3D ECM-Based Scaffolds Boost Young Cell Secretome-Derived EV Rejuvenating Effects in Senescent Cells
Bibliographic record
Abstract
Aging is a complex, multifaceted degenerative process characterized by a progressive accumulation of macroscopic and microscopic modifications that cause a gradual decline of physiological functions. During the last few years, strategies to ease and counteract senescence or even rejuvenate cells and tissues were proposed. Here we investigate whether young cell secretome-derived extracellular vesicles (EVs) ameliorate the cellular and physiological hallmarks of aging in senescent cells. In addition, based on the assumption that extracellular matrix (ECM) provides biomechanical stimuli, directly influencing cell behavior, we examine whether ECM-based bio-scaffolds, obtained from decellularized ovaries of young swine, stably maintain the rejuvenated phenotype acquired by cells after exposure to young cell secretome. The results obtained demonstrate that young cells release EVs endowed with the ability to counteract aging. In addition, comparison between young and aged cell secretomes shows a significantly higher miR-200 content in EVs produced using fibroblasts isolated from young donors. The effect exerted by young cell secretome-derived EVs is transient, but can be stabilized using a young ECM microenvironment. This finding indicates a synergistic interaction occurring among molecular effectors and ECM-derived stimuli that cooperate to control a unique program, driving the cell clock. The model described in this paper may represent a useful tool to finely dissect the complex regulations and multiple biochemical and biomechanical cues driving cellular biological age.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".