Sensitivity of myofibroblasts to H<sub>2</sub>O<sub>2</sub>‐mediated apoptosis and their antioxidant cell network
Bibliographic record
Abstract
During wound healing, the transition from granulation to scar tissue shows a decrease in myofibroblast cellularity. Previous results have correlated the disappearance of these cells with the induction of apoptotic cell death by some unknown stimuli. In contrast, hypertrophic scar appearance after wound healing is thought to be linked to a disorder of apoptotic function which induces myofibroblast persistence in granulation tissue. Oxidative stress being an important mediator of apoptosis, we have evaluated the apoptotic response of normal and pathological wound myofibroblasts (WMyo and HMyo respectively) in their interaction with two oxidative stress inducers: hydrogen peroxide, using a high concentration as a single dose, and sodium ascorbate which induced a continuous release of H2O2 at a low concentration. Our results showed that, according to the H2O2 treatment type, HMyo were more sensitive (after ascorbate treatment) or less sensitive (after H2O2 treatment) when compared to WMyo and Fb. We next assessed the presence of several molecules known to be involved in the antioxidant network protecting cells against H2O2 injury and found HMyo to have a higher level of activity of glutathione peroxidase and a lower level of activity of catalase than WMyo. These results can help explain the contradictory responses of myofibroblasts according to the oxidative stress treatment. This is the first study linking refractory oxidative stress mediated cell death to cellular phenotype in hypertrophic myofibroblasts, and indicates a pivotal role for the antioxidant enzyme system in this type of resistance.
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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".