Improvement of hypertrophic scarring by using topical anti‐fibrogenic/anti‐inflammatory factors in a rabbit ear model
Bibliographic record
Abstract
This study investigates the scar-reducing efficacy of topical application of stratifin and acetylsalicylic acid (ASA) in a rabbit ear model. A total of five New Zealand white rabbits with four wounds per ear were examined. Either recombinant stratifin (0.002%) or ASA (0.5%) incorporated in carboxymethyl cellulose gel was topically applied on each wound at postwounding Day 5. Scars were harvested at postwounding Day 28 for histological analysis. The wounds treated with stratifin and ASA showed 82 and 73% reduction in scar volume, respectively, compared with that of untreated controls. A reduction of 57 and 41% in total tissue cellularity along with 79 and 91% reduction in infiltrated CD3+ T cells were observed in response to treatment with stratifin and ASA, respectively, compared with those of untreated controls. Wounds treated with stratifin showed a 2.8-fold increase in matrix metalloproteinase-1 expression, which resulted in a 48% decrease in collagen density compared with those of untreated controls. Qualitative wound assessment showed a reduced hypertrophic scarring in stratifin and ASA-treated wounds when compared with the controls. This study showed that topical application of either stratifin or ASA-impregnated carboxymethyl cellulose gel reduced hypertrophic scar formation following dermal injuries in a rabbit ear fibrotic model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".