Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning.

The "similarity of dissimilarities" is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a more detailed understanding of evolutionary processes, allowing for a deeper exploration of regions that influence biological functionality. For ML models, incorporating dissimilarity measures helps avoid misleading results caused by highly correlated or similar data, addressing confounding issues like the Doppelgänger Effect.

Burnout among medical residents: key drivers and practical mitigating strategies.

Burnout is a prevalent phenomenon in medicine, affecting >50% of physicians and up to 60% of medical residents. This has negative consequences for both doctors' mental health and job satisfaction as well as patient care quality. While numerous studies have explored the causes, psychological effects, and workplace solutions, we aim to practicalize the issue from the perspectives of residents by discussing three key drivers of burnout and offering actionable, multipronged strategies that may be able to tackle these root causes effectively.