Although cellular senescence has been recognized as a hallmark of aging, it is challenging to detect senescence cells (SnCs) due to their high level of heterogeneity at the molecular level. Machine learning (ML) is likely an ideal approach to address this challenge because of its ability to recognize complex patterns that cannot be characterized by one or a few features, from high-dimensional data. To test this, we evaluated the performance of four ML algorithms including support vector machines (SVM), random forest (RF), decision tree (DT), and Soft Independent Modelling of Class Analogy (SIMCA), in distinguishing SnCs from controls based on bulk RNA sequencing data.
View Article and Find Full Text PDFJ Vasc Bras
November 2024
Blood coagulation is a vital physiological process involving a complex network of biochemical reactions, which converge to form a blood clot that repairs vascular injury. This process unfolds in three phases: initiation, amplification, and propagation, ultimately leading to thrombin formation. Coagulation begins when tissue factor (TF) is exposed on an injured vessel's wall.
View Article and Find Full Text PDFSenescence is a cell fate driven by different types of stress that results in exit from the cell cycle and expression of an inflammatory senescence-associated secretory phenotype (SASP). Here, we demonstrate that stable overexpression of miR-96-5p was sufficient to induce cellular senescence in the absence of genotoxic stress, inducing expression of certain markers of early senescence including SASP factors while repressing markers of deep senescence including LINE-1 and type 1 interferons. Stable miR-96-5p overexpression led to genome-wide changes in heterochromatin followed by epigenetic activation of p16, p21, and SASP expression, induction of a marker of DNA damage, and induction of a transcriptional signature similar to other senescent lung and endothelial cell types.
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