Renal cyclooxygenase and lipoxygenase products are altered in polycystic kidneys and by dietary soy protein and fish oil treatment in the Han:SPRD‐<i>Cy</i>rat
Bibliographic record
Abstract
SCOPE: Dietary fish oil (FO) and soy protein (SP) are two interventions that slow disease progression in the Han:SPRD-Cy rat model of polycystic kidney disease (PKD). Inhibition of cyclooxygenase (COX)-derived eicosanoids also reduces disease progression, but the role of lipoxygenase (LOX) products in this disease is not known. METHODS AND RESULTS: Since dietary FO and SP have been shown to alter eicosanoid formation via differing mechanisms, Han:SPRD-Cy rats were given diets containing either casein protein (CP) or SP, and soy oil (SO) or FO. Analysis of eicosanoids revealed that renal COX products were higher and LOX products were lower in diseased kidneys. SP feeding resulted in lower COX products, activity and COX1 protein and higher LOX products in the diseased kidneys in parallel with reduced renal cyst growth and fibrosis. By comparison, FO reduced both COX and LOX products produced from n-6 fatty acids and increased 3-series prostanoids in both normal and diseased cortex and medulla, but these differences did not parallel effects on disease. CONCLUSION: Renal COX-derived eicosanoids are elevated and LOX products are reduced in this model of kidney disease. The effects of dietary SP, but not FO, on renal eicosanoids parallel the effects on disease.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".