A Review on Remdesivir: A Broad-spectrum Antiviral Molecule for Possible COVID-19 Treatment.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel coronavirus strain and the causative agent of COVID-19 was emerged in Wuhan, China, in December 2019 [1]. This pandemic situation and magnitude of suffering have led to global effort to find out effective measures for discovery of new specific drugs and vaccines to combat this deadly disease. In addition to many initiatives to develop vaccines for protective immunity against SARS-CoV-2, some of which are at various stages of clinical trials, researchers worldwide are currently using available conventional therapeutic drugs with the potential to combat the disease effectively in other viral infections and it is believed that these antiviral drugs could act as a promising immediate alternative. Remdesivir (RDV), a broad-spectrum anti-viral agent, initially developed for the treatment of Ebola virus (EBOV) and known to showed promising efficiency in in vitro and in vivo studies against SARS and MERS coronaviruses, is now being investigated against SARS-CoV-2. On May 1, 2020, The U.S. Food and Drug Administration (FDA) granted Emergency Use Authorization (EUA) for RDV to treat COVID- 19 patients [2]. A number of multicentre clinical trials are on-going to check the safety and efficacy of RDV for the treatment of COVID-19. Results of published double blind, and placebo-controlled trial on RDV against SARS-CoV-2, showed that RDV administration led to faster clinical improvement in severe COVID-19 patients compared to placebo. This review highlights the available knowledge about RDV as a therapeutic drug for coronaviruses and its preclinical and clinical trials against COVID-19.

The Role of Hydroxychloroquine in COVID-19: A Systematic Review and Meta-Analysis.

OBJECTIVE: A systematic review and meta-analysis was carried out to examine the role of hydroxychloroquine (HCQ) in the treatment of COVID-19. METHODS: We performed a systematic search in PubMed, Scopus, Embase, CochraneLibrary, Web of Science, Google Scholar, and medRxiv pre-print databases using available MeSH terms for COVID-19 and hydroxychloroquine. Data from all studies that focused on the effectiveness of HCQ with or without the addition of azithromycin (AZM) in confirmed COVID-19 patients, which were published up to 12 September 2020, were collated for analysis using CMA v.2.2.064. RESULTS: Our systematic review retrieved 41 studies. Among these, 37 studies including 45,913 participants fulfilled the criteria for subsequent meta-analysis. The data showed no significant difference in treatment efficacy between the HCQ and control groups (RR: 1.02, 95% CI, 0.81-1.27). Combination of HCQ with AZM also did not lead to improved treatment outcomes (RR: 1.26, 95% CI, 0.91-1.74). Furthermore, the mortality difference was not significant, neither in HCQ treatment group (RR: 0.86, 95% CI, 0.71-1.03) nor in HCQ plus AZM treatment group (RR: 1.28, 95% CI, 0.76-2.14) in comparison to controls. Meta-regression analysis showed that age was the factor that significantly affected mortality (P<0.00001). CONCLUSION: The meta-analysis found that there was no clinical benefit of using either HCQ by itself or in combination with AZM for the treatment of COVID-19 patients. Hence, it may be prudent for clinicians and researchers to focus on other therapeutic options that may show greater promise in this disease.

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

Covid-19: Fat, Obesity, Inflammation, Ethnicity, and Sex Differences.

Although obesity is known to be a risk factor for COVID-19 severity, there is an urgent need to distinguish between different kinds of fat-visceral and subcutaneous fat-and their inflammation status in COVID-19. These different fat types have partially diverging biochemical roles in the human body, and they are differentially associated with SARS-CoV-2, which targets the angiotensin-converting enzyme 2 (ACE2) for cell entry. ACE2 is highly expressed in adipose tissue, especially in visceral fat, suggesting an important role for this tissue in determining COVID-19 disease severity. In this perspective article, we discuss group differences in the amount of visceral fat levels and the extent of inflammation in adipocytes of visceral fat tissue, which may, in part, drive population, cross-national, ethnic, and sex differences in COVID-19 disease. It is vital to steer the scientific community's attention to the effects of visceral fat in creating individual and population differences in COVID-19 severity. This can help researchers unravel the reasons for the reported population, ethnic, and sex differences in COVID-19 severity and mortality.

AI for radiographic COVID-19 detection selects shortcuts over signal.

Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals. We observe that the approach to obtain training data for these AI systems introduces a nearly ideal scenario for AI to learn these spurious "shortcuts." Because this approach to data collection has also been used to obtain training data for detection of COVID-19 in computed tomography scans and for medical imaging tasks related to other diseases, our study reveals a far-reaching problem in medical imaging AI. In addition, we show that evaluation of a model on external data is insufficient to ensure AI systems rely on medically relevant pathology, since the undesired "shortcuts" learned by AI systems may not impair performance in new hospitals. These findings demonstrate that explainable AI should be seen as a prerequisite to clinical deployment of ML healthcare models.

The Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID): Development of Case Report Forms for Global Use.

Since its original report in January 2020, the coronavirus disease 2019 (COVID-19) due to Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infection has rapidly become one of the deadliest global pandemics. Early reports indicate possible neurological manifestations associated with COVID-19, with symptoms ranging from mild to severe, highly variable prevalence rates, and uncertainty regarding causal or coincidental occurrence of symptoms. As neurological involvement of any systemic disease is frequently associated with adverse effects on morbidity and mortality, obtaining accurate and consistent global data on the extent to which COVID-19 may impact the nervous system is urgently needed. To address this need, investigators from the Neurocritical Care Society launched the Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID). The GCS-NeuroCOVID consortium rapidly implemented a Tier 1, pragmatic study to establish phenotypes and prevalence of neurological manifestations of COVID-19. A key component of this global collaboration is development and application of common data elements (CDEs) and definitions to facilitate rigorous and systematic data collection across resource settings. Integration of these elements is critical to reduce heterogeneity of data and allow for future high-quality meta-analyses. The GCS-NeuroCOVID consortium specifically designed these elements to be feasible for clinician investigators during a global pandemic when healthcare systems are likely overwhelmed and resources for research may be limited. Elements include pediatric components and translated versions to facilitate collaboration and data capture in Latin America, one of the epicenters of this global outbreak. In this manuscript, we share the specific data elements, definitions, and rationale for the adult and pediatric CDEs for Tier 1 of the GCS-NeuroCOVID consortium, as well as the translated versions adapted for use in Latin America. Global efforts are underway to further harmonize CDEs with other large consortia studying neurological and general aspects of COVID-19 infections. Ultimately, the GCS-NeuroCOVID consortium network provides a critical infrastructure to systematically capture data in current and future unanticipated disasters and disease outbreaks.

Intravenous high-dose vitamin C for the treatment of severe COVID-19: study protocol for a multicentre randomised controlled trial.

INTRODUCTION: The rapid worldwide spread of COVID-19 has caused a global health crisis. To date, symptomatic supportive care has been the most common treatment. It has been reported that the mechanism of COVID-19 is related to cytokine storms and subsequent immunogenic damage, especially damage to the endothelium and alveolar membrane. Vitamin C (VC), also known as L-ascorbic acid, has been shown to have antimicrobial and immunomodulatory properties. A high dose of intravenous VC (HIVC) was proven to block several key components of cytokine storms, and HIVC showed safety and varying degrees of efficacy in clinical trials conducted on patients with bacterial-induced sepsis and acute respiratory distress syndrome (ARDS). Therefore, we hypothesise that HIVC could be added to the treatment of ARDS and multiorgan dysfunction related to COVID-19. METHODS AND ANALYSIS: The investigators designed a multicentre prospective randomised placebo-controlled trial that is planned to recruit 308 adults diagnosed with COVID-19 and transferred into the intensive care unit. Participants will randomly receive HIVC diluted in sterile water or placebo for 7 days once enrolled. Patients with a history of VC allergy, end-stage pulmonary disease, advanced malignancy or glucose-6-phosphate dehydrogenase deficiency will be excluded. The primary outcome is ventilation-free days within 28 observational days. This is one of the first clinical trials applying HIVC to treat COVID-19, and it will provide credible efficacy and safety data. We predict that HIVC could suppress cytokine storms caused by COVID-19, help improve pulmonary function and reduce the risk of ARDS of COVID-19. ETHICS AND DISSEMINATION: The study protocol was approved by the Ethics Committee of Zhongnan Hospital of Wuhan University (identifiers: Clinical Ethical Approval No. 2020001). Findings of the trial will be disseminated through peer-reviewed journals and scientific conferences. TRIAL REGISTRATION NUMBER: NCT04264533.

A Comprehensive Analysis of Clinical Trials in the COVID-19 Pandemic Era.

Background and objective: Despite medical advances, we are facing the unprecedented disaster of the coronavirus disease 2019 (COVID-19) pandemic without available treatments and effective vaccines. As the COVID-19 pandemic has approached its culmination, desperate efforts have been made to seek proper treatments and response strategies, and the number of clinical trials has been rapidly increasing. In this time of the pandemic, it is believed that learning lessons from it would be meaningful in preparing for future pandemics. Thus, this study aims at providing a comprehensive landscape of COVID-19 related clinical trials based on the ClinicalTrials.gov database. Materials and methods: Up to 30 March 2020, we identified a total of 147 eligible clinical trials and reviewed the overview of the studies. Results: Until then, the most clinical trials were set up in China. Treatment approaches are the most frequent purpose of the registered studies. Chloroquine, interferon, and antiviral agents such as remdesivir, lopinavir, and ritonavir are agents under investigation in these trials. Conclusions: In this study, we introduced the promising therapeutic options that many researchers and clinicians are interested in, and to address the hidden issues behind clinical trials in this COVID-19 pandemic.