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Objective: To evaluate the reliability, the minimally important difference (MID), and the learning effect of the 1-minute sit-to-stand test (1STST) in assessing functional exercise capacity in patients with heart failure (HF).
Design: A cross-sectional study.
Setting: Two hospitals.
Participants: Patients with an HF (N=47) diagnosis by a cardiologist following the 2021 European Society of Cardiology HF guideline. Most participants were men (60%). Most participants had a normal body mass index (62%). All participants were classified as having New York Heart Association Functional Class II or III.
Interventions: Not applicable.
Main Outcome Measures: Each patient performed a 6-minute walk test (6MWT) and 2 1STSTs on 2 occasions spaced 1 month apart. Test-retest reliability between occasions was evaluated using the intraclass correlation coefficient (ICC), Bland-Altman plot analysis, and linear regression. The MID was determined using a distribution-based method. To assess the learning effect, we conducted a paired t test comparing the 2 1STSTs on each occasion, and the magnitude of the learning effect was quantified using Cohen's d. The correlation between the 1STST and 6MWT was performed to confirm validity.
Results: The 1STST showed good test-retest reliability (ICC=0.98, P<.001) with good agreement between the 2 measurements, without proportional bias (P>.05). The MID was 1.1 repetitions. A learning effect of the 1STST was observed (P<.001) with a large effect size when repeated a month later (Cohen's d>0.8). The 1STST strongly correlated with the 6MWT (r=0.74, P=.01).
Conclusions: The 1STST is a valid and reliable measure of functional exercise capacity in patients with HF. Because of the learning effect, this study recommended performing 2 trials to capture the true value.
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http://dx.doi.org/10.1016/j.apmr.2025.05.004 | DOI Listing |
Front Immunol
September 2025
Department of Medicine, Division of Hematology, Bioclinicum and Center for Molecular Medicine, Karolinska Institute and Karolinska University Hospital Solna, Stockholm, Sweden.
Background: Metabolic reprogramming is an important hallmark of cervical cancer (CC), and extensive studies have provided important information for translational and clinical oncology. Here we sought to determine metabolic association with molecular aberrations, telomere maintenance and outcomes in CC.
Methods: RNA sequencing data from TCGA cohort of CC was analyzed for their metabolic gene expression profile and consensus clustering was then performed to classify tumors into different groups/subtypes.
Int J Chron Obstruct Pulmon Dis
September 2025
The First Clinical Medical College of Lanzhou University, Lanzhou, People's Republic of China.
Chronic Obstructive Pulmonary Disease (COPD) is a prevalent chronic respiratory disorder characterized by airway inflammation and irreversible airflow limitation. Its marked heterogeneity and complexity pose significant challenges to traditional clinical assessments in terms of prognostic prediction and personalized management. In recent years, the exploration of biomarkers has opened new avenues for the precise evaluation of COPD, particularly through multi-biomarker prediction models and integrative multimodal data strategies, which have substantially improved the accuracy and reliability of prognostic assessments.
View Article and Find Full Text PDFVet Anim Sci
December 2025
Hunan Provincial Key Laboratory of the Traditional Chinese Medicine Agricultural Biogenomics, Changsha Medical University, Changsha 410219, China.
Muscovy duck reovirus (MDRV) and Novel duck reovirus (NDRV) are highly infectious diseases of waterfowl, causing significant harm to the global poultry industry. Early detection and diagnosis of NDRV and MDRV in clinical samples are crucial for effectively preventing and controlling these diseases. This study developed a duplex crystal digital PCR (dPCR) assay for the differential detection of NDRV and MDRV.
View Article and Find Full Text PDFJ Biomed Opt
September 2025
Leibniz University Hannover, Hannover Centre for Optical Technologies, Hannover, Germany.
Significance: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability.
Aim: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach.