MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

Background: The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages.

Results: We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads.

Utilization of Antibiotics for Hospitalized Patients with Severe Coronavirus Disease 2019 in Al-Madinah Al-Munawara, Saudi Arabia: A Retrospective Study.

Background: Most patients admitted to intensive care units (ICUs) with severe Corona Virus Disease 2019 (COVID-19) pneumonia receive antibacterial antibiotics with little evidence of bacterial infections.

Objective: This study was designed to review the profiles of patients with severe COVID-19 pneumonia requiring intensive care, the rate of bacterial coinfection, the antibiotics used, and their relation to patient outcomes (death or recovery).

Methods: This was a retrospective study that reviewed the medical records of all patients with confirmed COVID-19 (n = 120) severe pneumonia admitted directly from the emergency room to the intensive care unit, at a public hospital during the period from May 2020 to April 2021.