Effects of curcumin on brain-derived neurotrophic factor levels and oxidative damage in obesity and diabetes
Bibliographic record
Abstract
We evaluated the effects of curcumin treatment on protein oxidation (PO), lipid peroxidation (LP) and brain-derived neurotrophic factor (BDNF) levels in the hippocampus and frontal cortex (FC) of diabetic db/db mice (DM) and in sera of obese humans. Thus, DM were treated daily with 50 mg/kg of curcumin during an 8-week period. Obese human were treated daily with 500 and 750 mg of curcumin that was administered orally for 12 weeks; BDNF, PO and LP levels in sera were determined at in weeks 0, 2, 6 and 12 of treatment. BDNF levels decreased in hippocampus and FC of DM as compared with untreated wild-type mice. Curcumin improved or restored BDNF levels to normal levels in DM, but curcumin did not have any effect on BDNF levels in sera of obese humans. In hippocampus and FC of DM, hyperglycaemia and curcumin did not have effect on LP levels. Hyperglycaemia increased PO levels in hippocampus and FC, whereas curcumin decreased these levels in hippocampus but not in FC. In sera of obese humans, the 500-mg dose decreased LP levels in weeks 6 and 12 when compared with basal levels, but the 750-mg dose did not have any effect; both doses of curcumin decreased PO levels in weeks 2, 6 and 12 of treatment when compared with basal levels. Present results suggest a therapeutic potential of curcumin to decrease oxidation caused by obesity in humans and also show that curcumin restores BDNF levels in DM.
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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) | 0.001 | 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".