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Aim: To discuss inter-organisational collaboration in the context of the successful COVID-19 vaccination programme in North Central London (NCL).
Design: An action research study in 2023-2024.
Methods: Six action research cycles used mixed qualitative methods.
Recruitment Challenges and Strategies in a Technology-Based Intervention for Dementia Caregivers: Descriptive Study.
JMIR Form Res
January 2025
College of Nursing, The Ohio State University, Columbus, OH, United States.
Background: Researchers have encountered challenges in recruiting unpaid caregivers of people living with Alzheimer disease and related dementias for intervention studies. However, little is known about the reasons for nonparticipation in in-home smart health interventions in community-based settings.
Objective: This study aimed to (1) assess recruitment rates in a smart health technology intervention for caregivers of people living with Alzheimer disease and related dementias and reasons for nonparticipation among them and (2) discuss lessons learned from recruitment challenges and strategies to improve recruitment.
First and Second Degree Left Bundle Branch Block and Masquerading Bundle Branch Block: Lessons Learned During Conduction System Pacing.
Heart Rhythm
January 2025
Geisinger Heart Institute, Geisinger Wyoming Valley Medical Center, MC 36-10, 1000 E Mountain Blvd, Wilkes-Barre, PA 18711.
COVID-19: Lessons Learned from Molecular and Clinical Research.
Int J Mol Sci
January 2025
IRCAD (Interdisciplinary Research Center of Autoimmune Diseases), Università del Piemonte Orientale (UPO), 28100 Novara, Italy.
SARS-CoV-2 virus, the etiological agent of the novel coronavirus disease 19 (COVID-19), was first identified in late 2019, following the sudden appearance of a cluster of pneumonia cases of unknown origin in China [...
View Article and Find Full Text PDFLessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins-The Case Study of Coiled-Coil NOD-like Receptors.
Int J Mol Sci
January 2025
Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independentei 296, 060031 Bucharest, Romania.
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain-like (NOD-like) receptors from and a few extra examples for reference. Results reveal a truly remarkable ability of these platforms to correctly predict the 3D structure of modules that fold in well-established topologies. A lower performance is noticed in modeling morphing regions of these proteins, such as the coiled coils.
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