Maternal nutrient restriction in guinea pigs as an animal model for studying growth-restricted offspring with postnatal catch-up growth
Bibliographic record
Abstract
We determined the impact of moderate maternal nutrient restriction (MNR) in guinea pigs with fetal growth restriction (FGR) on offspring body and organ weights, hypothesizing that FGR-MNR animals will show catch-up growth but with organ-specific differences. Guinea pig sows were fed ad libitum (Control) or 70% of the control diet from 4 weeks preconception, switching to 90% at midpregnancy (MNR). Control newborns >95 g [appropriate for gestational age (AGA); n = 37] and MNR newborns <85 g (FGR; n = 37) were monitored until neonatal (~25 days) or adult (~110 days) necropsy. Birth weights and body/organ weights at necropsy were used to calculate absolute and fractional growth rates (FRs). FGR-MNR birth weights were decreased ~32% compared with the AGA-Controls. FGR-MNR neonatal whole body FRs were increased ~36% compared with Controls indicating catch-up growth, with values negatively correlated to birth weights indicating the degree of FGR leads to greater catch-up growth. However, the increase in organ FRs in the FGR-MNR neonates compared with Controls was variable, being similar for the brain and kidneys indicating comparable catch-up growth to that of the whole body and twofold increased for the liver but negligible for the heart indicating markedly increased and absent catch-up growth, respectively. While FGR-MNR body and organ weights were unchanged from the AGA-Controls by adulthood, whole body growth rates were increased. These findings confirm early catch-up growth in FGR-MNR guinea pigs but with organ-specific differences and enhanced growth rates by adulthood, which are likely to have implications for structural alterations and disease risk in later life.
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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) | 0.000 | 0.000 |
| 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 | 0.000 |
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".