P3997Catecholamine-induced cellular senescence in endothelium and bone marrow promotes cardiac inflammation and remodeling in mice
Bibliographic record
Abstract
Catecholamine, such as norepinephrine, plays a central role in the activation of adrenergic signaling. There is evidence that catecholamine-release is increased during heart failure, however, contribution to the pathology of heart failure remains to be defined. Here we show that continuous catecholamine-infusion induces cellular senescence in endothelial cells and bone marrow cells and promote pathologies by inducing cardiac inflammation in a murine heart failure model. Continuous isoproterenol infusion into C57B6/NCr mice induced cardiac dysfunction and promoted cardiac inflammation. In these mice, we found that expression of p53, known as a key molecule for inducing cellular senescence, is increased in cardiac tissue and bone marrow cells. Suppression of cellular senescence with genetic deletion of p53 in endothelial cells or bone marrow cells suppressed isoproterenol-induced cardiac inflammation and remodeling. In vitro studies showed that constitutive activation of adrenergic signaling would increase the expression of adhesion molecules in endothelial cells and macrophages via promoting p53 signaling. Our results indicate that catecholamine-induced cellular senescence in endothelium and bone marrow plays a crucial role in the progression of heart failure by promoting inflammatory response in cardiac tissue. Suppression of adrenergic signaling-p53-cellular senescence pathway is crucial for the inhibition of pathologies in heart failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".