were duplicates. Finally, 310 articles were evaluated, by reading their titles and abstracts, and 208 of them eliminated based on our selection criteria. Finally, 33 articles met our inclusion criteria and were included in the final assessment. Genetic data from 33,923 patients with COVID-19 drawn from the general population and deriving from over 160 regions and 50 countries, as well as approximately 560,000 samples from global-public genetic databases, were included in our analysis. Ultimately, we identified 10 SNPs and 21 mutations in the ACE2 gene, along with 13 SNPs and 12 variants in the TMPRSS2 gene, which may be associated with COVID-19. CONCLUSIONS: ACE2 and TMPRSS2 play vital roles in the onset, development, and prognosis of SARS-CoV-2 infection, and have both been strongly associated with vulnerability, intensity, and the clinical result of COVID-19. Overall, these genetic factors may have potential for future development of personalized drugs and vaccines against COVID-19. TRIAL REGISTRATION: CRD42021239400 in PROSPERO 2021.

Mediators of the Effects of Canagliflozin (CANA) on Heart Failure (HF) and CV Death in Patients with Type 2 Diabetes (T2D) and Chronic Kidney Disease (CKD)

The benefits of CANA for HF in people with T2D at CV risk appeared to be statistically mediated by erythrocyte concentration, serum urate, and urinary albumin:creatinine ratio (UACR) in the CANVAS Program. CANA reduced the risk of HF in patients with T2D and CKD in CREDENCE. We explored potential mediators of CANA's effects on the composite of hospitalized HF (HHF) and CV death. Mediation analyses are hypothesis-generating observational analyses that calculate the effect of selected biomarkers on the overall treatment effect using time-varying Cox regression. We compared hazard ratios for the effect of randomized treatment from an unadjusted model versus a model adjusted for the average post-randomization level of the biomarker of interest. 62 routine clinical biomarkers and vital sign indicators were collected on all participants and tested as potential mediators. When multiple potential mediators represented a single pathway, those with the strongest univariable mediation were tested in multivariable models. 12 biomarkers, including 3 markers of volume/erythropoiesis (hematocrit [24%], hemoglobin [32%], erythrocytes [27%]), 2 markers of kidney function (UACR [28%], eGFR from wk 3 [7.4%]), and serum albumin (39%), serum protein (24%), lactate dehydrogenase (13%), systolic BP (10%), urine pH (8%), serum urate (7%) and gamma glutamyltransferase (4%), mediated the effect of CANA on HHF/CV death in univariable models. In the multivariable model, hemoglobin, UACR, serum urate and systolic BP maximized cumulative mediation (74%). A diverse set of potential mediators of CANA's effect on HHF/CV death were identified with serum albumin, hemoglobin (or its analogues) and UACR being the most important. The extent to which these mediators reflect underlying inflammatory, nutritional, volume-related or cardiorenal pathways is unclear and underscores the need for further research into the mechanisms of benefit of SGLT2 inhibitors. Disclosure: J. Li: Employee;Self;George Institute. B. Neal: Research Support;Self;Janssen Research & Development, LLC, Merck Schering Plough, Roche Pharma, Servier, Zydus Pharmaceuticals, Inc. Other Relationship;Self;Abbott, Janssen, Novartis, Pfizer, Roche, and Servier. H.L. Heerspink: Consultant;Self;AbbVie Inc., AstraZeneca, Boehringer Ingelheim International GmbH, CSL Behring, Gilead