Airway Epithelium Senescence as a Driving Mechanism in COPD Pathogenesis
Bibliographic record
Abstract
Cellular senescence is a state of permanent cell cycle arrest triggered by various intrinsic and extrinsic stressors. Cellular senescence results in impaired tissue repair and remodeling, loss of physiological integrity, organ dysfunction, and changes in the secretome. The systemic accumulation of senescence cells has been observed in many age-related diseases. Likewise, cellular senescence has been implicated as a risk factor and driving mechanism in chronic obstructive pulmonary disease (COPD) pathogenesis. Airway epithelium exhibits hallmark features of senescence in COPD including activation of the p53/p21WAF1/CIP1 and p16INK4A/RB pathways, leading to cell cycle arrest. Airway epithelial senescent cells secrete an array of inflammatory mediators, the so-called senescence-associated secretory phenotype (SASP), leading to a persistent low-grade chronic inflammation in COPD. SASP further promotes senescence in an autocrine and paracrine manner, potentially contributing to the onset and progression of COPD. In addition, cellular senescence in COPD airway epithelium is associated with telomere dysfunction, DNA damage, and oxidative stress. This review discusses the potential mechanisms of airway epithelial cell senescence in COPD, the impact of cellular senescence on the development and severity of the disease, and highlights potential targets for modulating cellular senescence in airway epithelium as a potential therapeutic approach in COPD.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".