Human keratinocyte (HaCaT) stimulation and healing effect of the methanol fraction from the decoction from leaf from <i>Sideroxylon obtusifolium</i> (Roem. & Schult.) T.D. Penn on experimental burn wound model
Bibliographic record
Abstract
Abstract The larger number of plants, with therapeutic potential, popularly used in Northeastern Brazil is due to their easy access and the great Brazilian biodiversity. Previously, was demonstrated that the methanol fraction from Sideroxylon obtusifolium (MFSOL) promoted an anti-inflammatory and healing activity in excisional wounds. Thus, this work aimed to investigate the healing effects of MFSOL on human keratinocytes cells (HaCaT) and experimental burn model injuries. HaCaT cells were used to investigate migration and proliferation of cell rates. Female Swiss mice were subjected to second-degree superficial burn protocol and divided into four treatment groups: Vehicle (cream-base), 1.0% Silver Sulfadiazine (Sulfa), and 0.5% or 1.0% MFSOL cream (CrMFSOL). Samples were collected for quantification of the inflammatory mediators and histological analyses after 3, 7 and 14 days on evaluation. As result, MFSOL (50 μg/ml) stimulated HaCaT cells by increasing proliferation and migration rates. Moreover, CrMFSOL 0.5% attenuated myeloperoxidase (MPO) activity and also stimulated the release of IL-1β and IL-10, after 3 days with treatment. CrMFSOL 0.5% enhanced wound contraction, promoted tissue remodeling improvement and highest collagen production after 7 days, and VEGF release after 14 days. Therefore, MFSOL evidenced the stimulation of human keratinocyte (HaCaT) cells and improvements on wound healing via inflammatory modulation on burn injuries.
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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) | 0.002 | 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".