Antioxidant Effect of Physalis Peruviana Fruit Aqueous Extract − The Antioxidant Effect of Physalis
Bibliographic record
Abstract
The acid 2,4-dichlorophenoxyacetic (2,4-D), agrochemical widely applied to the improvement of the agricultural productivity, is recognized as extremely toxic, once that its effects are accompanied by the occurrence of oxidative stress. On this context it becomes fundamental to explore components that are able to reduce the damages caused to the organism by this pesticide. The P. Peruviana, is a plant known that it presents components which contribute to the neutralization of the reactive species. Thus, this study had as purpose to evaluate the effect of P. Peruviana fruit aqueous extract on the bio-markers of oxidative stress in erythrocytes exposed to the 2,4-D. The exposition of samples to the 2,4-D has been done, followed to the treatment of these ones with different concentrations of the P. Peruviana Extract (1; 10; 25; 50 e 83 g/L). The results show an increase of the TBARS, PCs, and GSH after the exposure to the 2,4-D. On the other hand, after the treatment of the samples there was a reduction of the PCs and GSH levels in all the treated groups, and a decrease of the lipid peroxidation levels on the groups that were exposed to the Extract on the concentrations of 1 and 10g/L. The results show that the P. Peruviana owns an effect on the antioxidant system of the organism, viewing that it stimulated the consumption of GSH and thus it was able to fix damages in lipids and proteins provoked by 2,4-D specially on the concentrations of 1 and 10 g/L.
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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".