Sciences, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Mitsubishi Tanabe Pharma Corporation, Mundipharma International, Retrophin, Inc. C. Arnott: Employee;Self;George Institute for Global Health. C. Cannon: None. R. Agarwal: Other Relationship;Self;AbbVie Inc., Akebia Therapeutics, Amgen, AstraZeneca, Bayer Inc., Bird Rock Bio, Boehringer Ingelheim Pharmaceuticals, Inc., Celgene, Daiichi Sankyo, Eli Lilly and Company, GlaxoSmithKline plc., Ironwood Pharmaceuticals, Johnson & Johnson, Merck & Co., Inc., Novartis Pharmaceuticals Corporation, OPKO Health, Inc., Reata, Relypsa, Inc., Sandoz, Sanofi, Takeda Pharmaceutical Company Limited, ZS Pharma. G. Bakris: Consultant;Self;Alnylam, Merck & Co., Inc., Relypsa, Inc., Teijin Pharma Limited. Other Relationship;Self;Bayer AG, Novo Nordisk Inc., Vascular Dynamics. D.M. Charytan: Advisory Panel;Self;Allena Pharmaceuticals, AstraZeneca, Merck & Co., Inc., PLC Medical. Employee;Self;BAIM Institute. Research Support;Self;Janssen Pharmaceuticals, Inc. Other Relationship;Self;Baim, Amgen, Medtronic/Covidien, Zoll, Fresenius, Daiichi Sankyo, Douglas and London, Eli Lilly, Merck, Gilead, and Novo Nordisk. D. de Zeeuw: Advisory Panel;Self;AbbVie Inc., Bayer AG, Boehringer Ingelheim International GmbH, Fresenius Medical Care, Janssen Pharmaceuticals, Inc., Mitsubishi Tanabe Pharma Corporation. T. Greene: Other Relationship;Self;Janssen, Durect, and Pfizer. A. Levin: Consultant;Self;Janssen Pharmaceuticals, Inc. Research Support;Self;AstraZeneca K.K., Boehringer Ingelheim Pharmaceuticals, Inc., Gilead Sciences, Inc. R. Oh: Employee;Self;Janssen Pharmaceuticals, Inc. C.A. Pollock: Advisory Panel;Self;AstraZeneca, Boehringer Ingelheim Pharma euticals, Inc., Eli Lilly and Company, Merck Sharp & Dohme Corp., Otsuka Pharmaceutical Co., Ltd., Vifor Pharma Group. Research Support;Self;Diabetes Australia. Speaker's Bureau;Self;AstraZeneca, Cipla Inc., MedErgy, Medscape, Mitsubishi Tanabe Pharma Corporation, Novartis AG, Otsuka Pharmaceutical Co., Ltd., Vifor Pharma Group. Other Relationship;Self;Amgen, George Institute for Global Health, Gilead Sciences, Inc., Janssen Pharmaceuticals, Inc. D.C. Wheeler: Advisory Panel;Self;Boehringer Ingelheim Pharmaceuticals, Inc., Reata. Consultant;Self;AstraZeneca, Bayer AG, GlaxoSmithKline, Janssen Pharmaceuticals, Inc. Speaker's Bureau;Self;Amgen, Astellas Pharma Inc., Mundipharma International, Napp Pharmaceuticals. Y. Yavin: Employee;Self;Janssen Research & Development, LLC. H. Zhang: Employee;Self;Renal Division of Peking University First Hospital. B. Zinman: Advisory Panel;Self;Abbott, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk Inc., Sanofi-Aventis. G. Di Tanna: Employee;Self;George Institute for Global Health. V. Perkovic: Other Relationship;Self;See Other Relationship field. K.W. Mahaffey: Consultant;Self;Medscape, Mitsubishi, Myokardia, NIH, Novartis, Novo Nordisk, Portola, Radiometer, Regeneron, SmartMedics, Springer Publishing, UCSF. Research Support;Self;Afferent, Amgen, Apple, Inc, AstraZeneca, Cardiva Medical, Inc, Daiichi, Ferring, Google (Verily), Johnson & Johnson, Luitpold, Medtronic, Merck, NIH, Novartis, Sanofi, St. Jude, Tenax. M. Jardine: Other Relationship;Self;See Other Relationship field. Funding: Janssen Research & Development, LLC [ABSTRACT FROM AUTHOR] Copyright of Diabetes is the property of American Diabetes Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Effect of Canagliflozin on Total Hospitalization for Heart Failure Events in Patients with Type 2 Diabetes and Chronic Kidney Disease

