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Abstract Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems. To greatly enhance the speed and utility of our model, we applied an artificial intelligence method to segment and classify regions on CT imaging, from which interpretable data could be directly fed into the predictive machine learning model for overall recovery. Across all organ systems we achieved validation set area under the receiver operator characteristic curve (AUC) values for organ-specific recovery ranging from 0.80 to 0.89, and significant overall recovery prediction in Kaplan-Meier analyses. This demonstrates that the synergistic use of an artificial intelligence (AI) framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.

Coronavirus Disease 2019 (COVID-19) is caused by the novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) - the culprit of an ongoing pandemic responsible for the loss of over 3 million lives worldwide within a year and a half. While the majority of SARS-CoV-2 infected people develop no or mild symptoms, some become severely ill and may die from COVID-19-related complications. In this review, we compile and comment on a number of biomarkers that have been identified and are expected to enhance the detection, protection and treatment of individuals at high risk of developing severe illnesses, as well as enable the monitoring of COVID-19 prognosis and responsiveness to therapeutic interventions. Consistent with the emerging notion that the majority of COVID-19 deaths occur in older and frail individuals, we researched the scientific literature and report the identification of a subset of COVID-19 biomarkers indicative of increased vulnerability to developing severe COVID-19 in older and frail patients. Mechanistically, increased frailty results from reduced disease tolerance, a phenomenon aggravated by ageing and comorbidities. While biomarkers of ageing and frailty may predict COVID-19 severity, biomarkers of disease tolerance may predict resistance to COVID-19 with socio-economic factors such as access to adequate health care remaining as major non-biomolecular influencers of COVID-19 outcomes. Graphical Abstract Figure: Biomarkers of ageing and frailty may predict COVID-19 severity as both conditions are associated with reduced disease tolerance - the host’s defense mechanisms to limit tissue damage or reduce immunopathology induced by the infection with a pathogen. While these biomolecular markers inform about the baseline ground for exacerbated viral infection, inflammaging and pre-existing comorbidities, which are common at advanced ages, as well as socio-economic conditions that affect people in underdeveloped nations and underserved communities of developed nations appear to be strong influencers of COVID-19 trajectory - particularly in older and frail individuals.ga1

High-throughput detection strategies for antibodies against SARS-CoV-2 in patients recovering from COVID-19, or in vaccinated individuals, are urgently required during this ongoing pandemic. Serological assays are the most widely used method to measure antibody responses in patients. However, most of the current methods lack the speed, stability, sensitivity, and specificity to be selected as a test for worldwide serosurveys. Here, we demonstrate a novel NanoBiT-based serological assay for fast and sensitive detection of SARS-CoV-2 RBD-specific antibodies in sera of COVID-19 patients. This assay can be done in high-throughput manner at 384 samples per hour and only requires a minimum of 5 μL of serum or 10 ng of antibody. The stability of our NanoBiT reporter in various temperatures (4–42 °C) and pH (4–12) settings suggests the assay will be able to withstand imperfect shipping and handling conditions for worldwide seroepidemiologic surveillance in the post-vaccination period of the pandemic. Our newly developed rapid assay is highly accessible and may facilitate a more cost-effective solution for seroconversion screening as vaccination efforts progress.

The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), was declared on March 11, 2020 by the World Health Organization. As of the 31st of May, 2020, there have been more than 6 million COVID-19 cases diagnosed worldwide and over 370,000 deaths, according to Johns Hopkins. Thousands of SARS-CoV-2 strains have been sequenced to date, providing a valuable opportunity to investigate the evolution of the virus on a global scale. We performed a phylogenetic analysis of over 1,225 SARS-CoV-2 genomes spanning from late December 2019 to mid-March 2020. We identified a missense mutation, D614G, in the spike protein of SARS-CoV-2, which has emerged as a predominant clade in Europe (954 of 1,449 (66%) sequences) and is spreading worldwide (1,237 of 2,795 (44%) sequences). Molecular dating analysis estimated the emergence of this clade around mid-to-late January (10 - 25 January) 2020. We also applied structural bioinformatics to assess D614G potential impact on the virulence and epidemiology of SARS-CoV-2. In silico analyses on the spike protein structure suggests that the mutation is most likely neutral to protein function as it relates to its interaction with the human ACE2 receptor. The lack of clinical metadata available prevented our investigation of association between viral clade and disease severity phenotype. Future work that can leverage clinical outcome data with both viral and human genomic diversity is needed to monitor the pandemic.

