Senescent Cells Contribute to the Physiological Remodeling of Aged Lungs
Bibliographic record
Abstract
Age-associated decline in organ function governs life span. We determined the effect of aging on lung function and cellular/molecular changes of 8- to 32-month old mice. Proteomic analysis of lung matrix indicated significant compositional changes with advanced age consistent with a profibrotic environment that leads to a significant increase in dynamic compliance and airway resistance. The excess of matrix proteins deposition was associated modestly with the activation of myofibroblasts and transforming growth factor-beta signaling pathway. More importantly, detection of senescent cells in the lungs increased with age and these cells contributed toward the excess extracellular matrix deposition observed in our aged mouse model and in elderly human samples. Mechanistic target of rapamycin (mTOR)/AKT activity was enhanced in aged mouse lungs compared with those from younger mice associated with the increased expression of the histone variant protein, MH2A, a marker for aging and potentially for senescence. Introduction in the mouse diet of rapamycin, significantly blocked the mTOR activity and limited the activation of myofibroblasts but did not result in a reduction in lung collagen deposition unless it was associated with prevention of cellular senescence. Together these data indicate that cellular senescence significantly contributes to the extracellular matrix changes associated with aging in a mTOR 1-dependent mechanism.
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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) | 0.001 | 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".