Background: The sodium glucose co-transporter 2 inhibitor canagliflozin reduced the risk of first hospitalization for heart failure (HHF) in the CREDENCE trial. The prevention of recurrent events is important to patients, clinicians and payers. In this post-hoc analysis, we evaluated the effect of canagliflozin on total HHF events. Methods: The CREDENCE trial compared canagliflozin or matching placebo and followed patients for a median of 2.6 years. The study included 4401 participants with type 2 diabetes, substantial albuminuria and estimated glomerular filtration rate (eGFR) 30 to <90 ml/min/1.73 m2 receiving renin-angiotensin system blockade. Negative binomial regression models were performed to assess the effect of canagliflozin on the total number of HHF events. Results: The mean age of participants was 63 years, with a mean eGFR of 56.3 ml/min/1.73 m2, while 50% had a history of previous cardiovascular disease and 15% had a history of heart failure. During the trial, 230 people experienced 326 total HHF events, with 166 having 1 event, 43 having 2 events, 15 having 3 events, and 6 having ≥4 events;thus, 42% of those experiencing at least 1 event went on to suffer a recurrent event during the follow up. Canagliflozin reduced first HHF events by 39% (hazard ratio [HR], 0.61;95% confidence interval [CI] 0.47-0.80;P <0.001;number needed to treat [NNT], 46;95% CI 29-124) and total HHF events by 36% (event rates of 22.0 and 34.8 participants with an event/1000 patient-years with canagliflozin and placebo, respectively;incidence rate ratio [RR], 0.64;95% CI 0.56-0.73;P <0.001). Conclusions: Canagliflozin significantly reduced first and recurrent HHF events. These findings provide further support for the benefit of continuing canagliflozin therapy after an index heart failure presentation to prevent recurrent HHF events. Disclosure: J. Li: Employee;Self;George Institute. M.J. Jardine: Other Relationship;Self;See Other Relationship field. B. Neal: Research Support;Self;Janssen Research & Development, LLC, Merck Schering Plough, Roche Pharma, Servier, Zydus Pharmaceuticals, Inc. Other Relationship;Self;Abbott, Janssen, Novartis, Pfizer, Roche, and Servier. H.L. Heerspink: Consultant;Self;AbbVie Inc., AstraZeneca, Boehringer Ingelheim International GmbH, CSL Behring, Gilead Sciences, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Mitsubishi Tanabe Pharma Corporation, Mundipharma International, Retrophin, Inc. C. Cannon: None. R. Agarwal: Other Relationship;Self;AbbVie Inc., Akebia Therapeutics, Amgen, AstraZeneca, Bayer Inc., Bird Rock Bio, Boehringer Ingelheim Pharmaceuticals, Inc., Celgene, Daiichi Sankyo, Eli Lilly and Company, GlaxoSmithKline plc., Ironwood Pharmaceuticals, Johnson & Johnson, Merck & Co., Inc., Novartis Pharmaceuticals Corporation, OPKO Health, Inc., Reata, Relypsa, Inc., Sandoz, Sanofi, Takeda Pharmaceutical Company Limited, ZS Pharma. G. Bakris: Consultant;Self;Alnylam, Merck & Co., Inc., Relypsa, Inc., Teijin Pharma Limited. Other Relationship;Self;Bayer AG, Novo Nordisk Inc., Vascular Dynamics. D.M. Charytan: Advisory Panel;Self;Allena Pharmaceuticals, AstraZeneca, Merck & Co., Inc., PLC Medical. Employee;Self;BAIM Institute. Research Support;Self;Janssen Pharmaceuticals, Inc. Other Relationship;Self;Baim, Amgen, Medtronic/Covidien, Zoll, Fresenius, Daiichi Sankyo, Douglas and London, Eli Lilly, Merck, Gilead, and Novo Nordisk. D. de Zeeuw: Advisory Panel;Self;AbbVie Inc., Bayer AG, Boehringer Ingelheim International GmbH, Fresenius Medical Care, Janssen Pharmaceuticals, Inc., Mitsubishi Tanabe Pharma Corporation. R. Edwards: Employee;Self;Janssen. T. Greene: Other Relationship;Self;Janssen, Durect, and Pfizer. A. Levin: Consultant;Self;Janssen Pharmaceuticals, Inc. Research Support;Self;AstraZeneca K.K., Boehringer Ingelheim Pharmaceuticals, Inc., Gilead Sciences, Inc. C.A. Pollock: Advisory Panel;Self;AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Merck Sharp & Dohme Corp., Otsuka Pharmaceutical Co., Ltd., Vifor Pharma Group. Research Support;Self;Diabetes Australia. Speaker's Bureau;Self;AstraZeneca, Cipla Inc., MedErgy, Medscape, Mitsubishi Tanabe Pharma Corporation, Novartis AG, Otsuka Pharmaceutical Co., Ltd., Vifor Pharma Group. Other Relationship;Self;Amgen, George Institute for Global Health, Gilead Sciences, Inc., Janssen Pharmaceuticals, Inc. N. Rosenthal: None. D.C. Wheeler: Advisory Panel;Self;Boehringer Ingelheim Pharmaceuticals, Inc., Reata. Consultant;Self;AstraZeneca, Bayer AG, GlaxoSmithKline, Janssen Pharmaceuticals, Inc. Speaker's Bureau;Self;Amgen, Astellas Pharma Inc., Mundipharma International, Napp Pharmaceuticals. H. Zhang: Employee;Self;Renal Division of Peking University First Hospital. B. Zinman: Advisory Panel;Self;Abbott, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk Inc., Sanofi-Aventis. V. Perkovic: Other Relationship;Self;See Other Relationship field. K.W. Mahaffey: Consultant;Self;Medscape, Mitsubishi, Myokardia, NIH, Novartis, Novo Nordisk, Portola, Radiometer, Regeneron, SmartMedics, Springer Publishing, UCSF. Research Support;Self;Afferent, Amgen, Apple, Inc, AstraZeneca, Cardiva Medical, Inc, Daiichi, Ferring, Google (Verily), Johnson & Johnson, Luitpold, Medtronic, Merck, NIH, Novartis, Sanofi, St. Jude, Tenax. C. Arnott: Employee;Self;George Institute for Global Health. Funding: Janssen Research & Development, LLC [ABSTRACT FROM AUTHOR] Copyright of Diabetes is the property of American Diabetes Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis

