The push to infuse mental health education across advanced nursing curricula.

Research indicates that knowledge gaps and unfavorable attitudes among primary care advanced practice registered nurses (APRNs) are linked to stigma surrounding psychiatric care, affecting the management of patients experiencing mental illness. Despite standards of practice and educational guidelines set forth by professional nursing organizations to increase quality of care, challenges exist when delivering care to patients with mental health disorders. Lack of integration of mental health education throughout graduate nursing courses contributes to an underestimation of its significance and applicability within advanced practice nursing in primary care.

Training Community Health Workers to Respond to Public Health Demands.

Background: Community health workers (CHWs) connect individuals to community resources and build individual competence in an effort to improve overall community/public health. There is a need for more research on how community health nurse (CHN)-led training programs are needed to help train and support CHWs.

Purpose: The purpose was to describe the development and evaluation of a series of CHN-led CHW trainings on CHW role, boundaries, and motivational interviewing; diabetes; mental health and long COVID; sexually transmitted infections; and lead poisoning prevention and treatment.

Using Full Dive Virtual Reality to Operationalize Warfighter Resilience: From Proof of Concept and Usability of Hardware and Software to Upcoming Integrated Psychological Skills Training.

Introduction: Modern warfare operations are volatile, highly complex environments, placing immense physiological, psychological, and cognitive demands on the warfighter. To maximize cognitive performance and warfighter resilience and readiness, training must address psychological stress to enhance performance. Resilience in the face of adversity is fundamentally rooted in an individual's psychophysiological stress response and optimized through decreased susceptibility to the negative impact of trauma exposure.

Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.

Ground-breaking progress has been made in structure prediction of biomolecular assemblies, including the recent breakthrough of AlphaFold 3. However, it remains challenging for AlphaFold 3 and other state-of-the-art deep learning-based methods to accurately predict protein-RNA complex structures, in part due to the limited availability of evolutionary and structural information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a new deep-learning framework for protein-RNA complex structure prediction with only single-sequence input.

The landscape of RNA 3D structure modeling with transformer networks.

Transformers are a powerful subclass of neural networks catalyzing the development of a growing number of computational methods for RNA structure modeling. Here, we conduct an objective and empirical study of the predictive modeling accuracy of the emerging transformer-based methods for RNA structure prediction. Our study reveals multi-faceted complementarity between the methods and underscores some key aspects that affect the prediction accuracy.