The comparative incremental validity of five self-as-context measures in predicting psychological distress and satisfaction with life, after controlling for relevant demographic variables and other psychological flexibility processes, was evaluated in a college student sample ( = 315). All of the measures except the self-as-context subscale of the Multidimensional Psychological Flexibility Inventory (Rolffs et al., 2018) separately accounted for a significant increase in variability in psychological distress. The centering subscale of the Self-as-Context Scale (Zettle et al., 2018) was the only measure to also display incremental predictive validity in accounting for significant variance in life satisfaction. The conceptual and clinical implications of the findings in the context of study limitations are discussed.
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http://dx.doi.org/10.1891/JCP-2023-0032 | DOI Listing |
Int J Cardiol Heart Vasc
February 2025
Department of Nephropathy, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.
Background: Heart failure (HF) is a significant cause of death among patients with chronic kidney disease (CKD). Emerging data suggest a crucial role of fibroblast growth factor 23 (FGF23) in the pathogenesis of HF in CKD patients. The present study aimed to investigate whether the serum intact FGF23 (iFGF23) level is elevated when ejection fraction (EF) is preserved and to evaluate its predictive value for incident HF and cardiac mortality in CKD patients with preserved EF.
View Article and Find Full Text PDFBMJ Open
December 2024
Division of Research, Kaiser Permanente, Pleasanton, California, USA.
Objectives: The US Preventive Services Task Force recommends screening of adults aged 35-70 with a body mass index ≥25 kg/m for type 2 diabetes and referral of individuals who screen positive for pre-diabetes to evidence-based prevention strategies. The diabetes burden in the USA is predicted to triple by 2060 necessitating strategic diabetes prevention efforts, particularly in areas of highest need. This study aimed to identify pre-diabetes hotspots using geospatial mapping to inform targeted diabetes prevention strategies.
View Article and Find Full Text PDFCMAJ
January 2025
Schools of Health and Wellbeing (Nakada, Pell, Ho), and Cardiovascular and Metabolic Health (Welsh, Celis-Morales), University of Glasgow, Glasgow, UK; Human Performance Laboratory, Education, Physical Activity and Health Research Unit (Celis-Morales), Universidad Católica del Maule, Talca, Chile; Centro de Investigación en Medicina de Altura (CEIMA) (Celis-Morales), Universidad Arturo Prat, Iquique, Chile.
Background: Anxiety and depression are associated with cardiovascular disease (CVD). We aimed to investigate whether adding measures of anxiety and depression to the American Heart Association Predicting Risk of Cardiovascular Disease Events (PREVENT) predictors improves the prediction of CVD risk.
Methods: We developed and internally validated risk prediction models using 60% and 40% of the cohort data from the UK Biobank, respectively.
PLoS One
January 2025
Harvard extension school, Harvard University, Boston, Massachusetts, United States of America.
To address the limitations of existing stock price prediction models in handling real-time data streams-such as poor scalability, declining predictive performance due to dynamic changes in data distribution, and difficulties in accurately forecasting non-stationary stock prices-this paper proposes an incremental learning-based enhanced Transformer framework (IL-ETransformer) for online stock price prediction. This method leverages a multi-head self-attention mechanism to deeply explore the complex temporal dependencies between stock prices and feature factors. Additionally, a continual normalization mechanism is employed to stabilize the data stream, enhancing the model's adaptability to dynamic changes.
View Article and Find Full Text PDFJ Mol Cell Cardiol Plus
March 2025
Center for Clinical Investigation (CIC1436)/CARDIOMET, Rangueil University Hospital, Toulouse, France.
Background: The identification of new biomarkers that improve existing cardiovascular risk prediction models for acute coronary syndrome is essential for accurately identifying high-risk patients and refining treatment strategies. Autophagy, a vital cellular degradation mechanism, is important for maintaining cardiac health. Dysregulation of autophagy has been described in cardiovascular conditions such as myocardial ischemia-reperfusion injury, a key factor in myocardial infarction (MI).
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