Extracts of Brocchia cinerea (Delile) Vis Exhibit In Vivo Wound Healing, Anti-Inflammatory and Analgesic Activities, and Other In Vitro Therapeutic Effects
Bibliographic record
Abstract
The plant Brocchia cinerea (Delile) (B. cinerea) has many uses in traditional pharmacology. Aqueous (BCAE) and ethanolic extracts (BCEE) obtained from the aerial parts can be used as an alternative to some synthetic drugs. In vitro, DPPH, FRAP and TAC are three tests used to measure antioxidant efficacy. Antibacterial activities were determined against one Gram positive and two Gram negative strains of bacteria. The analgesic power was evaluated in vivo using the abdominal contortion model in mice, while carrageenan-induced edema in rats was the model chosen for the anti-inflammatory test; wound healing was evaluated in an experimental second degree burn model. The results of the phytochemical analysis showed that BCEE had the greatest content of polyphenols (21.06 mg AGE/g extract), flavonoids (10.43 mg QE/g extract) and tannins (24.05 mg TAE/g extract). HPLC-DAD reveals the high content of gallic acid, quercetin and caffeic acid in extracts. BCEE has a strong antiradical potency against DPPH (IC50 = 0.14 mg/mL) and a medium iron reducing activity (EC50 = 0.24 mg/mL), while BCAE inhibited the growth of the antibiotic resistant bacterium, P. aeruginosa (MIC = 10 mg/mL). BCAE also exhibited significant pharmacological effects and analgesic efficacy (55.81% inhibition 55.64% for the standard used) and the re-epithelialization of wounds, with 96.91% against 98.60% for the standard. These results confirm the validity of the traditional applications of this plant and its potential as a model to develop analogous drugs.
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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) | 0.001 | 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".