Seroprevalence of SARS-CoV-2 IgG in blood donors in a teaching institute from Western part of Maharashtra.

Introduction: COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 that has appeared as a global pandemic in recent times. Currently, the transmission rate has slowed down significantly, but the definite pathological reason behind this is still unknown. Therefore, the prevalence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody must be studied to establish the relation between the rate of transmission and antibody presence.

Development of a machine learning model to predict risk of development of COVID-19-associated mucormycosis.

The study aimed to identify quantitative parameters that increase the risk of rhino-orbito-cerebral mucormycosis, and subsequently developed a machine learning model that can anticipate susceptibility to developing this condition. Clinicopathological data from 124 patients were used to quantify their association with COVID-19-associated mucormycosis (CAM) and subsequently develop a machine learning model to predict its likelihood. Diabetes mellitus, noninvasive ventilation and hypertension were found to have statistically significant associations with radiologically confirmed CAM cases.

Case series of invasive lung infections by species and zygomycosis among post COVID-19 and post-transplant individuals.

Airborne pathogens like bring the lungs in the frontline for defense. Pulmonary diseases caused by species are broadly classified as aspergilloma, chronic necrotizing pulmonary aspergillosis, invasive pulmonary aspergillosis (IPA), and bronchopulmonary aspergillosis. Intensive care unit (ICU) admission is required for a large number of patients associated with IPA.