Soy Protein Alleviates Hypertension and Fish Oil Improves Diastolic Heart Function in the Han:SPRD‐<i>Cy</i> Rat Model of Cystic Kidney Disease
Bibliographic record
Abstract
Abnormalities in cardiac structure and function are very common among people with chronic kidney disease, in whom cardiovascular disease is the major cause of death. Dietary soy protein and fish oil reduce kidney disease progression in the Han:SPRD-Cy model of cystic renal disease. However, the effects of these dietary interventions in preventing alterations in cardiac structure and function due to kidney disease (reno-cardiac syndrome) in a cystic kidney disease model are not known. Therefore, weanling Han:SPRD-Cy diseased (Cy/+) and normal (+/+) rats were given diets containing either casein or soy protein, and either soy or fish oil in a three-way design for 8 weeks. Diseased rats had larger hearts, augmented left ventricular mass, and higher systolic and mean arterial blood pressure compared to the normal rats. Assessment of cardiac function using two-dimensional guided M-mode and pulse-wave Doppler echocardiography revealed that isovolumic relaxation time was prolonged in the diseased compared to normal rats, reflecting a diastolic heart dysfunction, and fish oil prevented this elevation. Soy protein resulted in a small improvement in systolic and mean arterial pressure but did not improve diastolic heart function, while fish oil prevented diastolic heart dysfunction in this model of cystic kidney 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".