The National Academy of Medicine (NAM) recently released the Future of Nursing 2020-2030 focused on charting a path to achieve health equity. This article focuses on the critical role of nursing education in this call for action. Most specifically, we provide an overview of the process of developing a diversity, equity, and civility council in a school of nursing to promote equity, inclusivity, and diversity grounded in social justice within the culture of the school for faculty, staff, and students. In addition, we describe the council's work to date highlighting goals and the council's plans for future work based on data-driven processes.
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Cell-Instructive Biomaterials with Native-Like Biochemical Complexity.
Annu Rev Biomed Eng
January 2025
1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA; email:
Biochemical signals in native tissue microenvironments instruct cell behavior during many biological processes ranging from developmental morphogenesis and tissue regeneration to tumor metastasis and disease progression. The detection and characterization of these signals using spatial and highly resolved quantitative methods have revealed their existence as matricellular proteins in the matrisome, some of which are bound to the extracellular matrix while others are freely diffusing. Including these biochemical signals in engineered biomaterials can impart enhanced functionality and native-like complexity, ultimately benefiting efforts to understand, model, and treat various diseases.
View Article and Find Full Text PDFEmerging treatments for sarcoma: from 2024 onward.
Expert Opin Emerg Drugs
January 2025
Department of Medical Oncology, Vall d'Hebron University Hospital, Barcelona, Spain.
Introduction: Sarcomas are a rare and diverse group of mesenchymal-origin solid tumors, constituting only 1% of adult malignancies and classified into soft tissue and bone sarcomas. For localized disease, surgery and radiotherapy remain the cornerstone treatments. However, systemic options for advanced stages are limited, with an overall survival of approximately 20 months.
View Article and Find Full Text PDFCross-Platform Ecological Momentary Assessment App (JTrack-EMA+): Development and Usability Study.
J Med Internet Res
January 2025
Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Jülich, Germany.
Background: Traditional in-clinic methods of collecting self-reported information are costly, time-consuming, subjective, and often limited in the quality and quantity of observation. However, smartphone-based ecological momentary assessments (EMAs) provide complementary information to in-clinic visits by collecting real-time, frequent, and longitudinal data that are ecologically valid. While these methods are promising, they are often prone to various technical obstacles.
View Article and Find Full Text PDFValue Propositions for Digital Shared Medication Plans to Boost Patient-Health Care Professional Partnerships: Co-Design Study.
J Particip Med
January 2025
Department of Ambulatory Care, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Background: Health authorities worldwide have invested in digital technologies to establish robust information exchange systems for improving the safety and efficiency of medication management. Nevertheless, inaccurate medication lists and information gaps are common, particularly during care transitions, leading to avoidable harm, inefficiencies, and increased costs. Besides fragmented health care processes, the inconsistent incorporation of patient-driven changes contributes to these problems.
View Article and Find Full Text PDFRobustly interrogating machine learning-based scoring functions: what are they learning?
Bioinformatics
January 2025
Department of Statistics, University of Oxford, St Giles', Oxford, OX1 3LB, United Kingdom.
Motivation: Machine learning-based scoring functions (MLBSFs) have been found to exhibit inconsistent performance on different benchmarks and be prone to learning dataset bias. For the field to develop MLBSFs that learn a generalisable understanding of physics, a more rigorous understanding of how they perform is required.
Results: In this work, we compared the performance of a diverse set of popular MLBSFs (RFScore, SIGN, OnionNet-2, Pafnucy, and PointVS) to our proposed baseline models that can only learn dataset biases on a range of benchmarks.
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