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NPJ Digit Med ; 4(1): 29, 2021 Feb 18.
Article in English | MEDLINE | ID: covidwho-1091450


Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.

iScience ; 23(11): 101697, 2020 Nov 20.
Article in English | MEDLINE | ID: covidwho-1023610


The beginning of the 21st century has been marked by three distinct waves of zoonotic coronavirus outbreaks into the human population. The COVID-19 (coronavirus disease 2019) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and emerged as a global threat endangering the livelihoods of millions worldwide. Currently, and despite collaborative efforts, diverse therapeutic strategies from ongoing clinical trials are still debated. To address the need for such an immediate call of action, we leveraged the largest dataset of drug-induced transcriptomic perturbations, public SARS-CoV-2 transcriptomic datasets, and expression profiles from normal lung transcriptomes. Most importantly, our unbiased systems biology approach prioritized more than 50 repurposable drug candidates (e.g., corticosteroids, Janus kinase and Bruton kinase inhibitors). Further clinical investigation of these FDA-approved candidates as monotherapy or in combination with an antiviral regimen (e.g., remdesivir) could lead to promising outcomes in patients with COVID-19.