AbstractThe COVID-19 pandemic has impacted the mental health, physical activity, and sedentary behavior of citizens worldwide. Using an online survey with 1669 respondents, we sought to understand why and how by querying about perceived barriers and motivators to physical activity that changed because of the pandemic, and how those changes impacted mental health. Consistent with prior reports, our respondents were less physically active (aerobic activity, −11%, p <0.05; strength-based activity, −30%, p<0.01) and more sedentary (+11%, p<0.01) during the pandemic as compared to 6-months before. The pandemic also increased psychological stress (+22%, p <0.01) and brought on moderate symptoms of anxiety and depression. Respondents’ whose mental health deteriorated the most were also the ones who were least active (depression r = −.21, p<0.01; anxiety r = −.12, p<0.01). The majority of respondents were unmotivated to exercise because they were too anxious (+8%, p <0.01), lacked social support (+6%, p =<0.01), or had limited access to equipment (+23%, p <0.01) or space (+41%, p <0.01). The respondents who were able to stay active reported feeling less motivated by physical health outcomes such as weight loss (−7%, p<0.01) or strength (−14%, p<0.01) and instead more motivated by mental health outcomes such as anxiety relief (+14%, p <0.01). Coupled with previous work demonstrating a direct relationship between mental health and physical activity, these results highlight the potential protective effect of physical activity on mental health and point to the need for psychological support to overcome perceived barriers so that people can continue to be physically active during stressful times like the pandemic.

Intending to shield front-liners who are currently exposed to COVID-19, and because of the lack of proper powered air-purifying respirator, this study shows the design and development of an open-source ergonomic respirator with a washable filter. This device has an estimated working time of 12 h, and the tests' airflow always showed a value over 4.5 cubic feet per minute, a higher value than the national institute for occupational safety and health specification for full-face closed respirators. The proposal relies on 3D printing technology for all the custom-design parts and usages easy-to-access components for the rest of the material. The mask for the APRPAPR in the article has a defogging feature, 180 degrees of viewing angle, an ergonomic profile, and no obstruction on the mouth to show the user's full face. This respirator has an estimated cost of 318 USD, approximately one-third of the market's price of well-known brands. Graphical abstract

Novel Coronavirus (COVID-19) has drastically overwhelmed more than 200 countries affecting millions and claiming almost 2 million lives, since its emergence in late 2019. This highly contagious disease can easily spread, and if not controlled in a timely fashion, can rapidly incapacitate healthcare systems. The current standard diagnosis method, the Reverse Transcription Polymerase Chain Reaction (RT- PCR), is time consuming, and subject to low sensitivity. Chest Radiograph (CXR), the first imaging modality to be used, is readily available and gives immediate results. However, it has notoriously lower sensitivity than Computed Tomography (CT), which can be used efficiently to complement other diagnostic methods. This paper introduces a new COVID-19 CT scan dataset, referred to as COVID-CT-MD, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP). COVID-CT-MD dataset, which is accompanied with lobe-level, slice-level and patient-level labels, has the potential to facilitate the COVID-19 research, in particular COVID-CT-MD can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions. Measurement(s) Low Dose Computed Tomography of the Chest • viral infectious disease Technology Type(s) digital curation • image processing technique Factor Type(s) sex • gender • age group • weight • clinical characteristics • covid-19 RT-PCR result • follow-up data Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13583015

Alcohol-based hand rubs (ABHRs) formulated with technical-grade ethanol were temporarily permitted in Canada and the U.S beginning April 2020 to meet the current demand due to COVID-19. ABHRs formulated with technical-grade ethanol are low risk for general use. In this review, we discuss the toxicity of common contaminants found in technical-grade ethanol, as well as contaminants that may have been introduced into the products during formulation and packaging of ABHRs. Although primary route of exposure is via dermal absorption and inhalation, there have been reported elevated concerns regarding to ingestion of ABHRs. Overall, the highest risks were associated with methanol (for its toxicity), ethyl acetate (skin defattening), and acetaldehyde (carcinogenic and teratogenic). For these reasons Health Canada and the United States Food and Drug Administration have issued recalls on products containing some of these contaminants. More vigilant policing by regulatory agencies and general product users are required to ensure compliance, safety, and efficacy of these new products, as demand continue to rise during this unprecedented pandemic. Highlights • Alcohol-based hand rubs formulated with technical-grade ethanol can increase exposure to alcoholic impurities. • Inexperienced manufacturers may introduce additional contaminants into the product. • More vigilant policing should be employed to ensure compliancy, safety and efficacy. Graphical abstract

An SL1L2I1I2A1A2R epidemic model is formulated that describes the spread of an epidemic in a population. The model incorporates an Erlang distribution of times of sojourn in incubating, symptomatically and asymptomatically infectious compartments. Basic properties of the model are explored, with focus on properties important in the context of current COVID-19 pandemic.