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.

Prognostic impact of high-intensity lipid-lowering therapy under-prescription after acute myocardial infarction in women.

Aims: Women are less likely to receive lipid-lowering therapy (LLT) after acute myocardial infarction (AMI). We analysed whether this under-prescription currently persists and has an impact on long-term outcomes.

Methods And Results: The FAST-MI programme consists of nationwide registries including all patients admitted for AMI ≤ 48 h from onset over a 1 month period in 2005, 2010, and 2015, with long-term follow-up.

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.

Extruded alginate tubes with myogenic potential.

Background: There are currently no proven methods to reverse muscle loss in humans, which is caused by trauma (e.g., volumetric muscle loss, VML), genetic neuromuscular diseases (e.

Understanding current antenatal Hepatitis C testing and care in maternity services in England.

Objectives: Universal opt-out antenatal screening for Hepatitis C virus (HCV) is not currently recommened and it is recommended that maternity services offer risk-based testing. We aimed to investigate antenatal HCV testing and adherence to testing guidance.

Methods: A cross-sectional survey was circulated to maternity service providers between November-December 2020 which included testing policy, training for healthcare staff, and management of women found to be HCV positive.

Depth variation in benthic community response to repeated marine heatwaves on remote Central Indian Ocean reefs.

Coral reefs are increasingly impacted by climate-induced warming events. However, there is limited empirical evidence on the variation in the response of shallow coral reef communities to thermal stress across depths. Here, we assess depth-dependent changes in coral reef benthic communities following successive marine heatwaves from 2015 to 2017 across a 5-25 m depth gradient in the remote Chagos Archipelago, Central Indian Ocean.

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks.

Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here, we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions.

Autobiographical Cerebral Network Activation in Older Adults Before and After Reminiscence Therapy: A Preliminary Report.

Introduction: Reminiscence therapy (RT), which engages individuals to evoke positive memories, has been shown to be effective in improving psychological well-being in older adults suffering from PTSD, depression, and anxiety. However, its impact on brain function has yet to be determined. This paper presents functional magnetic resonance imaging (fMRI) data to describe changes in autobiographical memory networks (AMN) in community-dwelling older adults.

Comparative Analysis of a New Automatic System and Four Existing Techniques for Autologous Fat Grafting.

Background: Autologous fat grafting is increasingly used worldwide and is a very attractive technique in many ways. However, treatment duration and postinjection tissue resorption remain problematic elements, which are largely related to the preparation method used. Moreover, few scientific studies objectively compare different fat preparation methods.

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks.

Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions.

E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.

Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures.

Protection against Severe Illness versus Immunity-Redefining Vaccine Effectiveness in the Aftermath of COVID-19.

Anti-SARS-CoV-2 vaccines have played a pivotal role in reducing the risk of developing severe illness from COVID-19, thus helping end the COVID-19 global public health emergency after more than three years. Intriguingly, as SARS-CoV-2 variants emerged, individuals who were fully vaccinated did get infected in high numbers, and viral loads in vaccinated individuals were as high as those in the unvaccinated. However, even with high viral loads, vaccinated individuals were significantly less likely to develop severe illness; this begs the question as to whether the main effect of anti-SARS-CoV-2 vaccines is to confer protection against severe illness or immunity against infection.

A Mental Health Pandemic? Assessing the Impact of COVID-19 on Young People's Mental Health.

Background: Research indicates that young people have been a particularly vulnerable group when it comes to negative mental health outcomes following COVID-19, with some authors warning of a 'mental health pandemic'.

Materials And Method: Using a survey approach, this study explored the effects of lockdowns on the mental health of 1995 16-year-olds in Northern Ireland. Respondents completed the 12-item version of the General Health Questionnaire (GHQ-12) along with closed- and open-ended questions about COVID-19.

Seasonal development of a tidal mixing front drives shifts in community structure and diversity of bacterioplankton.

Bacterioplankton underpin biogeochemical cycles and an improved understanding of the patterns and drivers of variability in their distribution is needed to determine their wider functioning and importance. Sharp environmental gradients and dispersal barriers associated with ocean fronts are emerging as key determinants of bacterioplankton biodiversity patterns. We examined how the development of the Celtic Sea Front (CF), a tidal mixing front on the Northwest European Shelf affects bacterioplankton communities.

The transformative power of transformers in protein structure prediction.

Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods built on transformers for 69 protein targets from the recently concluded 15th Critical Assessment of Structure Prediction (CASP15) challenge. Our study shows the power of transformers in protein structure modeling and highlights future areas of improvement.

Clinical characteristic of a Haitian stroke cohort and a scoping review of the literature of stroke among the Haitian population.

Background And Aim: There are significant disparities in stroke care and outcomes between low- and middle-income countries compared to high-income countries. Haiti, a lower-middle-income country, suffers from a lack of resources for acute stroke management. This study is the first to report the epidemiological profile of the Haitian population presenting with stroke symptoms at the largest academic hospital in the nation.

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries.

Motivation: Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations.

Results: Here, we present PIQLE, a deep graph learning method for protein-protein interface quality estimation.

Sex differences in hardiness, coping, and health in new West Point cadets.

The U.S. Military Academy at West Point places young men and women in a highly demanding world of extreme mental and physical challenges.

Contact-Assisted Threading in Low-Homology Protein Modeling.

The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling.

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries.

Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here we present PIQLE, a deep graph learning method for protein-protein interface quality estimation.

Cognitive interventions for memory and psychological well-being in aging and dementias.

The human lifespan has expanded drastically in the last few centuries, due to improvements in sanitation, medicine, and nutrition, but with this increase in longevity comes higher rates of cognitive pathology such as mild cognitive impairment (MCI) and dementia; the latter is estimated to reach more than 75 million people by 2030. Pathology risk is related to measures of executive function, lifestyle factors (e.g.