April 27, 2020 (Nature)

Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals

Yuan Liu, Zhi Ning, Ke Lan

https://www.nature.com/articles/s41586-020-2271-3

While the transmission of SARS-CoV-2 via human respiratory droplets and direct contact is clear, the potential for aerosol transmission is poorly understood. This study investigates the aerodynamic nature of SARS-CoV-2 by measuring viral RNA in aerosols in different areas of two Wuhan hospitals during the COVID-19 outbreak in February and March 2020.  The authors propose that the virus could be transmitted via aerosols. They show that room ventilation, open space, sanitization of protective apparel, and proper use and disinfection of toilet areas can effectively limit the concentration of SARS-CoV-2 RNA in aerosols.

 

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April 24, 2020 (PLOS Biology)

Leveraging open hardware to alleviate the burden of COVID-19 on global health systems

Andre Maia Chagas , Jennifer C. Molloy , Lucia L. Prieto-Godino  et al

https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000730

The authors summarise community-driven approaches based on Free and Open Source scientific and medical Hardware (FOSH) as well as personal protective equipment (PPE) currently being developed to support the global response for COVID-19 prevention, treatment and diagnosis. If you are interested to explore further, do read this paper.

 

 

April 20, 2020  (J Biomol Struct Dyn)

Novel 2019 Coronavirus Structure, Mechanism of Action, Antiviral Drug Promises and Rule Out Against Its Treatment

Subramanian Boopathi , Adolfo B Poma, Ponmalai Kolandaivel

https://www.tandfonline.com/doi/full/10.1080/07391102.2020.1758788

This review addresses novel coronavirus structure, mechanism of action, and trial test of antiviral drugs in the laboratories and patients with COVID-19. Computational simulation such as computer-aided drug design has been a very useful research tool. It has very good illustrations on the structures and mechanisms of action.

 

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Apr 17, 2020 (Eur Radiol Exp)

Deep Learning Detection and Quantification of Pneumothorax in Heterogeneous Routine Chest Computed Tomography

S Röhrich, T Schlegl, C Bardach et al

https://pubmed.ncbi.nlm.nih.gov/32303861/

The authors developed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data to facilitate the automated triage of urgent examinations and make decision for treatment support.

They used a deep residual UNet  to evaluate automated, volume-level pneumothorax grading (i.e., labelling a volume whether a pneumothorax was present or not), and pixel-level classification (i.e., segmentation and quantification of pneumothorax), on a retrospective series of routine chest CT data.

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April 13, 2020  (PNAS) 

Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts

https://doi.org/10.1073/pnas.1919176117

Nardus Mollentze and  Daniel G. Streicker

The authors report that variation in the frequency of zoonoses among animal orders can be explained without invoking special ecological or immunological relationships between hosts and viruses. They point to a need to reconsider current approaches aimed at finding and predicting novel zoonoses.

 

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April 12, 2020  (Infect Genet Evol)

Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS

Liang K

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141629/

This paper reveals the spread rules of the three pneumonia: COVID-19, SARS and MERS, and then compares them. Stats analysis shows that the growth rate of COVID-19 is about twice that of the SARS and MERS, and the COVID-19 doubling cycle is two to three days.

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April 10, 2020 

Modeling the COVID-19 pandemic - parameter identification and reliability of predictions

Hackl, K.  

https://t.co/fs1E2pLvT0

This paper tries to identify the parameters in an epidemic model, the so-called SI-model, via non-linear regression using data of the COVID-19 pandemic. They attempt to estimate the reliability of predictions. They validate this procedure using data from China and South Korea and then we apply to predict for Germany, Italy and the United States.

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April 9, 2020 (J Chem Inf Model)

A Community Letter Regarding Sharing Bimolecular Simulation Data for COVID-19

Rommie E. Amaro  and Adrian J. Mulholland

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00319

​This letter highlights the urgent need to share methods, models and results openly and quickly to test findings, ensure reproducibility, test significance  and accelerate discovery. Sharing of data for COVID-19 applications will help connect scientists across the global biomolecular simulation community and to  improve collaboration.

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April 8, 2020 (Int J Mol Sci)

Development of a Novel, Genome Subtraction-Derived, SARS-CoV-2-Specific COVID-19-nsp2 Real-Time RT-PCR Assay and Its Evaluation Using Clinical Specimens

Yip CC, Ho CC, Chan JF et al

https://www.mdpi.com/1422-0067/21/7/2574

The team developed a rapid, sensitive, SARS-CoV-2-specific real-time RT-PCR assay on COVID-19-nsp2. They tested on 96 SARS-CoV-2 and 104 non-SARS-CoV-2 coronavirus genomes and using their in-house program, GolayMetaMiner, four specific regions longer than 50 nucleotides in the SARS-CoV-2 genome were identified. Evaluation of the new assay using 59 clinical specimens from 14 confirmed cases showed 100% concordance with their previously developed COVID-19-RdRp/Hel reference assay.

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April 8, 2020 (PNAS)

Phylogenetic network analysis of SARS-CoV-2 genomes

Peter Forster, Lucy Forster, Colin Renfrew and Michael Forster

https://www.pnas.org/content/early/2020/04/07/2004999117

The authors have found three main variants in a phylogenetic network analysis of 160 complete human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) genomes. The network reliably traces routes of infections for documented coronavirus disease 2019 (COVID-19) cases, indicating that the phylogenetic networks can be successfully used to help trace undocumented COVID-19 infection sources of the disease worldwide.

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April 7, 2020  (Patterns)

COVID-19 Is a Data Science Issue

Sarah Callaghan

https://doi.org/10.1016/j.patter.2020.100022

This editorial highlights the important role of data science in this global publich health emergency. Data scientists should rise to the occasion and contribute to the solution. It has a very useful web resources.

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April 7, 2020 (Maturitas)

COVID-19: The forgotten priorities of the pandemic

Cristina Mesa Vieira, Oscar H. Franco, Carlos Gomez Restrepo, Thomas Abel

https://doi.org/10.1016/j.maturitas.2020.04.004

This article has been awarded the Editor’s choice for the June edition of Maturitas. In the paper, the authors describe some implications of social distancing that can be detrimental to people’s mental health, especially of those who do not have an extensive support network.

 

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April 3, 2020 (Asian J of Psychiatry)

Issues relevant to mental health promotion in frontline health care providers managing quarantined/isolated COVID19 patients

Ritin Mohindra, Ravaki R, Vikas Suri et al.

https://doi.org/10.1016/j.ajp.2020.102084

The authors conducted interviews with health care providers managing COVID-19 patients to find out the perceived motivations influencing morale. They identified three themes: positive Motivational factors (that need to be strengthened), Negatives, frustrations associated with patient care, and personal fears and annoyances experienced by doctors. They present their findings with a view to disseminate so that hospitals facing or preparing for COVID-19 can factor in these issues.

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April 2, 2020

Stochastic modeling and estimation of COVID-19 population dynamics

ttps://arxiv.org/abs/2004.00941

The authors describe a model of the development of the Covid-19 contamination of the population of a country or a region

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April 2, 2020

COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images

https://arxiv.org/abs/2003.09871

The authors are developing an open access COVID-Net  to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and appropriate treatment to be given. 

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