Prophylactic effects of pomegranate (<i>Punica granatum</i>) juice on sodium fluoride induced oxidative damage in liver and erythrocytes of rats
Bibliographic record
Abstract
The objective of this study was to investigate the protective effects of pomegranate (Punica granatum) juice (PGJ) on oxidative damages in liver tissue and erythrocytes of rats intoxicated by sodium fluoride (NaF). Rats were randomly divided into two groups: group I received standard diet and group II received orally 1 mL of PGJ. After 5 weeks of pretreatment, each group was divided again into two subgroups and treated for another 3 weeks as follows: group I was subdivided into a control group and a group that was treated with 100 ppm of NaF (in drinking water); group II was subdivided into one group that was treated daily with both 100 ppm NaF and PGJ (1 mL orally) and one that received daily 1 mL of pomegranate juice. Exposure to NaF decreased hematological parameters, changed the total protein, albumin, bilirubin levels, and increased the activities of hepatic marker enzymes. We also noted an increase in lipid peroxidation contents, accompanied by a decrease of reduced glutathione levels. Antioxidant enzyme activities in both tissues were modified in the NaF group compared with the control group. However, the administration of PGJ juice caused an amelioration of the previous parameters. Our results indicated the potential effects of NaF to induce oxidative damage in tissues and the ability of PGJ to attenuate NaF-induced oxidative injury.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".