Distinctive effects of plant protein sources on renal disease progression and associated cardiac hypertrophy in experimental kidney disease
Bibliographic record
Abstract
SCOPE: Dietary soy protein reduces renal disease progression in a number of renal diseases, suggesting that plant compared with animal proteins may be renoprotective. The inclusion of other plant protein sources could enhance compliance of intervention diets, but the effects of other plant protein sources are not known. METHODS AND RESULTS: Weanling Han:SPRD-cy rats with experimental polycystic kidney disease were given hemp-, pea- and soy protein-based diets compared with the standard AIN 93G diet with casein as the protein source. Kidneys from diseased rats given diets which contained soy or hemp protein compared with casein-based diets were less enlarged, had lower fluid content, smaller cyst volumes, less fibrosis, lower chemokine receptor 2 (CCR2) levels and normalized serum creatinine levels. Soy and hemp protein diets also normalized heart size, which was enlarged in diseased compared with normal rats consuming casein. Kidneys from diseased rats given pea protein compared with casein were more enlarged and had higher fluid content and cyst volumes, despite growing better and having lower serum creatinine and renal chemokine receptor 2 levels, and similar levels of renal fibrosis. CONCLUSION: Not all plant proteins are equally protective in experimental kidney disease and associated cardiac hypertrophy.
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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) | 0.000 | 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".