Gestational and postnatal protein deficiency affects postnatal development and histomorphometry of liver, kidneys, and ovaries of female rats’ offspring
Bibliographic record
Abstract
Pre- and postnatal protein deficiency may lead to decreased foetal intra-uterine development and postnatal growth, which is common in developing countries. The present study aimed to investigate the consequences of a low-protein intake during gestation and postnatally on adult female rats' offspring. Female rats were given either a control or a protein-deficient diet throughout the gestation and lactation periods. A subset of females was killed at day 20 of pregnancy for foetal and placental measurements. Another subset of females farrowed and the number, length, and weight of the offspring were measured. After weaning, the offspring received the same diet as their dams until 70 days of age. They were sacrificed, and some organs were weighed and collected for histomorphometrical analyses. Placental weight and size and foetal weight were lower in protein-deficient dams. The weight and length of pups at birth were also lower in the deficient group. The organs to body weight ratio were higher in the deficient animals at 70 days of age. The protein-deficient female offspring had a smaller ovarian area, greater numbers of primordial follicles and developing follicles per square millimetres of ovarian cortex, and no corpora lutea. The liver showed smaller nuclear diameter of the hepatocytes and height of the hepatocytes cords. The kidneys showed smaller cortical area with reduced glomerular number and diameter. These results provide the first evidence of the histomorphological changes of the association between gestational and postnatal protein deficiency in female rats' offspring.
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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 | 0.000 |
| 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".