Work Participation Interventions for Individuals with Disabilities: An Evidence-Based Practice Project

This Evidence-Based Practice (EBP) project considered the following question: What are the characteristics of interventions, programs, and services that are effective in supporting work participation for individuals with disabilities and their employers

Evaluating Literacy Sensitive Client Education Materials for the SMMART Clinic

This master’s project was completed in collaboration with the St. Mary’s Medical and Rehabilitative Therapies (SMMART) Clinic, located on the campus of St. Catherine University in St. Paul, Minnesota. Through the completion of literature reviews, a needs assessment, and project activities, nine graduate occupational therapy students analyzed the needs of this clinic and aimed to improve client care. The SMMART clinic serves primarily Spanish-speaking clients who are low-income, uninsured, or underinsured. This population often faces obstacles in accessing primary health care and rehabilitation, including language and literacy-related barriers. Occupational therapy can play an important role in addressing these barriers and providing high quality care and education that is sensitive to clients’ literacy and language preferences

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery