A comparison of the effects of fish oil and flaxseed oil on cardiac allograft chronic rejection in rats
Bibliographic record
Abstract
Both fish and flaxseed oils are major sources of different n-3 fatty acids. Beneficial effects of fish oil on posttransplantation complications have been reported. The current study aimed to compare the effects of flaxseed and fish oils in a rat cardiac allograft model. Male Fischer and Lewis rats were used as donors and recipients, respectively, to generate a heterotopic cardiac allograft model. Animals were randomly assigned into three groups and fed a diet supplemented with 1) 5% (wt/wt) safflower oil (control, n = 7), 2) 5% (wt/wt) flaxseed oil (n = 8), or 3) 2% (wt/wt) fish oil (n = 7), and an intraperitoneal injection of cyclosporine A (CsA; 1.5 mg.kg(-1).day(-1)) over 12 wk. Body weight, blood pressure, plasma levels of lipids, CsA, select cytokines, as well as graft function and chronic rejection features were assessed. Body weight and blood CsA levels were similar among the groups. Relative to controls, both treated groups had lower systolic and diastolic blood pressure and plasma levels of macrophage chemotactic protein-1. Treatment with fish oil significantly (P < 0.05) lowered plasma levels of triglycerides, total cholesterol, and LDL-cholesterol. HDL-cholesterol concentrations were significantly higher (P < 0.05) in the flaxseed oil-treated group compared with the other two groups. Both flaxseed oil and fish oil may provide similar biochemical, hemodynamic, and inflammatory benefits after heart transplantation; however, neither of the oils was able to statistically significantly impact chronic rejection or histological evidence of apparent cyclosporine-induced nephrotoxicity in this model.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".