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It is extremely significant to explore the relationship between ruminative thinking and breathlessness catastrophizing among elderly COPD patients. However, the impact of self-efficacy on this relationship is still unclear. This study attempted to explore the mediating role of self-efficacy between ruminative thinking and breathlessness catastrophizing.
A cross-sectional study was reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
This study was conducted between 10 November 2024 and 25 January 2025, with 225 patients. Data were collected using the valid and reliable instruments, including the Ruminative Response Scale (RRS), the COPD Self-Efficacy Scale (CSES) and the Breathlessness Catastrophizing Scale (BCS). Additionally, IBM SPSS v28.0 software was used to explore the mediating effect.
The scores for BCS of most patients were at moderate and high levels. Scores for the RRS and CSES were significantly correlated with the BCS. The analysis of the mediating effect demonstrated that ruminative thinking has a direct predictive effect on breathlessness catastrophizing. Additionally, ruminative thinking can also predict breathlessness catastrophizing indirectly through the mediation of self-efficacy. The direct effect accounted for 64.4% of the total effect.
This research revealed that self-efficacy played a partial mediating role in the relationship between ruminative thinking and breathlessness catastrophizing. Specifically, patients who were trapped in ruminative thinking were more likely to experience heightened breathlessness catastrophizing, but this relationship was mitigated by their level of self-efficacy.
This finding underscores the significant psychological burden that accompanies the physical symptoms of COPD in this demographic. It is imperative that nurses adopt a holistic approach in the management of elderly COPD patients.
Voluntary patients with elderly COPD hospitalised in the pneumology department were included in the study.
Hypertrophic scar (HS) is a chronic inflammatory skin disorder characterized by excessive deposition of extracellular matrix, and the mechanisms underlying their formation remain poorly understood. We analysed scRNA-seq data from samples of normal skin and HS. Using the hdWGCNA method, key gene modules of fibroblasts in HS were identified. Non-negative matrix factorization was employed to perform subtype analysis of HS patients using these gene modules. Multiple machine learning algorithms were applied to screen and validate accurate gene signatures for identifying and predicting HS, and a convolutional neural network (CNN) based on deep learning was established and validated. Quantitative reverse transcription-polymerase chain reaction and western blotting were performed to measure mRNA and protein expression. Immunofluorescence was used for gene localization analysis, and biological features were assessed through CCK8 and wound healing assay. Single-cell sequencing revealed distinct subpopulations of fibroblasts in HS. HdWGCNA identified key gene characteristics of this population, and pseudotime analysis was conducted to investigate gene variation during fibroblast differentiation. By employing various machine learning algorithms, the gene range was narrowed down to three key genes. A CNN was trained using the expression of these key genes and immune cell infiltration, enabling diagnosis and prediction of HS. Functional experiments demonstrated that THBS2 is associated with fibroblast proliferation and migration in HS and affects the formation and development of HS through the TGFβ1/P-Smad2/3 pathway. Our study identifies unique fibroblast subpopulations closely associated with HS and provides biomarkers for the diagnosis and treatment of HS.
FreshRSS 