Publications by authors named "M Nolden"
Background: Evidence-based practice (EBP) is foundational to safe and quality health care; however, barriers to nursing engagement in EBP have been well documented. To circumvent these barriers, nursing leadership must proactively implement system-level, multifaceted strategies within their organization to enhance EBP engagement. One Veterans Administration (VA) hospital has operationalized these strategies.
View Article and Find Full Text PDFCRISPR/Cas9 genome editing is a rapidly advancing technology that has the potential to accelerate research and development in a variety of fields. However, manual genome editing processes suffer from limitations in scalability, efficiency, and standardization. The implementation of automated systems for genome editing addresses these challenges, allowing researchers to cover the increasing need and perform large-scale studies for disease modeling, drug development, and personalized medicine.
View Article and Find Full Text PDFArticle Synopsis
- The study evaluates how well deep-learning models detect chronic obstructive pulmonary disease (COPD) in different ethnic groups, focusing on non-Hispanic Whites and African Americans.
- Training on balanced datasets (both ethnic groups) and using self-supervised learning methods significantly improved model performance and reduced biases compared to using population-specific data.
- The results underscore the need for equitable and effective AI healthcare solutions to ensure accurate COPD diagnosis across diverse populations.
Objectives: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation.
Materials And Methods: The consensus was achieved by a multi-stage process.
Article Synopsis
- The study aims to improve the understanding of chronic obstructive pulmonary disease (COPD) by comparing traditional diagnostic methods with a new self-supervised anomaly detection technique on CT scans, focusing on early detection and disease progression.
- Using data from 1,310 individuals, including COPD patients and never-smokers, researchers applied the anomaly detection approach to identify lung abnormalities and associated these findings with traditional parametric response mapping (PRM) and pulmonary function tests.
- Analysis revealed clear patterns of lung abnormalities linked to different stages of COPD and identified distinct clusters, highlighting the effectiveness of the new method in distinguishing between healthy and diseased lung regions.