September 29, 2020 (Bioorganic Chemistry)

A perspective on potential target proteins of COVID-19: Comparison with SARS-CoV for designing new small molecules

Devendra Kumar, Gaurav Chauhan, Sourav Kalra et al.

https://doi.org/10.1016/j.bioorg.2020.104326

These chemists explored three major targets namely; SARS-CoV-2 spike (S) protein, RNA dependent RNA polymerase, and 3CL or Mpro Protease for the inhibition of SARS-CoV-2. These targets have attracted the attention of the medicinal chemists working on computer-aided drug design in developing new small molecules that might inhibit these targets for combating COVID-19 disease. We have observed that both the coronaviruses share around 80% similarity in their amino acid sequence. This study will help the medicinal chemists to understand the key amino acids essential for interactions at the active site of target proteins in SARS-CoV-2, based on their similarity with earlier reported viruses. In this review, we have also described the lead molecules under various clinical trials for their efficacy against COVID-19.

 

 

September 25, 2020 (Proceedings of the National Academy of Sciences)

Speech can produce jet-like transport relevant to asymptomatic spreading of virus

Manouk Abkarian, Simon Mendez, Nan Xue et al.

https://doi.org/10.1073/pnas.2012156117

Droplet emission occurs during the speech, but the lack of understanding prevents informed public health guidance for risk reduction and mitigation strategies. The authors analyze flows during breathing and speaking, including phonetic features, using numerical simulations and laboratory experiments. The authors document the spatiotemporal structure of the expelled airflow. Phonetic characteristics of sound like “P” lead to enhanced directed transport of jet-like flows. They highlight three distinct temporal scaling laws for the transport distance of exhaled material including 1) transport over a short distance (<0.5 m) in a fraction of a second, with large angular variations due to the complexity of speech; 2) a longer distance, ∼1 m,  and 3) a distance out to about 2 m. This work will highlight the role of ventilation, aerosol transport in disease transmission for humans and other animals and provide a better understanding of linguistic aerodynamics, i.e., aerophonetics.

 

 

September 18, 2020 (The Lancet Digital Health)

Artificial Intelligence in COVID-19 drug repurposing

Yadi Zhou, Fei Wang, Jian Tang et al.

https://doi.org/10.1016/S2589-7500(20)30192-8

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging disease such as COVID-19. This Review provides a strong rationale for using AI-based tools for drug repurposing medications for the disease. The authors introduce guidelines on how to use AI for accelerating drug repurposing or repositioning to expedite therapeutic development.

 

 

September 11, 2020 (Science)

Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures

Thomas Lecocq, Stephen P. Hicks, Koen Van Noten et al.

https://science.sciencemag.org/content/369/6509/1338.full

During months-long lock-downs in many countries, seismic noise of up to 50% was recorded. Human activities cause vibrations that propagate into the ground as high-frequency seismic waves. The 2020 seismic noise quiet period is the longest and most prominent global human-origin seismic noise reduction on record. The scientists found a strong correlation between seismic noise and independent measurements of human mobility; this suggests that seismology provides an absolute, real-time estimate of human activities.

 

 

September 2, 2020 (Nonlinear Dynamics)

Human mobility and COVID-19 initial dynamics

Stefano Maria Iacus, Carlos Santamaria, Francesco Sermi et al.

https://doi.org/10.1007/s11071-020-05854-6

Reliable and consistent method to measure the evolution of contagion at the international level is still missing. Europe took different mobility containment measures to curb the spread of COVID-19. The European Commission asked mobile network operators to share voluntarily anonymised and aggregated mobile data to improve the quality of modelling and forecasting for the pandemic. This study shows that mobility alone can explain up to 92% of the initial spread in France and Italy and that the spreading of the virus and human mobility are connected. The findings will support policymakers in formulating strategies for future outbreaks.

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September 2, 2020 (Journal of Biological Rhythms)

Accounting for Time: Circadian Rhythms in the Time of COVID-19

Shaon Sengupta, Thomas G. Brooks, Gregory R. Grant et al.

https://doi.org/10.1177%2F0748730420953335

It is known that various aspects of our physiology and response to pathogens are controlled by the strict biological clock. However, we still have limited knowledge of applying circadian biology in our clinical and research practices. They discuss how circadian biology may improve our diagnostic and therapeutic strategies using a focused review of the literature and original analyses of the UK Biobank data. The biological clock may be relevant to the pathophysiology and treatment of the COVID-19.

