Author Interviews / 22.05.2020 Interview with: Marco Piccininni Research Associate, CONVERGE Universitätsmedizin Institute of Public Health Berlin, Germany What is the background for this study? Response: The Lombardy region of northern Italy was severely hit by the covid-19 pandemic. However, despite the very high number of confirmed covid-19 deaths in this region, some local investigations suggested that there was a mismatch between the confirmed covid-19 death count and the increase in all-cause deaths. In our study, we decided to further investigate this aspect in the city of Nembro (province of Bergamo), which was one of the first cities to report covid-19 cases, and one of the cities most affected by the pandemic. (more…)
Author Interviews, COVID -19 Coronavirus, Pharmaceutical Companies / 02.04.2020 Interview with: Dr. Larry Schlesinger MD Professor, President and CEO Texas Biomed What is the background and mission of Texas Biomed? Response: Texas Biomedical Research Institute (Texas Biomed) is a not-for-profit, independent research institute with a strong history of pioneering, biomedical breakthroughs that have contributed to the world of science and human health for nearly 80 years. The Texas Biomed mission is to pioneer and share scientific breakthroughs that protect you, your families and our global community from the threat of infectious diseases. Texas Biomed is capitalizing on its strengths – outstanding collaborative scientists and unique assets and resources. Texas Biomed is home to the nation’s only privately-owned BSL4 facility, five fully outfitted BSL3 facilities with the latest technologies and the Southwest National Primate Research Center (SNPRC). The Institute focuses on a core understanding of the basic biology of infectious diseases, animal model development, and studies to move therapies and vaccines to human clinical trials. The Institute’s independent, nonprofit business model moves science from the bench to clinical trials faster and with less bureaucracy. (more…)
Author Interviews, Columbia, COVID -19 Coronavirus, NYU, Technology / 02.04.2020 Interview with: Professor Anasse Bari PhD Courant Institute of Mathematical Sciences, Computer Science Department, New York University, New York, and Megan Coffee MD PhD Division of Infectious Diseases and Immunology, Department of Medicine New York University, Department of Population and Family Health Mailman School of Public Health Columbia University, New York What is the background for this study? Coffee and Bari:  This work is led by NYU Grossman School of Medicine and NYU’s Courant Institute of Mathematical Sciences, in partnership with Wenzhou Central Hospital and Cangnan People's Hospital, both in Wenzhou, China. This is a multi-disciplinary team with backgrounds in clinical infectious disease as well as artificial intelligence (AI) and computer science. There is a critical need to better understand COVID-19. Doctors learn from collective and individual clinical experiences. Here, no clinician has years of experience. All are learning as they go, having to make important decisions about clinical management with stretched resources. The goal here is to augment clinical learning with machine learning. In particular, the goal is to allow clinicians to identify early who from the many infected will need close medical attention. Most patients will first develop mild symptoms, yet some 5-8 days later will develop critical illness. It is hard to know who these people are who will need to be admitted and may need to be intubated until they become ill. Knowing this earlier would allow more attention and resources to be spent on those patients with worse prognoses. If there were ever treatments in the future that could be used early in the course of illness, it would be important to identify who would most benefit We present in this study a first step in building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. It is at this point a proof of concept that it could be possible to identify future severity based on initial presentation in COVID-19. (more…)