Hypolipidemic and antioxidant effects of curcumin and capsaicin in high-fat-fed rats
Bibliographic record
Abstract
The beneficial hypolipidemic and antioxidant influences of the dietary spice compounds curcumin and capsaicin were evaluated. Curcumin, capsaicin, or their combination were included in the diet of high-(30%)-fat-fed rats for 8 weeks. Dietary high-fat-induced hypertriglyceridemia was countered by dietary curcumin, capsaicin, or their combination by 12%-20%. Curcumin, capsaicin, and their combination also produced a slight decrease in serum total cholesterol in these animals. Serum alpha-tocopherol content was increased by dietary curcumin, capsaicin, and their combination in high-fat-fed rats. Serum total thiol content in high-fat-fed animals and serum ascorbic acid in normal animals was elevated by the combination of curcumin and capsaicin. Hepatic glutathione was increased by curcumin, capsaicin, or their combination in normal animals. Hepatic glutathione and alpha-tocopherol were increased, whereas lipid peroxide level was reduced by dietary curcumin and combination of curcumin and capsaicin in high-fat-fed animals. Serum glutathione peroxidase and glutathione transferase in high-fat-fed rats were generally higher as a result of dietary curcumin, capsaicin, and the combination of curcumin and capsaicin. Hepatic glutathione reductase and glutathione peroxidase were significantly elevated by dietary spice principles in high-fat-fed animals. The additive effect of the 2 bioactive compounds was generally not evident with respect to hypolipidemic or antioxidant potential. However, the effectiveness of the combination was higher in a few instances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".