Intradermal tacrolimus prevent scar hypertrophy in a rabbit ear model: a clinical, histological and spectroscopical analysis
Bibliographic record
Abstract
BACKGROUND: Keloids and hypertrophic scars (HSc) affect 4.5-16% of the population. Thus far, the different approaches of keloid treatment are not very efficient, with a 50% relapse rate and many ongoing researches are looking for simple, safe and more efficient therapeutic methods. Tacrolimus is an immunomodulator that could be useful in treating keloid. OBJECTIVES: The objective of this study is to evaluate the effectiveness of Tacrolimus in inhibiting HSc formation on rabbits' ears model and to check optical skin spectroscopy in tissue characterization. METHODS: Our study was carried out on 20 New-Zealand female white rabbits. HSc were obtained by wounding rabbits' ear. These wounds were treated with intradermal injections of tacrolimus (0.2-0.5 mg/cm(2)) or a vehicule. The assessment of treatment efficacy was performed by clinical examinations, histological assay and skin spectrometry. RESULTS: Tacrolimus did not induce general or local side-effects. The scar elevation index in treated subjects was half less than that of the untreated ones. Furthermore, dermal thickness and inflammatory cellular density were both significantly smaller for treated scars than for the control ones. In vivo optical skin spectroscopy can characterize hypertrophic and normal skin with high sensibility and specificity. CONCLUSION: Intradermal injection of tacrolimus at 0.5 mg/cm(2) is an efficient way to prevent HSc in our experiment model and its tolerance is correct. Optical spectroscopy could be a good non-invasive tool to evaluate HSc treatment. These promising results might be proposed for patients suffering from keloid.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".