This study aims to provide an empirical demonstration of a novel method, regression mixture model, by examining differential effects of somatic amplification to positive affect and identifying the predictors that contribute to the differential effects. Data derived from the second wave of Midlife in the United States. The analytic sample consisted of 1,766 adults aged from 33 to 84 years. Regression mixture models were fitted using Mplus 7.4, and a two-step model-building approach was adopted. Three latent groups were identified consisting of a maladaptive (32.1%), a vulnerable (62.5%), and a resilient (5.4%) group. Six covariates (i.e., age, education level, positive relations with others, purpose in life, depressive symptoms, and physical health) significantly predicted the latent class membership in the regression mixture model. The study demonstrated the regression mixture model to be a flexible and efficient statistical tool in assessing individual differences in response to adversity and identifying resilience factors, which contributes to aging research.
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http://dx.doi.org/10.1177/00914150211066552 | DOI Listing |
Energy Fuels
January 2025
PolySense Lab, Dipartimento Interateneo di Fisica, University and Polytechnic of Bari, Via Amendola 173, Bari 70126, Italy.
A compact and portable gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) for the detection of methane (C1), ethane (C2), and propane (C3) in natural gas (NG)-like mixtures is reported. An interband cascade laser (ICL) emitting at 3367 nm is employed to target absorption features of the three alkanes, and partial least-squares regression analysis is employed to filter out spectral interferences and matrix effects characterizing the examined gas mixtures. Spectra of methane, ethane, and propane mixtures diluted in nitrogen are employed to train and test the regression algorithm, achieving a prediction accuracy of ∼98%, ∼96%, and ∼93% on C1, C2, and C3, respectively.
View Article and Find Full Text PDFDiabetes Metab Res Rev
January 2025
Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Department of Biostatistics and Epidemiology, Ministry of Education, School of Public Health, China Medical University, Shenyang, China.
Aims: Stroke is a common diabetic complication, by which the Chinese visceral adiposity index (CVAI) is confirmed as a better predictor of visceral fat. However, the relationship between CVAI change and the stroke risk among patients with diabetes and prediabetes remains unclear. Therefore, we aimed to examine the association of CVAI trajectory with the risk of stroke.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Social Medicine, School of Health Management, Harbin Medical University, Harbin, 150081, China.
Background: Accumulating research highlights that exposure to serum brominated flame retardants (BFRs) may elevate health risks. The effects of serum BFRs, both alone and in combination, on obstructive sleep apnea syndrome (OSAS) have not been thoroughly studied. Our main goal was to examine the association between individual and mixtures of serum BFRs and OSAS risk.
View Article and Find Full Text PDFBiol Trace Elem Res
January 2025
Department of Geriatrics, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, 210011, China.
Several studies have reported associations between specific heavy metals and essential trace elements and acute myocardial infarction (AMI). However, there is limited understanding of the relationships between trace elements and AMI in real-life co-exposure scenarios, where multiple elements may interact simultaneously. This cross-sectional study measured serum levels of 56 trace elements using inductively coupled plasma mass spectrometry.
View Article and Find Full Text PDFAm J Cancer Res
December 2024
Department of Oncology, Dongying District People's Hospital 333 Jinan Road, Dongying District, Dongying, Shandong, China.
The use of routine adjuvant radiotherapy (RT) after breast-conserving surgery (BCS) is controversial in elderly patients with early-stage breast cancer (EBC). This study aimed to evaluate the efficacy of adjuvant RT for elderly EBC patients using deep learning (DL) to personalize treatment plans. Five distinct DL models were developed to generate personalized treatment recommendations.
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