COVID-19 has seriously threatened people’s health and well-being across the globe since it was first reported in Wuhan, China in late 2019. This study investigates the mechanism of COVID-19 transmission in different periods within and between cities in China to better understand the nature of the outbreak. We use Moran’s I, a measure of spatial autocorrelation, to examine the spatial dependency of COVID-19 and a dynamic spatial autoregressive model to explore the transmission mechanism. We find that the spatial dependency of COVID-19 decreased over time and that the transmission of the disease could be divided into three distinct stages: an eruption stage, a stabilization stage, and a declination stage. The infection rate between cities was close to one-third of the infection rate within cities at the eruption stage, while it reduced to zero at the declination stage. We also find that the infection rates within cities at the eruption stage and declination stage were similar. China’s policies for controlling the spread of the epidemic, specifically with respect to limiting inter-city mobility and implementing intra-city travel restrictions (social isolation), were most effective in reducing the viral transmission of COVID-19. The findings from this study indicate that the elimination of inter-city mobility had the largest impact on controlling disease transmission.

Vital Signs During the COVID-19 Outbreak: A Retrospective Analysis of 19,960 Participants in Wuhan and Four Nearby Capital Cities in China.

Background: The implications of city lockdown on vital signs during the COVID-19 outbreak are unknown. Objective: We longitudinally tracked vital signs using data from wearable sensors and determined associations with anxiety and depression. Methods: We selected all participants in the HUAWEI Heart Study from Wuhan and four nearby large provincial capital cities (Guangzhou, Chongqing, Hangzhou, Zhengzhou) and extracted all data from 26 December 2019 (one month before city lockdown) to 21 February 2020. Sleep duration and quality, daily steps, oxygen saturation and heart rate were collected on a daily basis. We compared the vital signs before and after the lockdown using segmented regression analysis of the interrupted time series. The depression and anxiety cases were defined as scores ≥8 on the Hospital Anxiety and Depression Scale depression and anxiety subscales [HADS-D and HADS-A] in 727 participants who finished the survey. Results: We included 19,960 participants (mean age 36 yrs, 90% men). Compared with pre-lockdown, resting heart rate dropped immediately by 1.1 bpm after city lockdown (95% confidence interval [CI]: -1.8, -0.4). Sleep duration increased by 0.5 hour (95% CI: 0.3, 0.8) but deep sleep ratio decreased by 0.9% (95% CI: -1.2, -0.6). Daily steps decreased by 3352 steps (95% CI: -4333, -2370). Anxiety and depression existed in 26% and 17% among 727 available participants, respectively, and associated with longer sleep duration (0.2 and 0.1 hour, both p < 0.001). Conclusions: Lockdown of Wuhan in China was associated with an adverse vital signs profile (reduced physical activity, heart rate, and sleep quality, but increased sleep duration). Wearable devices in combination with mobile-based apps may be useful to monitor both physical and mental health. Clinical trial registration: The trial is registered at Chinese Clinical Trial Registry (ChiCTR) website (ChiCTR-OOC-17014138).

