Maternal protein restriction before and during pregnancy leads to a gestational day-dependent response of folliculogenesis in outbred mice
Bibliographic record
Abstract
Knowledge of follicle development during pregnancy under experimental conditions could be a key factor to understanding maternal ovarian activity. Thus, this study evaluated the effects of maternal protein restriction before and during pregnancy on folliculogenesis. Swiss outbred female mice were allocated to either a control (CC; 20% protein) or treated (TT; 8% protein) group. Pregnant females were killed either on Gestational day (GD) 7.5 or GD17.5 and the ovaries were evaluated using histomorphometric and immunohistochemical methods. TT females showed higher feed and energy intakes, but lower bodyweight gain at GD17.5 (P <0.05). They also had lower number of secondary follicles at GD7.5 and a higher proportion of primordial follicles at GD17.5 (P <0.05). In addition, the areas of the secondary follicles and their granulosa layer were smaller in the TT group on GD7.5, whereas the areas of the oocyte and granulosa layer from atretic follicles were larger (P <0.05). Notwithstanding the slight increase in the insulin-like growth factor 1 (IGF1) receptor expression on GD7.5 in the TT group, there was a marked reduction in IGF1 expression detected in secondary follicles on GD17.5 (P <0.05). Collectively, these results demonstrate that protein restriction during pregnancy negatively affects follicle quality by reducing the size and activation capacity, which is more severe in late pregnancy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".