Regulation of cellular senescence by extracellular matrix during chronic fibrotic diseases
Bibliographic record
Abstract
The extracellular matrix (ECM) is a complex network of macromolecules surrounding cells providing structural support and stability to tissues. The understanding of the ECM and the diverse roles it plays in development, homoeostasis and injury have greatly advanced in the last three decades. The ECM is crucial for maintaining tissue homoeostasis but also many pathological conditions arise from aberrant matrix remodelling during ageing. Ageing is characterised as functional decline of tissue over time ultimately leading to tissue dysfunction, and is a risk factor in many diseases including cardiovascular disease, diabetes, cancer, dementia, glaucoma, chronic obstructive pulmonary disease (COPD) and fibrosis. ECM changes are recognised as a major driver of aberrant cell responses. Mesenchymal cells in aged tissue show signs of growth arrest and resistance to apoptosis, which are indicative of cellular senescence. It was recently postulated that cellular senescence contributes to the pathogenesis of chronic fibrotic diseases in the heart, kidney, liver and lung. Senescent cells negatively impact tissue regeneration while creating a pro-inflammatory environment as part of the senescence-associated secretory phenotype (SASP) favouring disease progression. In this review, we explore and summarise the current knowledge around how aberrant ECM potentially influences the senescent phenotype in chronic fibrotic diseases. Lastly, we will explore the possibility for interventions in the ECM-senescence regulatory pathways for therapeutic potential in chronic fibrotic 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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".