Excision Wound Healing and Antioxidant Activity of Different Root Extract of <i>Plumbago indica</i>
Bibliographic record
Abstract
The present study was to evaluate wound healing activity of ethanolic, chloroform and hexane extracts of root of Plumbago indica using excision wound model and also in its antioxidant activity. The tested animals were divided into five groups with five in each group 10% w/v of each extract was prepared in normal saline with 0.1% propylene glycol. The anti-oxidant activities of different root extracts were investigated including scavenging activity of hydrogen peroxide, DPPH and free radicals of superoxide anion in vitro . Ethanol extract of Plumbago indica were found to possess better wound healing activity than the chloroform and hexane extract, and it was more effective than the commercial wound healing agent Burnol tried in experiments with complete wound healing on the 9 th day. It showed rapid epithelization, better wound closure and reduction of infection. Hexane extract also had good wound healing capacity while the chloroform extract was found to be ineffective as a wound healing agent. Biochemical analysis post wound healing showed normal counts while the differential WBC counts was the higher in the mice treated with chloroform extract and it was even higher in the untreated mice. The results showed that the ethanolic extract has powerful antioxidant potential and hexane extract showed some antioxidant activity while chloroform extract was ineffective as an antioxidant. The ethanolic extract of Plumbago indica had good wound healing capacity and also showed good antioxidant property than the other extracts.
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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".