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| http://dx.doi.org/10.1016/j.afos.2023.08.004 | DOI Listing |
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What are family caregivers' experiences of coordinating end-of-life care at home? A narrative review.
Palliat Support Care
January 2025
School of Nursing and Midwifery, University of Plymouth, Plymouth, UK.
Objectives: People with life-limiting diseases, who are no longer receiving active or curable treatment, often state their preferred place of care and death as the home. This requires coordinating a multidisciplinary approach, using available health and social care services to synchronize care. Family caregivers are key to enabling home-based end-of-life support; however, the 2 elements that facilitate success - coordination and family caregiver - are not necessarily associated as being intertwined or one and the same.
View Article and Find Full Text PDFUse of the chronic illness research recruitment taxonomy to evaluate recruitment strategies in an eHealth feasibility study.
Contemp Clin Trials Commun
February 2025
Department of Public Health, Faculty of Health Sciences, University of Stavanger, 4036 Stavanger, Norway.
Background: Chronic illness research has many challenges making research recruitment difficult. Despite reports of facilitators and barriers to research recruitment challenges remain. The reporting of research strategies and their impact on recruitment and subsequent randomised control trials is not sufficient.
View Article and Find Full Text PDFAdverse drug events (ADEs) risk signal mining related to eculizumab based on the FARES database.
Front Pharmacol
January 2025
The First Department of Specialty Medicine, Inner Mongolia Corps Hospital of The Chinese People's Armed Police Force, Hohhot, China.
Introduction: Eculizumab is a C5 complement inhibitor approved by the FDA for the targeted treatment of four rare diseases, paroxysmal nocturnal hemoglobinuria (PNH), atypical hemolytic uremic syndrome (aHUS), generalized myasthenia gravis (gMG), and aquaporin-4 immunoglobulin G-positive optic neuromyelitis optica spectrum disorders (AQP4-IgG+NMOSD). The current study was conducted to assess real-world adverse events (AEs) associated with eculizumab through data mining of the FDA Adverse Event Reporting System (FAERS).
Methods: Disproportionality analyses, including Reporting Ratio Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS) algorithms were used to quantify the signals of eculizumab-associated AEs.
Screening and identification of two novel phosphate-solubilizing strains and their role in enhancing phosphorus uptake in rice.
Front Microbiol
January 2025
Rice Research Institute, Guangdong Academy of Agricultural Sciences/Guangdong Key Laboratory of New Technology in Rice Breeding/Guangdong Rice Engineering Laboratory/Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, China.
Low phosphorus (P) use efficiency significantly impacts rice yields. An environmentally friendly approach to increase phosphorus absorption and utilization in rice involves the exploration of phosphorus-solubilizing fungal resources. This study aimed to isolate and characterize fungal strains from the rice rhizosphere and assess their phosphate solubilization capabilities, plant-growth-promoting (PGP) traits, and mechanisms involved.
View Article and Find Full Text PDFIdentification of Bacterial Lipopolysaccharide-Associated Genes and Molecular Subtypes in Autism Spectrum Disorder.
Pharmgenomics Pers Med
January 2025
Department of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, 637000, People's Republic of China.
Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by diverse symptoms affecting social interaction, communication, and behavior. This research aims to explore bacterial lipopolysaccharide (LPS)- and immune-related (BLI) molecular subgroups in ASD to enhance understanding of the disorder.
Methods: We analyzed 89 control samples and 157 ASD samples from the GEO database, identifying BLI signatures using least absolute shrinkage and selection operator regression (LASSO) and logistic regression machine learning algorithms.
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