Abstract 13816: Clearing P16+ Cells Prevents Cardiac Dysfunction and Death in Mice With Acute Myocardial Infarction
Bibliographic record
Abstract
Introduction: Cellular senescence, a stress and age-related response, is often characterized by p16 expression in p16 positive (p16 + ) senescent cells. Senescent cells secrete profibrotic and proinflammatory factors as parts of a “senescence associated secretory phenotype”. The role of cellular senescence in myocardial infarction (MI) is unclear. Aims: To study the effect of p16 + cell clearance on survival, cardiac remodeling and function after MI. Methods: We used INK ATTAC transgenic mice, in which p16 + cells undergo targeted apoptosis upon exposure to a dimerizer, AP20187 (AP). MI mice were treated with AP or vehicle (V), twice a week for 1 month, beginning 3-4 hours post-MI. Cardiac function and structure were evaluated with echocardiography, hemodynamic parameters with a Millar catheter, infarct area with Masson’s Trichrome, gene expression with qPCR and single-nucleus RNA-sequencing (snRNAseq). Results: Survival post MI was dramatically increased in MI-AP vs MI-V mice (P<0.001, Fig A), primarily via prevention of cardiac rupture (CardRup). Left ventricular (LV) ejection fraction (EF%) and LV anterior wall thickness at end diastole (LVAWd) decreased in MI-V vs Sham and improved in MI-AP mice (Fig B), indicating preservation of cardiac function and structure with p16 + cell clearance. Hemodynamic indices of LV function (max and min dP/dt) improved with AP (Fig C). Fibrosis-related gene expression (Col1a1, Col3a1 and Lox; Fig D) increased with MI and improved with AP, paralleling changes in infarct size (Fig E). snRNAseq showed that genes were principally upregulated in MI-V macrophages and neutrophils; significant responses were seen in TNF-α, inflammatory response pathways and gene programs associated with CardRup (Fig F). Conclusion: These results suggest that p16 + cells play a significant role in the evolution of MI, apparently by modulating inflammatory signaling, pointing to an important pathophysiological role of cellular senescence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".