Dietary soya protein during pregnancy and lactation in rats with hereditary kidney disease attenuates disease progression in offspring
Bibliographic record
Abstract
Dietary soya protein substitution for casein initiated at weaning slows disease progression in animal models of chronic renal disease. As there is increasing evidence that fetal programming can have a significant impact on kidney physiology and function in offspring, the objective of the current study was to determine whether exposure to soya protein in the diet earlier than weaning would have further benefits. Han:SPRD-cy (cy/+) breeder rats were fed a casein-based or soya protein-based diet 2 weeks prior to mating, throughout pregnancy and during lactation. Following this maternal period, 3-week-old pups were given either the same or the alternate diet for a 7-week weaning period. Dietary soya protein compared with casein in the maternal or weaning period both independently resulted in less renal inflammation (macrophage infiltration lower by 24% (P=0.0003) and 32% (P<0.001), respectively). When soya protein was given in both feeding periods, the effect was additive. Soya protein substitution for casein resulted in less oxidative damages as indicated by 28% lower oxidized-LDL staining (P=0.013) when present in the maternal period, or in the weaning period (by 56%, P<0.0001). Renal cell proliferation was reduced by 29-33% (P<0.05) in rats given soya protein whether the exposure was during the maternal or weaning period. Soya protein compared with casein in the maternal period also resulted in 33% (P=0.0013) less proteinuria, indicating superior renal function. Dietary soya protein during pregnancy and lactation represents a potential preventative approach in treating for those with congenital kidney diseases.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".