When COVID-19 patients inundated St. Louis hospitals, respiratory therapists arriving for yet another grueling shift with a dwindling supply of ventilators would often glance at their assignments and cry, heading into the locker room to collect themselves.

Clinical research requires that data be mined for insights. Machine learning, which develops algorithms to find patterns, has difficulty doing this with data related to health records because this type of information is neither static nor regularly collected. A new study developed a transparent and reproducible machine learning tool to facilitate analysis of health information. The tool can be used in clinical forecasting, which can predict trends as well as outcomes in individual patients.

The rapid onset of the COVID-19 pandemic, which is caused by the SARS-CoV-2 virus, has meant that many laboratories around the world have quickly redirected research efforts to focus on COVID-19. Correspondingly, there has been an urgent need to establish the right research tools that will improve our understanding of SARS-CoV-2, and allow researchers to focus on critical areas of COVID-19 research, such as studies on new variants of SARS-CoV-2, examinations of vaccine efficacy and evaluation of antiviral drugs. While these tools can sometimes take years to develop, a new paper published in PLOS Biology by an international consortium, including researchers in Australia, Scotland and Estonia, addresses this need for COVID-19 research tools, and describes an openly available SARS-CoV-2 laboratory research toolkit. This coronavirus research toolkit is aimed at increasing the availability and accessibility of a variety of research materials, particularly for researchers who may be unaccustomed to coronavirus research.

It doesn’t take a scientist to understand that laughter feels good, while anger feels awful.

(HealthDay)—Helping your brain stay sharp with age may be as simple as changing up the food on your plate at dinnertime, a new study suggests.