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Resistance training is used to combat skeletal muscle function decline in older adults. Few studies have been designed specific for females, resulting in very limited treatment options for skeletal muscle atrophy in aging women. Here, we analyzed the gene expression profiles of skeletal muscle samples from sedentary young women, sedentary older women, and resistance-trained older women, using microarray data from public database. A total of 45 genes that were differentially expressed during female muscle aging and reversed by resistance training were identified. Functional and pathway enrichment analysis, protein–protein interaction network analysis, and receiver operating characteristic analysis were performed to reveal the key genes and pathways involved in the effects of resistance training on female muscle aging. The collagen genes COL1A1, COL3A1, and COL4A1 were identified important regulators of female muscle aging and resistance training, by modulating multiple signaling pathways, such as PI3 kinase-Akt signaling, focal adhesions, extracellular matrix-receptor interactions, and relaxin signaling. Interestingly, the expression of CDKN1A and TP63 were increased during aging, and further upregulated by resistance training in older women, suggesting they may negatively affect resistance training outcomes. Our findings provide novel insights into the molecular mechanisms of resistance training on female muscle aging and identify potential biomarkers and targets for clinical intervention. [ABSTRACT FROM AUTHOR]

The increasing prevalence of type 2 diabetes mellitus (T2DM) leads to significant global health challenges, including cardiac structural and functional deficits, which in severe cases can progress to heart failure that can further strain healthcare resources. Aerobic exercise can ameliorate cardiac dysfunction in individuals with diabetes, although a comprehensive understanding of its underlying mechanisms remains elusive. This study utilizes untargeted metabolomics to reveal aerobic-exercise-activated metabolic biomarkers in the cardiac tissues of Sprague Dawley rats with T2DM. Metabolomics analysis revealed that diabetes altered 1029 myocardial metabolites, while aerobic exercise reversed 208 of these metabolites, of which 112 were upregulated and 96 downregulated. Pathway topology analysis suggested that these metabolites predominantly contributed to purine metabolism and arginine biosynthesis. Furthermore, receiver operating characteristic curve analysis identified 10 potential biomarkers, including xanthine, hypoxanthine, inosine, dGMP, l-glutamic acid, l-arginine, l-tryptophan, (R)-3-hydroxybutyric acid, riboflavin, and glucolepidiin. Finally, data from Pearson correlation analysis indicated that some metabolic biomarkers strongly correlated with cardiac function. Our data suggest that certain metabolic biomarkers play an important role in ameliorating diabetes-related cardiac dysfunction by aerobic exercise. [ABSTRACT FROM AUTHOR]

ABSTRACT A greedy randomized augmented Kaczmarz (GRAK) method was proposed in Bai and Wu for large and sparse inconsistent linear systems. However, in each iteration of this method, one has to construct two new index sets via computing residual vector with respect to the augmented linear system. Thus, the computational overhead of this method may be large for extremely large‐scale problems. Moreover, there is no reliable stopping criterion for this method. In this work, we are interested in solving large‐scale sparse or dense inconsistent linear systems, and try to enhance the numerical performance of this method. The contributions of this work are as follows. First, we propose an accelerated GRAK method. Theoretical analysis indicates that it converges faster than the GRAK method under a very weak assumption. Second, we apply the semi‐randomized Kaczmarz method to the augmented linear system, and propose a semi‐randomized augmented Kaczmarz method with simple random sampling. In this method, there is no need to access all the information of the data matrix. The convergence of the proposed methods are established. To the best of our knowledge, there are no practical stopping criteria in all randomized Kaczmarz‐type methods till now. To fill‐in this gap, the third contribution of this work is to introduce a practical stopping criterion for Kaczmarz‐type methods, and show its rationality from a theoretical point of view. Numerical results are performed on both real‐world and synthetic data sets, which demonstrate the effectiveness of the proposed stopping criterion, and illustrate that the new methods are often superior to many state‐of‐the‐art Kaczmarz methods for large‐scale inconsistent linear systems. [ABSTRACT FROM AUTHOR]