Fernandez-Aviles, F. | Search bioRxiv

bioRxiv2024

Unravelling the ageing-reversal potency of stem cell-derived extracellular vesicles in a rat model of premature cardiac senescence

Cardiosphere-derived cells (CDCs) and the extracellular vesicles they release (CDC-EVs) have demonstrated to induce rejuvenation and to improve cardiac structure and function, but their efficacy may vary among donors and there is still a lack of adequate potency assays. We aimed to identify parameters that could easily predict the ageing-reversal potency of CDC-EVs. CDCs derived from cardiac tissue of 34 human donors (age range: 3-months to 81-years old) were characterized in terms of their phenotypical and biological properties. The anti-senescent activity of the CDC-EVs was assessed in vitro using a predesigned matrix assay with several ageing-related markers. We found that while CDC-donors chronological age was not determinant, the degree of CDC senescence in culture showed a strong correlation to most CD[C]s bioactive properties. However, CDC senescence alone was insufficient to predict CDC-EV in vitro potency. Thus, we scored the potency of the CDC-EVs based on their performance in the newly designed in vitro matrix assay and classified them as more-(P-EVs) and less-potent (NP-EVs). We then tested P- and NP-EVs in a rat model of premature cardiac ageing and found that only the P-EVs reduced senescence-associated GLB1 gene expression in the heart and tended to protect it from hypertrophy development and fibrosis. On the contrary, NP-EVs failed to induce any improvements and negatively affected cardiac hypertrophy, fibrosis, and perfusion. In conclusion, despite CDC-EV anti-ageing potency cannot be predicted by the chronological age of the donors or CDC senescence as surrogate markers, we propose an in vitro potency assay that could be used to evaluate allogenic EV suitability before its use in the treatment of cardiac ageing.

cell biology↗

bioRxiv2018

Exploratory and prospective model of stent restenosis after Percutaneous Coronary Intervention using MultivariateGaussian Subspatial Regression

A new statistical analysis methodology, "Multivariate Gaussian Subspatial Regression" (MGSR), has been applied to randomized clinical trial data collected from percutaneous coronary intervention (PCI) patients, which combines the descriptive quality of Factorial Techniques and the predictive power of Gaussian Processes. This model has been built from 3 different quantitative coronary angiographic core-lab measures of the same lesion from 2 separate angiograms (at baseline before PCI, at baseline immediately after PCI and at 12 months follow-up). Measurements of the pre-PCI variables of a patient are mapped to the factorial plane and predictions are visualized as regions of interest in this plane. MGSR makes it possible to detect patients at risk of coronary stent restenosis or patients in whom ruling out the disease, in a graphical way; avoiding unnecessary, costly and possibly risky treatments for patients with no complications predicted; and advising to closely follow patients at risk. In addition, the model recovers missing values regardless of the variables, and once fitted, it corrects itself when more dependent variables are included. MGSR software is freely available online at https://github.com/victorvicpal/MGSR.

bioinformatics↗