Dietary Lipids Modify Intestinal Lipid-Binding Protein RNA Abundance in Diabetic and Control Rats
Bibliographic record
Abstract
BACKGROUND: Lipid-binding proteins have been identified in the enterocyte, including the cytosolic intestinal and liver fatty acid binding proteins (I-FABP and L-FABP, respectively) as well as the brush border membrane fatty acid transporter (FAT). It is unclear whether variations in the type of dietary lipids or diabetes modify the RNA abundance of these proteins. Diabetes is associated with an increased intestinal lipid uptake, and the lipid uptake is greater in rats fed a semisynthetic saturated fatty acid (SFA) as compared with a polyunsaturated fatty acid (PUFA) diet. METHODS: Male Sprague-Dawley rats were injected with streptozotocin or control vehicle and fed chow or either SFA or PUFA for 2 weeks. Northern blotting was performed on RNA isolated from jejunal and ileal tissues. RESULTS: In controls, feeding SFA as compared with PUFA reduced the jejunal abundance of I-FABP and L-FABP RNA. In diabetic rats, feeding SFA increased the ileal FAT RNA. Feeding PUFA reduced jejunal L-FABP and ileal FAT RNA in diabetic rats as compared with controls. CONCLUSIONS: The enhanced lipid uptakes reported with feeding an SFA diet or with diabetes were not associated with parallel alterations in lipid-binding proteins. We speculate that these lipid-binding proteins act as a storage mechanism for lipids in enterocytes and are not directly involved in lipid uptake.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".