Dietary Fat Content Influences Uptake of Hexoses and Lipids into Rabbit Jejunum following Heal Resection
Bibliographic record
Abstract
After 6 weeks' feeding on a high-fat or low-fat diet, the in vitro uptake of hexoses and lipids was measured in control rabbits with an intact intestinal tract, and in animals submitted to the surgical removal of the distal half of their small intestine. Jejunal villus height, villus surface area and mucosal surface area were higher in unresected control rabbits fed the low- as compared with the high-fat diet, whereas dietary fat content had no effect on villus morphology in resected animals. Mucosal surface area was similar in control and in resected animals fed the high-fat diet, but was lower in resected than in control animals fed the low-fat diet. The active and passive transport properties of the jejunum were influenced by dietary fat manipulation. These absorption changes were qualitatively and/or quantitatively different in animals with an ileal resection from those in animals with an intact small intestine. Dietary fat manipulation had a different effect on the uptake of each lipid probe. The effective resistance of the intestinal unstirred water layer also adapted to changes in the dietary content of fat, but the changes in uptake of hexoses, fatty acids and cholesterol cannot be simply explained by alterations in this diffusion barrier, or by changes in the villus morphology. These findings indicate the importance of dietary fat on villus structure and transport function and their adaptation to ileal resection.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".