Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

Objectives: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups.

Materials And Methods: We collected and analyzed qualitative data from focus groups with varied end users, including: dialysis support providers (clinical providers and additional dialysis support providers such as dialysis clinic staff and social workers); patients; patients' caregivers (n = 52).

Supporting students from underrepresented minority backgrounds in graduate school: A mixed-methods formative study to inform post-baccalaureate design.

The US biomedical research workforce suffers from systemic barriers causing insufficient diversity and perpetuating inequity. To inform programming enhancing graduate program access, we implemented a formative mixed-method study to identify needed supports for program applications and graduate program success. Overall, results indicate value in added supports for understanding application needs, network development, critical thinking, time management, and reading academic/scientific literature.

Preclinical Evaluation of Sigma 1 Receptor Antagonists as a Novel Treatment for Painful Diabetic Neuropathy.

The global prevalence of diabetes is steadily rising, with an estimated 537 million adults affected by diabetes in 2021, projected to reach 783 million by 2045. A severe consequence of diabetes is the development of painful diabetic neuropathy (PDN), afflicting approximately one in every three diabetic patients and significantly compromising their quality of life. Current pharmacotherapies for PDN provide inadequate pain relief for many patients, underscoring the need for novel treatments that are both safe and effective.

Homophily and social mixing in a small community: Implications for infectious disease transmission.

Community mixing patterns by sociodemographic traits can inform the risk of epidemic spread among groups, and the balance of in- and out-group mixing affects epidemic potential. Understanding mixing patterns can provide insight about potential transmission pathways throughout a community. We used a snowball sampling design to enroll people recently diagnosed with SARS-CoV-2 in an ethnically and racially diverse county and asked them to describe their close contacts and recruit some contacts to enroll in the study.

Machine learning models to predict ligand binding affinity for the orexin 1 receptor.

The orexin 1 receptor (OX1R) is a G-protein coupled receptor that regulates a variety of physiological processes through interactions with the neuropeptides orexin A and B. Selective OX1R antagonists exhibit therapeutic effects in preclinical models of several behavioral disorders, including drug seeking and overeating. However, currently there are no selective OX1R antagonists approved for clinical use, fueling demand for novel compounds that act at this target.