A maternal low-protein diet and neonatal overnutrition result in similar changes to glomerular morphology and renal cortical oxidative stress measures in male Wistar rats
Bibliographic record
Abstract
There is a strong correlation between inadequate gestational and postpartum nutrition and the occurrence of cardiovascular diseases. The present study investigated the effects of a maternal low-protein diet and neonatal overfeeding on the oxidative balance and morphology of the renal cortex of male Wistar rats. Two independent protocols were used. First, pregnant Wistar rats received diets containing either 17% (normal protein) or 8% (low protein) casein throughout pregnancy and lactation. Second, the litter size was reduced by one-third on the third postnatal day to induce overnourishment in offspring. At 30 days, the oxidative balance and morphology of the renal cortex were analyzed. There was a small but significant increase in renal corpuscle area in the low protein (LP, 5%) and overnutrition (ON, 8%) groups. Glomerular tuft area also increased in LP (6%) and ON (9%), as did glomerular cellularity (LP, +11%; ON, +12%). In the oxidative stress analyses, both nutritional insults significantly elevated lipid peroxidation (LP, +18%; ON, +135%) and protein oxidation (LP, +40%; ON, +65%) while significantly reducing nonenzymatic antioxidant defenses, measured as reduced glutathione (LP, -32%; ON, -45%) and total thiol content (LP, -28%; ON, -24%). We also observed a decrease in superoxide dismutase (LP, -78%; ON, -51%), catalase (LP, -18%; ON, -61%), and glutathione S-transferase (only in ON, -44%) activities. Our results demonstrate that nutritional insults, even those of a very different nature, during perinatal development can result in similar changes in oxidative parameters and glomerular morphology in the renal cortex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".