Curcumin improves the metabolic syndrome in high-fructose-diet-fed rats: role of TNF-α, NF-κB, and oxidative stress
Bibliographic record
Abstract
This study aimed to investigate effects of curcumin on high fructose diet (HFD)-induced metabolic syndrome (MetS) in rats and the possible mechanisms involved. MetS was induced in male albino rats (n = 20), over 8 weeks, by 65% HFD. For 8-week experiment period, rats were assigned to 2 equal groups: curcumin-treated rats received curcumin (200 mg/kg, p.o, once daily) along with HFD, and untreated rats were fed with HFD only. We evaluated body mass (BM), systolic blood pressure (SBP), homeostasis model assessment of insulin resistance (HOMA-IR), and serum levels of glucose, insulin, leptin, total cholesterol (TC), triglycerides (TGs), uric acid, malondialdehyde (MDA; lipid peroxidation product), and tumor necrosis factor-α (TNF-α; inflammatory cytokine), and serum catalase (endogenous antioxidant) activity and immunohistochemical expression of nuclear factor κB (NF-κB; inflammation-related transcription factor) in hepatocytes. HFD produced increases in BM, SBP, HOMA-IR, and serum levels of glucose, insulin, leptin, TC, TGs, uric acid, MDA, and TNF-α, a decrease in catalase activity, and strong positive expression of NF-κB in hepatocytes. Curcumin, in presence of HFD, produced significant improvements in all glucose and fat metabolism parameters, and in oxidative stress and inflammation biomarkers. Curcumin may potentially be useful in the treatment of MetS through its ability to modulate oxidation stress status and inflammation cascades.
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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 | 0.000 | 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 | 0.001 |
| 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".