Granzyme B contributes to impaired wound healing during chronic inflammation in apolipoprotein E knockout mice (P3036)
Bibliographic record
Abstract
Abstract Granzyme B (GzmB) is a serine protease expressed by immune cells during chronic inflammation and is capable of degrading extracellular matrix (ECM) proteins such as fibronectin (FN). Apolipoprotein E (ApoE) is a protein known to suppress inflammation and is expressed in skin. ApoE knockout (KO) mice develop an inflammatory skin phenotype when fed a high fat diet leading to impaired wound healing. We hypothesized that excessive GzmB activity contributes to impaired healing in ApoE KO mice through the degradation of ECM. Wild type (WT), ApoE KO and ApoE/GzmB double knockout (DKO) mice were fed a high fat diet for 30 weeks and given a 1 cm diameter skin wound on their mid dorsum. Wounds were allowed to heal for 16 days at which point the wounded tissue was harvested. All WT mouse wounds showed complete closure after 16 days while only 40% achieved closure in ApoE KO mice. Wound contraction was also slower in ApoE KO mice compared to WT controls (P<0.05). Histological analysis demonstrated persistent inflammation in ApoE KO mouse wounds at Day 16 featuring neutrophils, macrophages, T cells and GzmB. Wounded skin from ApoE KO mice also contained a FN fragment of similar size to that produced by GzmB in vitro. Compared to ApoE KO mice, DKO mice showed faster contraction (P<0.05) and better healing with 80% of wounds achieving closure by Day 16. These results suggest GzmB contributes to impaired wound healing in ApoE KO mouse wounds, possibly through excessive degradation of FN.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".