Effects of Quercetin and Omega-3 Combination on Nuclear Factor Kappa B (NFκB) Expression Level in Pancreatic Tissue of Rats with Type-2 Diabetes Mellitus
Bibliographic record
Abstract
Background: Hyperglycemia increases nuclear factor kappa B (NFκB) expression and promotes cellular injury. Quercetin and omega-3 are expected to regulate NFκB expression. This study aims to measure the effect of combination therapy with quercetin and omega-3 in lowering the expression of NFκB in the pancreatic tissue of rats with type-2 DM as compared to those treated with monotherapy with either agent. Methods: This experimental study involved the use of a paraffin block of pancreatic tissue from 24 male Wistar rats aged 3 months, weighing between 250 g and 350 g. All rats underwent induction of type-2 DM and were divided into 4 groups: K1 (treated daily with placebo), K2 (treated with quercetin at 20 mg·kgBW-1·d-1), K3 (treated with omega-3 at 100 mg·kgBW-1·d-1), and K4 (treated with quercetin at 20 mg·kgBW-1·d-1 and omega-3 at 100 mg·kgBW-1·d-1). Treatments were administered orally for four weeks. Once the treatment was completed, samples of pancreatic tissue were collected for the measurement of the percentage of NFκB expression using immunohistochemical (IHC) staining. Results:The average level of NFκB expression in the pancreatic nuclei of DM rats treated with the combination of omega-3 and quercetin was significantly lower than that of those treated with placebo, quercetin only, or omega-3 only (p < 0.05). Conclusion: The combination of quercetin at 20 mg·kgBW-1·d-1 and omega-3 at 100 mg·kgBW-1·d-1 is significantly more effective in lowering the percentage of NFκB in pancreatic nuclei than monotherapy with either agent.
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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 | 0.000 | 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".