Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo.

Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins.

Genomic Comparison of Reoccurring, Emerging, and Persistent (REP) Shiga Toxin-Producing O157:H7.

O157:H7 strains associated with several recent (2017-2020) multi-state outbreaks linked to leafy green vegetables have been characterized as "reoccurring, emerging, and persistent" (REP). Our recent unpublished work demonstrated that the REP strains had significantly enhanced potential for biofilm formation. In this study, comparative genomic analyses were conducted for a better understanding of the mechanisms behind the enhanced biofilm formation, and thereby potentially increased environmental fitness, by the REP strains.

Relationships of Psychosocial Factors to Diabetes Self-efficacy: A Cross-sectional, Correlational Study.

Background: Self-care in people with diabetes is poor, which could be influenced by positive and negative psychosocial factors. Self-efficacy is an important factor affecting self-care, and depressive symptoms and diabetes distress may directly and indirectly affect self-efficacy.

Objective: The aim of this study was to examine the relationships of depressive symptoms, diabetes distress, age, sex, self-compassion, resilience, self-esteem, and social support to diabetes self-efficacy and the mediating roles of diabetes distress and depressive symptoms in the relationships among people with diabetes.

Accelerated drug-resistant variant discovery with an enhanced, scalable mutagenic base editor platform.

Personalized cancer therapeutics bring directed treatment options to patients based on their tumor's genetic signature. Unfortunately, tumor genomes are remarkably adaptable, and acquired resistance through gene mutation frequently occurs. Identifying mutations that promote resistance within drug-treated patient populations can be cost, resource, and time intensive.