COVID-19 Drug Discovery and Comparison Between Generative and Predictive Approach

The spread of the SARS-Cov-2 virus in the human race has caused 6.56M deaths worldwide as of Oct 2022 and has brought the economy to a standstill. It has also introduced several challenges worldwide. So, the need of finding a drug that would be able to dilute the symptoms of COVID-19 in the patients. The current methods for drug discovery via conventional methods are a tedious and time-consuming process. So here, the deep learning algorithms come to our rescue. Scientists and Doctors are diligently studying and analyzing the genome sequence of the virus and trying to understand the interaction between the coronavirus protease and a covalent inhibitor. Taking advantage of one such research work published by Shanghai Tech University, the research attempts in making research which is based on an approach to inhibit the protease of SARS-Cov-2(Or any virus) by a covalent inhibitor(also called Ligand). The research was done for some similar viruses to SARS-Cov-2, like SARS, MERS, and HIV. Protein target GI73745819 - SARS Protease, Protein target GI75593047 - HIV pol polyprotein, NS3 - Hep3 protease, and 3CL-Pro - Mers Protease. Bioactivities measured in these papers by medicinal chemists and biochemists are tracked by The National Center for Biotechnology Information (NCBI) which can be accessed by everyone. The goal of this research is to make efforts toward proposing a potentially highly active molecule against a target protein of the 2019 Novel Coronavirus. This research features training of the model in such a way that it predicts the binding power of the drug toward COVID-19 protease. Then compared and reported the inhibition score of ligand and protease to find out one of the best inhibitors. © 2022 IEEE.

Has increased telehealth access during COVID-19 led to over-utilization of primary care?

Telehealth use for primary care has skyrocketed since the onset of the COVID-19 pandemic. Enthusiasts have praised this new medium of delivery as a way to increase access to care while potentially reducing spending. Over two years into the pandemic, the question of whether telehealth will lead to an increase in primary care utilization and spending has been met with contradictory answers. Some evidence suggests that telehealth may be used as an addition to in-person visits. Others like Dixit et al. have found that telehealth can actually substitute for in-person care rather than contribute to overutilization. As telehealth continues to evolve, outcomes, utilization, and quality of care should be closely monitored.

Intelligent risk prediction in public health using wearable device data.

The importance of infection risk prediction as a key public health measure has only been underscored by the COVID-19 pandemic. In a recent study, researchers use machine learning to develop an algorithm that predicts the risk of COVID-19 infection, by combining biometric data from wearable devices like Fitbit, with electronic symptom surveys. In doing so, they aim to increase the efficiency of test allocation when tracking disease spread in resource-limited settings. But the implications of technology that applies data from wearables stretch far beyond infection monitoring into healthcare delivery and research. The adoption and implementation of this type of technology will depend on regulation, impact on patient outcomes, and cost savings.