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September 2, 2020 (Biocybernetics and Biomedical Engineering)

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

Bejoy Abraham, Madhu S. Nair.

​https://doi.org/10.1016/j.bbe.2020.08.005

Pre-trained convolutional neural networks are widely used for computer-aided detection of diseases from smaller datasets. This paper investigates the effectiveness of multi-CNN, a combination of several pre-trained CNNs, for the automated detection of COVID-19 from X-ray images. The authors use a combination of features extracted from multi-CNN with correlation-based feature selection technique and Bayesnet classifier for the prediction of COVID-19. The method was tested using two public datasets and they claimed promising results on both the datasets. This study demonstrates the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19.

 

 

September 1, 2020 (The Lancet Digital Health)

Measuring mobility to monitor travel and physical distancing interventions: a common framework for mobile phone data analysis

Nishant Kishore, Matthew V. Kiang, Kenth Engo-Monsen et al.

https://doi.org/10.1016/S2589-7500(20)30193-X

 There is an increased interest in the use of mobility data from mobile phones to monitor physical distancing and model the spread of COVID-19. There is a need to standardise data formats. The authors study aggregation principles and procedures for different mobile phone data streams and describe a common syntax for research and policy. They also mention the issues of privacy and data protection.

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Sep 2020  (Physics of Fluids)

Visualizing droplet dispersal for face shields and masks with exhalation valves

Siddhartha Verma, Manhar Dhanak, John Frankenfield

https://doi.org/10.1063/5.0022968

This is a rigorous scientific research on the physics of droplet spread. There is an increasing trend of public substituting surgical masks with clear plastic face shields and with masks equipped with exhalation valves. However, there is a concern that widespread public use of these alternatives to regular masks could hinder the mitigation efforts. Verma el al use qualitative visualizations to examine the performance of face shields and exhalation valves in impeding the spread of aerosol-sized droplets. The visualizations indicate that although face shields block the initial forward motion of the jet, the expelled droplets can move around the visor with relative ease and spread out over a large area disturbance. Visualizations for a mask equipped with an exhalation port indicate that a large number of droplets pass through the exhale valve unfiltered, which significantly reduces its effectiveness as a means of source control. The findings provide strong evidence to support the use of high quality cloth or surgical masks that are of a plain design, instead of face shields and masks equipped with exhale valves. View several excellent visualisation of the droplet spread with the use of various masks here. https://aip.scitation.org/doi/figure/10.1063/5.0022968

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Sep 2020  (Chaos, Solitons & Fractals)

Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19

Yong Zhang, Xiangnan Yu, Hong Guang Sun

https://www.sciencedirect.com/science/article/pii/S0960077920303581

Fractional calculus comes into action. The authors introduce fractional model to characterize the pattern of COVID-19 death. They then use a time-dependent SEIR model for fitting and prediction. Several models are attempted, such as the bi- molecular reaction method to evaluate the success of COVID-19 mitigation.

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Sep 2020  (International Immunopharmacology)

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population

Abhirup Banerjee, Surajit Ray, Bart Vorselaars et al

https://www.sciencedirect.com/science/article/pii/S1567576920315770

The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The authors reported that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94–95%) and those not admitted to hospital or in the community (AUC = 80–86%). 

 

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Sep 2020 (Journal of Systems Architecture)

A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19 Pandemic

Abu Sufian, Anirudha Ghosh, Ali Safaa Sadiq

https://www.sciencedirect.com/science/article/pii/S1383762120301223

The authors present a systematic study of Deep Learning (DL), Deep Transfer Learning (DTL) and Edge Computing (EC) to mitigate COVID-19. They survey existing DL, DTL,  EC and Dataset to mitigate the pandemics with potentials and challenges. They also point out that a shortage of reliable datasets of an ongoing pandemic is a common problem.

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September 2020   (Chaos, Solitons & Fractals)

COVID-19 created chaos across the globe: Three novel quarantine epidemic models

Bimal Kumar Mishra, Ajit Kumar Keshri,Yerra Shankar Rao

https://www.sciencedirect.com/science/article/pii/S0960077920303271?via%3Dihub

The authors developed three quarantine models of this pandemic taking into account the compartments: susceptible population, immigrant population, home isolation population, infectious population, hospital quarantine population and recovered population. Home isolation and quarantine are the two pivot parameters. These are then critically analysed with extensive numerical simulations and examples.

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