Angptl2 is a Marker of Cellular Senescence: The Physiological and Pathophysiological Impact of Angptl2-Related Senescence
Bibliographic record
Abstract
Cellular senescence is a cell fate primarily induced by DNA damage, characterized by irreversible growth arrest in an attempt to stop the damage. Senescence is a cellular response to a stressor and is observed with aging, but also during wound healing and in embryogenic developmental processes. Senescent cells are metabolically active and secrete a multitude of molecules gathered in the senescence-associated secretory phenotype (SASP). The SASP includes inflammatory cytokines, chemokines, growth factors and metalloproteinases, with autocrine and paracrine activities. Among hundreds of molecules, angiopoietin-like 2 (angptl2) is an interesting, although understudied, SASP member identified in various types of senescent cells. Angptl2 is a circulatory protein, and plasma angptl2 levels increase with age and with various chronic inflammatory diseases such as cancer, atherosclerosis, diabetes, heart failure and a multitude of age-related diseases. In this review, we will examine in which context angptl2 was identified as a SASP factor, describe the experimental evidence showing that angptl2 is a marker of senescence in vitro and in vivo, and discuss the impact of angptl2-related senescence in both physiological and pathological conditions. Future work is needed to demonstrate whether the senescence marker angptl2 is a potential clinical biomarker of age-related diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".