Effects of chia (<i>Salvia hispanica</i> L.) on oxidative stress and inflammation in ovariectomized adult female <i>Wistar</i> rats
Bibliographic record
Abstract
The present study investigated the influence of chia consumption on inflammation, oxidative stress, and lipid profiles in adult female ovariectomized rats fed a high-fat diet. Forty ovariectomized and 40 intact (SHAM) rats were allocated into 8 groups (n = 10), and each rat received one of the following four diets: standard diet (ST); standard diet + chia (STC); high-fat diet (HF); and high-fat diet + chia (HFC) for 126 days. Biochemical parameters and biomarkers of lipid peroxidation, inflammation, and oxidative stress were evaluated. The mRNA expression levels of PPAR-α, NFκB, TNF-α and Zn-SOD1 were analyzed, as well as those of TNF-α and IL-1β. Chia intake increased HDL cholesterol (HDL-c) and reduced LDL cholesterol (LDL-c) levels. Plasma catalase activity was elevated in the STC group. Concentrations of TBARS were higher in all groups fed HF. PPAR-α mRNA expression was elevated, and levels of NFκB mRNA expression were reduced in the STC group. mRNA expression and protein levels of TNF-α were lower in rats fed the standard diet. Protein levels of IL-1β were reduced in rats fed the standard diet, and the high fat diet with chia. In general, ovariectomy did not influence the inflammatory and oxidative stress parameters. Chia intake improved antioxidant activity by increasing SOD expression, PPAR-α expression, catalase activity, and HDL-c levels. In addition, chia consumption decreased the concentrations of the inflammatory markers IL-1β and LDL-c.
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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) | 0.000 | 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".