Dexamethasone and GLP‐2 Given to Lactating Rat Dams Influence Glucose Uptake in Suckling and Postweanling Offspring
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-2 (GLP-2) enhances intestinal absorption in adult animals. Glucocorticosteroids accelerate the ontogeny of the intestine and increase sugar uptake in adult animals. Modifying the maternal diet during lactation alters nutrient uptake in the offspring. The authors hypothesized that GLP-2 and dexamethasone, when administrated to lactating rat dams, enhance sugar uptake in the suckling and postweanling offspring. METHODS: Rat dams were treated during lactation with GLP-2 (0.1 microg/g/day subcutaneously [SC], twice daily), dexamethasone (0.128 microg/g/day SC, once daily), GLP-2 + dexamethasone (same doses), or placebo. The suckling offspring were sacrificed at 19-21 days of age, and the postweanlings were sacrificed 4 weeks later. Intestinal glucose and fructose uptake was assessed using an in vitro ring technique. RESULTS: GLP-2 and dexamethasone resulted in lower body weights, and dexamethasone caused intestinal atrophy in sucklings. The jejunal atrophy in sucklings given dexamethasone was prevented by GLP-2 + dexamethasone. In sucklings, the maximal transport rate and the Michaelis affinity constant for ileal glucose uptake were both increased by GLP-2 and GLP-2 + dexamethasone. In contrast, in postweanlings, the maximal transport rate for jejunal glucose uptake was reduced by dexamethasone and GLP-2, as was ileal fructose uptake. CONCLUSIONS: Treating lactating rat dams with GLP-2 or dexamethasone enhances glucose uptake in sucklings, but the late effect is a reduction in glucose and fructose absorption in postweanlings. The nutritional significance of these findings remains to be established.
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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) | 0.001 | 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".