Effect of Topical Application of Hydro-Ethanolic Extract of Acacia nilotica Pods on Second-Degree Cutaneous Infected Burns
Bibliographic record
Abstract
Acacia nilotica is a medicinal plant used traditionally in Togo to treat various diseases including burn wounds. The aim of this study was to evaluate experimentally Acacia nilotica burn wound healing effect.Cutaneous burn wounds were symmetrically caused on depilated areas of rat skin through contact with an aluminum bar (r = 10 mm), preheated at 80°C for 30 sec. Five groups of animal were constituted and each group contained 8 mice. Four groups of animal’s burn wounds were infected by Staphylococcus aureus. One group burn wound is uninfected and serves as negative control. Burn wounds were assessed by planimetry and histological parameters of healing. Twelve days after burn wound induction, wound contraction in the uninfected groups (negative control) was 19.9% for topical application against -2.43% for infected control. In the infected groups treated with extract, wound contraction was generally stimulates. Histological examination showed granulated tissue developing over the wounds treated with the extract of A. nilotica at 2.5% and 5% where the proliferation of fibroblasts and neo-blood vessels was very marked.In conclusion, A. nilotica pod contents tannin, flavonoids, alkaloids and protein reduces DPPH solution and significantly accelerates wound healing of burns, and this is the case even if wounds are infected with 109CFU/mL of S. aureus.
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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".