Fish oil supplementation attenuates changes in plasma lipids caused by dexamethasone treatment in rats
Bibliographic record
Abstract
Dexamethasone is an anti-inflammatory glucocorticoid that may alter glucose and lipid homeostasis when administered in high doses or for long periods of time. Omega-3 fatty acids, present in fish oil (FO), can be used as potential modulators of intermediary glucose and lipid metabolism. Herein, we evaluate the effects of FO supplementation (1 g·kg(-1) body weight (BW)) on glucose and lipid metabolism in rats treated with dexamethasone (0.5 mg·kg(-1) BW) for 15 days. Adult male Wistar rats were distributed among 4 groups: control (saline, 1 mL·kg(-1) BW and mineral oil, 1 g·kg(-1) BW), DEX (dexamethasone and mineral oil), FO (fish oil and saline), and DFO (fish oil and dexamethasone). Dexamethasone and saline were administered intraperitoneally, and fish oil and mineral oil were administered by gavage. We evaluated functional and molecular parameters of lipid and glycemic profiles at 8 days and at the end of treatment. FO supplementation increased hepatic docosahexaenoic acid (DEX: 5.6% ± 0.7%; DFO: 10.5% ± 0.8%) and eicosapentaenoic acid (DEX: 0.3% ± 0.0%; DFO: 1.3% ± 0.1%) contents and attenuated the increase of plasma triacylglycerol, total cholesterol, and non-high-density lipoprotein cholesterol concentrations in DFO rats compared with DEX rats. These effects seem not to depend on hepatic expression of insulin receptor substrate 1, protein kinase B, peroxisome proliferator-activated receptor γ coactivator 1-α, and peroxisome proliferator-activated receptor γ. There was no effect of supplementation on body weight loss, fasting glycemia, and glucose tolerance in rats treated with dexamethasone. In conclusion, we show that FO supplementation for 15 days attenuates the dyslipidemia induced by dexamethasone treatment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".