Granzyme B contributes to extracellular matrix degradation in UV‐treated skin (1046.1)
Bibliographic record
Abstract
Extracellular matrix (ECM) degradation is a hallmark of many chronic inflammatory diseases that can lead to a loss of function, aging and disease progression. Granzyme B (GzmB), a serine protease that is expressed by a variety of cells, has been shown to accumulate in the extracellular space during chronic inflammation and cleave a number of ECM proteins. Using a model of UV‐induced chronic inflammation in the skin, we hypothesized that GzmB contributes to ECM degradation through both direct cleavage of ECM proteins and indirectly through the induction of other proteinases, which leads to a phenotype of aged skin. Wild‐type and GzmB‐knockout (KO) mice were repeatedly exposed to minimal erythemal doses of solar simulated UV‐irradiation for up to 20 weeks. GzmB expression was significantly increased in wild‐type treated skin compared to controls. GzmB deficiency significantly protected against the formation of wrinkles and the loss of dermal collagen density, which was related to the cleavage of decorin, an abundant proteoglycan involved in collagen fibrillogenesis and integrity. GzmB also cleaved fibronectin, and fibronectin fragments have been shown to increase the expression of collagen‐degrading matrix metalloproteinases (MMPs) in fibroblasts. Collectively, these findings indicate a significant role of GzmB in ECM degradation, which may have implications in many chronic inflammatory diseases. Grant Funding Source : Supported by CIHR
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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 | 0.001 |
| 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".