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Pulmonary/Respiratory Diseases


CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design

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The emergence and outbreak of SARS-CoV-2, the causative agent of COVID-19, has rapidly become a global concern and has highlighted the need for fast, sensitive, and specific tools to surveil circulating viruses. Here we provide assay designs and experimental resources, for use with CRISPR-based nucleic acid detection, that could be valuable for ongoing surveillance. We provide assay designs for detection of 67 viral species and subspecies, including: SARS-CoV-2, phylogenetically-related viruses, and viruses with similar clinical presentation. The designs are outputs of algorithms that we are developing for rapidly designing nucleic acid detection assays that are comprehensive across genomic diversity and predicted to be highly sensitive and specific. Of our design set, we experimentally screened 4 SARS-CoV-2 designs with a CRISPR-Cas13 detection system and then extensively tested the highest-performing SARS-CoV-2 assay.


Machine Learning Network Determines Severity of Lung Cancer

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A machine learning tool analyzed CT scans to offer information about lung cancer severity and guide treatment options.


Best of arXiv.org for AI, Machine Learning, and Deep Learning – May 2020 - insideBIGDATA

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Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.


Machine learning model finds SARS-CoV-2 growing more infectious

#artificialintelligence

A novel machine learning model developed by researchers at Michigan State University suggests that mutations to the SARS-CoV-2 genome have made the virus more infectious. As infections continue to surge across the United States, the concern is that any slight mutation could have drastic consequences. The model, developed by lead research Guo-Wei Wei, professor in the Department of Biochemistry and Molecular Biology, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples. The researchers analyzed mutations to the spike protein -- a protein primarily responsible for facilitating infection -- and found that five of the six known virus subtypes are now more infectious. As with any virus, many mutations are ultimately benign, posing little to no risk to infected patients.


Serology assays to manage COVID-19

Science

In late 2019, China reported a cluster of atypical pneumonia cases of unknown etiology in Wuhan. The causative agent was identified as a new betacoronavirus, called severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), that causes coronavirus disease 2019 (COVID-19) (1). The virus rapidly spread across the globe and caused a pandemic. Sequencing of the viral genome allowed for the development of nucleic acid–based tests that have since been widely used for the diagnosis of acute (current) SARS-CoV-2 infections (2). Development of serological assays, which measure the antibody responses induced by SARS-CoV-2 infection (past but not current infections), took longer.



Contributing a New Large Dataset for SARS-CoV-2 Identification via CT Scan

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You can find the dataset on Kaggle. For the detailed paper, please visit medRxiv. In December 2019, an outbreak coronavirus (SARS-CoV-2) infection began in Wuhan, the capital of central China's Hubei province. On Jan 30, 2020, the World Health Organization (WHO) declared a global health emergency. By May 9, 2020, over 4 million officially confirmed cases were reported in practically every corner of the Earth, with 275,976 officially reported deaths documented.


Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period

Science

Four months into the severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) outbreak, we still do not know enough about postrecovery immune protection and environmental and seasonal influences on transmission to predict transmission dynamics accurately. However, we do know that humans are seasonally afflicted by other, less severe coronaviruses. Kissler et al. used existing data to build a deterministic model of multiyear interactions between existing coronaviruses, with a focus on the United States, and used this to project the potential epidemic dynamics and pressures on critical care capacity over the next 5 years. The long-term dynamics of SARS-CoV-2 strongly depends on immune responses and immune cross-reactions between the coronaviruses, as well as the timing of introduction of the new virus into a population. One scenario is that a resurgence in SARS-CoV-2 could occur as far into the future as 2025. It is urgent to understand the future of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) transmission. We used estimates of seasonality, immunity, and cross-immunity for human coronavirus OC43 (HCoV-OC43) and HCoV-HKU1 using time-series data from the United States to inform a model of SARS-CoV-2 transmission. We projected that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after the initial, most severe pandemic wave. Absent other interventions, a key metric for the success of social distancing is whether critical care capacities are exceeded. To avoid this, prolonged or intermittent social distancing may be necessary into 2022. Additional interventions, including expanded critical care capacity and an effective therapeutic, would improve the success of intermittent distancing and hasten the acquisition of herd immunity.


Q&A: research into sound-collecting app to aid respiratory disease diagnosis

AIHub

A recording of a cough, the noise of a person's breathing or even the sound of their voice could be used to help diagnose patients with Covid-19 in the future, according to Professor Cecilia Mascolo, co-director of the centre for mobile, wearable systems and augmented intelligence at the University of Cambridge, UK. Prof. Mascolo has developed a sound-collecting app to help train machine learning algorithms to detect the tell-tale sounds of coronavirus infection. Created as part of a project called EAR, she hopes it might eventually lead to new ways of diagnosing respiratory diseases and help in the global fight against coronavirus. The human body makes noises all of the time. Our heart, lungs and digestive system all make noises and they can tell us a lot.


Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

Science

New analyses indicate that severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) is more infectious and less virulent than the earlier SARS-CoV-1, which emerged in China in 2002. Unfortunately, the current virus has greater epidemic potential because it is difficult to trace mild or presymptomatic infections. As no treatment is currently available, the only tools that we can currently deploy to stop the epidemic are contact tracing, social distancing, and quarantine, all of which are slow to implement. However imperfect the data, the current global emergency requires more timely interventions. Ferretti et al. explored the feasibility of protecting the population (that is, achieving transmission below the basic reproduction number) using isolation coupled with classical contact tracing by questionnaires versus algorithmic instantaneous contact tracing assisted by a mobile phone application. For prevention, the crucial information is understanding the relative contributions of different routes of transmission. A phone app could show how finite resources must be divided between different intervention strategies for the most effective control. Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), has clear potential for a long-lasting global pandemic, high fatality rates, and incapacitated health systems. Until vaccines are widely available, the only available infection prevention approaches are case isolation, contact tracing and quarantine, physical distancing, decontamination, and hygiene measures. To implement the right measures at the right time, it is of crucial importance to understand the routes and timings of transmission. We used key parameters of epidemic spread to estimate the contribution of different transmission routes with a renewal equation formulation, and analytically determined the speed and scale for effective identification and contact tracing required to stop the epidemic. We developed a mathematical model for infectiousness to estimate the basic reproductive number R0 and to quantify the contribution of different transmission routes.