Phytochemicals content, screening and antioxidant/pro-oxidant activities of Carapa procera (barks) (Meliaceae)
Bibliographic record
Abstract
Total phenolic, flavonoid and tannin contents together with antioxidant and pro-oxidant activities of Carapa procera were investigated. The antioxidant activity of Carapa procera was evaluated by 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay, ferric reducing/ antioxidant power (FRAP) method and 2,2′-azino bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) assay. The study of pro-oxidant activity was based on the power producing of iron ion in the Fenton reaction . The total phenolic content (TPC) of the extract was exhibiting high value of 8.2 mg gallic acid equivalents (GAE)/ g of dried Carapa. The study revealed a low percentage of flavonoids 0.04% in the extract and 0.24 mg of tannic acid equivalents (TAE)/g of dried Carapa for the tannin content (TC). Proanthocyanidins were less represented among the tannins 6.80% of the extract. The chromatographic fingerprints showed the presence of tannins and acid, like (+)-catechin, epicatechin , trans-4-hyroxicinnamic, 3,4-dihydroxicinnamic and acid chlorogenic . Carapa procera exhibited high antioxidant activity in the both DPPH and ABTS assays, being 10.45 mg Vitamin C equivalents/g of dried Carapa and 500 µmol Trolox/g of dried Carapa, respectively. The FRAP gave a good result 2.45 mg Vitamin C equivalents /g of dried Carapa. The pro-oxidant activity was 24.25 µg/ml of Carapa extract. The phytochemical screening was the subject of intensive investigations in order to identify the chemical constituents present in medicinal plants , such as Carapa procera and to evaluate their potential biological activities .
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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 | 0.001 | 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".