Low dietary protein increases vitamin A absorption and deposition into milk in periparturient rats
Bibliographic record
Abstract
Abstract: Our objective was to study the effect of differing dietary crude protein and vitamin A on retinoid metabolism in a periparturient rat model. Sixty female rats, approximately 21 d before parturition, were fed rations containing either low protein (13%; LP) or high protein (22%; HP) crude protein and either low vitamin A (3 IU/g; LA) or high vitamin A (5 IU/g; HA), yielding treatments HPHA, HPLA, LPHA, and LPLA. Samples were collected at d −14, d +3, and +10 relative to parturition and analyzed for all-trans retinoid acid (RA), 13-Cis RA, and retinol. At d −14, serum all-trans RA concentrations decreased compared to baseline. At both d +3 and d +10, serum retinol increased and liver 13-Cis RA decreased. In the small intestine, 13-cis RA was higher in HPHA than HPLA pre-partum (0.93±0.12 vs. 0.40±0.12 ng/ml, P=0.04). Post-partum, 13-cis RA was lower in high vitamin HPHA and LPHA groups (0.35±0.06 and 0.38±0.06 ng/ml) than in low vitamin A HPLA and LPLA treatments (0.50±0.06 and 1.32±0.06 ng/ml, P<0.01). In rats fed LA diets, TNF-alpha expression tended to be lower in HPLA than LPLA groups on day +3 (0.69±0.34 vs 1.00±0.52, P=0.08), but not day +10 (0.56±0.25 vs. 1.00±0.49 fold change, P>0.10). Retinoids accumulated during pregnancy and were mobilized during lactation. The sequestration of retinoids was increased when dietary protein content was low. Further studies are needed to investigate how retinoid metabolism could be manipulated to improve vitamin A delivery to milk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".