Renoprotective and blood pressure-lowering effect of dietary soy protein via protein kinase C βII inhibition in a rat model of metabolic syndrome
Bibliographic record
Abstract
We studied whether substitution of soy protein for casein can improve insulin sensitivity, lower blood pressure (BP), and inhibit protein kinase C betaII (PKCbetaII) activation in kidney in an acquired model of metabolic syndrome. Adult male rats were fed 4 different diets: (i) starch (60%) and casein (20%) (CCD), (ii) fructose (60%) and casein (20%) (FCD), (iii) fructose (60%) and soy protein (20%) (FSD), and (iv) starch (60%) and soy protein (20%) (CSD). Renal function parameters, BP, pressor mechanisms, PKCbetaII expression, oxidative stress, and renal histology were evaluated after 60 days. FCD rats displayed insulin resistance and significant changes in body weight, kidney weight, urine volume, plasma and urine electrolytes accompanied by significant changes in renal function parameters compared with CCD rats. Elevated BP, plasma angiotensin-converting enzyme (ACE) activity, renal oxidative stress, and reduced nitrite (NO) and kallikrein activity were observed. Western blot analysis revealed enhanced renal expression of membrane-associated PKCbetaII in the FCD group. Histology showed fatty infiltration and thickening of glomeruli while urinary protein profile revealed a 5-fold increase in albumin. Substitution of soy protein for casein improved insulin sensitivity, lowered BP and PKCbetaII activation and restored renal function. Antioxidant action, inhibitory effect on ACE and PKCbetaII activation, and increased availability of kinins and NO could be contributing mechanisms for the benefits of dietary soy protein.
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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 | 0.001 | 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".