Multisystem inflammatory syndrome in a young adult successfully treated with plasmapheresis, immunoglobulins, and corticosteroids: a case report.

A novel condition named multisystem inflammatory syndrome has raised the alarm worldwide and is leading to severe illness and long-term effects in the post-COVID era. This condition includes infection with fever, abdominal symptoms, acute cardiac injury, and shock. It has similarities with severe forms of Kawasaki disease (KD). In this study, we present a case of a 20-year-old male patient with multisystem inflammatory syndrome associated with COVID-19 infection who was successfully treated with plasmapheresis, immunoglobulins, and steroids for 4 h/day without heparinization or ultrafiltration. Plasmapheresis represents a therapeutic option for KD in patients with all other therapeutic strategies that have failed. However, there is no evidence from controlled clinical trials confirming this option. In our case, plasmapheresis was beneficial in stabilizing and improving the patient's clinical condition. Given the pathophysiological and therapeutic similarities between KD and multisystem inflammatory syndrome, it could be considered a therapeutic option.

An approachable, flexible and practical machine learning workshop for biologists.

SUMMARY: The increasing prevalence and importance of machine learning in biological research have created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible or inappropriate for biologists because they require significant computational and mathematical knowledge, demand an unrealistic time-investment or teach skills primarily for computational researchers. We created the Machine Learning for Biologists (ML4Bio) workshop, a short, intensive workshop that empowers biological researchers to comprehend machine learning applications and pursue machine learning collaborations in their own research. The ML4Bio workshop focuses on classification and was designed around three principles: (i) emphasizing preparedness over fluency or expertise, (ii) necessitating minimal coding and mathematical background and (iii) requiring low time investment. It incorporates active learning methods and custom open-source software that allows participants to explore machine learning workflows. After multiple sessions to improve workshop design, we performed a study on three workshop sessions. Despite some confusion around identifying subtle methodological flaws in machine learning workflows, participants generally reported that the workshop met their goals, provided them with valuable skills and knowledge and greatly increased their beliefs that they could engage in research that uses machine learning. ML4Bio is an educational tool for biological researchers, and its creation and evaluation provide valuable insight into tailoring educational resources for active researchers in different domains. AVAILABILITY AND IMPLEMENTATION: Workshop materials are available at https://github.com/carpentries-incubator/ml4bio-workshop and the ml4bio software is available at https://github.com/gitter-lab/ml4bio. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.