Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.

Background: EPs pose significant challenges to individual health and quality of life, attracting attention in public health as a risk factor for diminished quality of life and healthy life expectancy in middle-aged and older adult populations. Therefore, in the context of global aging, meticulous exploration of the factors behind emotional issues becomes paramount. Whether ADL can serve as a potential marker for EPs remains unclear. This study aims to provide new evidence for ADL as an early predictor of EPs through statistical analysis and validation using machine learning algorithms. Methods: Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) national baseline survey, comprising 9,766 samples aged 45 and above, were utilized. ADL was assessed using the BI, while the presence of EPs was evaluated based on the record of "Diagnosed with Emotional Problems by a Doctor" in CHARLS data. Statistical analyses including independent samples t-test, chi-square test, Pearson correlation analysis, and multiple linear regression were conducted using SPSS 25.0. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented using Python 3.10.2. Results: Population demographic analysis revealed a significantly lower average BI score of 65.044 in the "Diagnosed with Emotional Problems by a Doctor" group compared to 85.128 in the "Not diagnosed with Emotional Problems by a Doctor" group. Pearson correlation analysis indicated a significant negative correlation between ADL and EPs (r = -0.165, p < 0.001). Iterative analysis using stratified multiple linear regression across three different models demonstrated the persistent statistical significance of the negative correlation between ADL and EPs (B = -0.002, ß = -0.186, t = -16.476, 95% CI = -0.002, -0.001, p = 0.000), confirming its stability. Machine learning algorithms validated our findings from statistical analysis, confirming the predictive accuracy of ADL for EPs. The area under the curve (AUC) for the three models were SVM-AUC = 0.700, DT-AUC = 0.742, and LR-AUC = 0.711. In experiments using other covariates and other covariates + BI, the overall prediction level of machine learning algorithms improved after adding BI, emphasizing the positive effect of ADL on EPs prediction. Conclusion: This study, employing various statistical methods, identified a negative correlation between ADL and EPs, with machine learning algorithms confirming this finding. Impaired ADL increases susceptibility to EPs.

The Effect of Formalin Preservation Time and Temperature on the Material Properties of Bovine Femoral Cortical Bone Tissue.

Literature has reported controversial findings on whether formalin affected bone properties, or not, especially when different preservation time durations and temperatures were involved. Hence, accurately and systematically quantifying the effect of formalin on the mechanical properties of bone using a large dataset is crucial for assessing biomechanical responses based on fixed specimens. A total of 154 longitudinal and 149 transverse cuboid-shaped (12 mm × 2 mm × 0.5 mm) specimens from the midsection of 12 bovine femora from six bovines were prepared and assigned to ten groups, including fresh-frozen, formalin-preserved at 25 °C for 4 weeks and 8 weeks, and formalin-preserved at 4 °C for 4 weeks and 8 weeks. All specimens underwent quasi-static three-point bending tests with a loading rate of 0.02 mm/s. The Young's modulus, yield stress, yield strain, tangent modulus, effective plastic strain, ultimate stress, and toughness were calculated by optimizing the material parameters to make the force-displacement curve of the finite element prediction consistent with the experimental curve, combined with specimen-specific finite element models. Preservation time and temperature both had significant effects on the Young's modulus, yield stress, effective plastic strain, yield strain and ultimate stress of cortical bone (p < 0.05). The Young's modulus, yield stress, and ultimate stress of longitudinal specimens decreased significantly with the increase of preservation time, and the yield strain increased significantly. As the preservation temperature increases, the Young's modulus of the transverse sample decreased significantly, and the yield strain increased significantly. The preservation time mainly affects the longitudinal specimens, while the preservation temperature mainly affects the transverse specimens. Formalin preservation of bovine femoral cortical bones at a lower temperature and less than 4 weeks is recommended for biomechanical testing.

Combining specimen-specific finite-element models and optimization in cortical-bone material characterization improves prediction accuracy in three-point bending tests.

Although the beam theory is widely used for calculating material parameters in three-point bending test, it cannot accurately describe the biomechanical properties of specimens after the yield. Hence, we propose a finite element (FE) based optimization method to obtain accurate bone material parameters from three-point bending test. We tested 80 machined bovine cortical bone specimens at both longitudinal and transverse directions using three-point bending. We then adopted the beam theory and the FE-based optimization method combined with specimen-specific FE models to derive the material parameters of cortical bone. We compared data obtained using these two methods and further evaluated two groups of parameters with three-point bending simulations. Our data indicated that the FE models with material properties from the FE-based optimization method showed best agreements with experimental data for the entire force-displacement responses, including the post-yield region. Using the beam theory, the yield stresses derived from 0.0058% strain offset for the longitudinal specimen and 0.0052% strain offset for the transverse specimen are closer to those derived from the FE-based optimization method, compared to yield stresses calculated without strain offset. In brief, we conclude that the optimization FE method is more appropriate than the traditional beam theory in identifying the material parameters of cortical bone for improving prediction accuracy in three-point bending mode. Given that the beam theory remains as a popular method because of its efficiency, we further provided correction functions to adjust parameters calculated from the beam theory for accurate FE simulation.