The therapeutic effect and safety of the drugs for COVID-19: A systematic review and meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread almost all regions of the world and caused great loss to the whole body of mankind. Thus, numerous clinical trials were conducted to find specific medicine for COVID-19 recently. However, it remains unanswered whether they are beneficial. OBJECTIVE: This study aimed to evaluate the efficiency and safety of the COVID-19 medicine. METHODS: Studies were determined through searching PubMed, Embase, Cochrane Library, and Medline. The studies of COVID-19 medicine were involved with eligible end points containing mortality, discharge rate, rate of clinical improvement, and rate of serious adverse events. RESULTS: A total of 33 studies involving 37,879 patients were included in our study, whose intervening measures contained three major types of COVID-19 medicine, ACEI/ARB, antiviral medicine, and chloroquine/hydroxychloroquine. Compared to control group, COVID-19 drugs have no distinct effect on mortality (RR, 0.93; 95% CI, 0.79-1.11, P = .43) and discharge rate (RR, 1.06; 95% CI, 0.98-1.14, P = .13). However, antiviral medicine presents the obvious advantage in clinical improvement (RR, 1.11; 95% CI, 1.01-1.23, P < .05). In addition, the serious adverse events rate (RR, 0.75; 95% CI, 0.63-0.88, P < .05) of COVID-19 medicine is lower than control group. CONCLUSION: The results indicated antiviral medicine was potential specific medicine for COVID-19 treatment by improving clinical symptoms, but it failed to increase the discharge rate and reduce mortality. Chloroquine/hydroxychloroquine and ACEI/ARB had no significant effect on treatment of COVID-19, thus they were not recommended for routine medication. Moreover, more trials are needed to find effective drugs to lower the mortality of COVID-19 patients.

The impact of 2019 novel coronavirus on heart injury: A Systematic review and Meta-analysis.

BACKGROUND: Evidence about COVID-19 on cardiac injury is inconsistent. OBJECTIVES: We aimed to summarize available data on severity differences in acute cardiac injury and acute cardiac injury with mortality during the COVID-19 outbreak. METHODS: We performed a systematic literature search across Pubmed, Embase and pre-print from December 1, 2019 to March 27, 2020, to identify all observational studies that reported cardiac specific biomarkers (troponin, creatine kinase-MB fraction, myoglobin, or NT-proBNP) during COVID-19 infection. We extracted data on patient demographics, infection severity, comorbidity history, and biomarkers during COVID-19 infection. Where possible, data were pooled for meta-analysis with standard (SMD) or weighted (WMD) mean difference and corresponding 95% confidence intervals (CI). RESULTS: We included 4189 confirmed COVID-19 infected patients from 28 studies. More severe COVID-19 infection is associated with higher mean troponin (SMD 0.53, 95% CI 0.30 to 0.75, p < 0.001), with a similar trend for creatine kinase-MB, myoglobin, and NT-proBNP. Acute cardiac injury was more frequent in those with severe, compared to milder, disease (risk ratio 5.99, 3.04 to 11.80; p < 0.001). Meta regression suggested that cardiac injury biomarker differences of severity are related to history of hypertension (p = 0.030). Also COVID19-related cardiac injury is associated with higher mortality (summary risk ratio 3.85, 2.13 to 6.96; p < 0.001). hsTnI and NT-proBNP levels increased during the course of hospitalization only in non-survivors. CONCLUSION: The severity of COVID-19 is associated with acute cardiac injury, and acute cardiac injury is associated with death. Cardiac injury biomarkers mainly increase in non-survivors. This highlights the need to effectively monitor heart health to prevent myocarditis in patients infected with COVID-19.