ABSTRACT
An early diagnosis of Alzheimer's disease is crucial as treatment efficacy is limited to the early stages. However, the current diagnostic methods are limited to mid or later stages of disease development owing to the limitations of clinical examinations and amyloid plaque imaging. Therefore, this study aimed to identify molecular signatures including blood plasma extracellular vesicle biomarker proteins associated with Alzheimer's disease to aid early-stage diagnosis. The hippocampus, cortex, and blood plasma extracellular vesicles of 3- and 6-month-old 5xFAD mice were analyzed using quantitative proteomics. Subsequent bioinformatics and biochemical analyses were performed to compare the molecular signatures between wild type and 5xFAD mice across different brain regions and age groups to elucidate disease pathology. There was a unique signature of significantly altered proteins in the hippocampal and cortical proteomes of 3- and 6-month-old mice. The plasma extracellular vesicle proteomes exhibited distinct informatic features compared with the other proteomes. Furthermore, the regulation of several canonical pathways (including phosphatidylinositol 3-kinase/protein kinase B signaling) differed between the hippocampus and cortex. Twelve potential biomarkers for the detection of early-stage Alzheimer's disease were identified and validated using plasma extracellular vesicles from stage-divided patients. Finally, integrin α-IIb, creatine kinase M-type, filamin C, glutamine γ-glutamyltransferase 2, and lysosomal α-mannosidase were selected as distinguishing biomarkers for healthy individuals and early-stage Alzheimer's disease patients using machine learning modeling with approximately 79% accuracy. Our study identified novel early-stage molecular signatures associated with the progression of Alzheimer's disease, thereby providing novel insights into its pathogenesis.
Subject(s)
Alzheimer Disease , Mice, Transgenic , Proteomics , Animals , Alzheimer Disease/metabolism , Alzheimer Disease/genetics , Alzheimer Disease/pathology , Alzheimer Disease/blood , Mice , Proteomics/methods , Biomarkers/blood , Biomarkers/metabolism , Humans , Disease Models, Animal , Proteome/metabolism , MaleABSTRACT
AIM: This study aimed to identify the effects of the subfactors of self-compassion on hospital nurse burnout and provide basic knowledge for an intervention programme to prevent nurse burnout. BACKGROUND: Burnout in nurses has been reported to influence their own and patients' mental health and safety. Enhancing self-compassion can help to prevent burnout. METHODS: This study involved descriptive research using a cross-sectional design. Data were collected at H University Hospital in Korea, from January to March 2020. Of the 213 questionnaires collected, 208 were included in data analysis. RESULTS: Burnout was higher when nurses were younger, unmarried, in poor health, general nurses, dissatisfied with their jobs, planning to leave their jobs or receiving low social support from peers. There was a negative correlation between burnout and the self-compassion subscales. Multiple regression analysis showed that isolation and mindfulness among self-compassion, were significantly related to burnout. CONCLUSIONS: Since individual variables and self-compassion are major elements impacting nurse burnout, it is important for nursing organizations to investigate nurses' individual variables, including subfactors of self-compassion, to help them prevent burnout. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse burnout might be prevented by increasing self-compassion. Hospital and nurse managers may employ individualized strategies to enhance nurses' self-compassion.
