Supplementation of commercial pig feed with palm oil during pregnancy downregulates genes related to development and permeability in the offspring small intestine
Bibliographic record
Abstract
The passage of microbes from the gut flora into the systemic circulation via increased intestinal permeability has been linked to inflammation of adipose tissue and the onset of obesity. A porcine model of a maternal high‐fat diet (mHF) consumed during pregnancy, compared to a control diet (mC) was used to test the hypothesis that a high‐fat diet during pregnancy will adversely affect offspring gut development. Expression of claudin‐4 (CLDN‐4), occludin (OCLN) and zonulin‐1 (ZO‐1) were used as markers of epithelial tight junction permeability. Epidermal growth factor receptor (EGFr) was used as a marker of intestinal development. mRNA expression was measured using qPCR. Tissue was taken at 7d, and offspring were grouped by maternal diet and birth weight: mC median (n=6), mC small (n=7), mHF median (n=8), mHF small (n=7). Offspring birth weights were not affected by maternal diet, but mHF offspring had a significantly increased fractional growth rate from 0–7d (p<0.05). EGFr, CLDN‐4 and OCLN mRNA expression was 50% lower in mHF piglets (p<0.05). ZO‐1 expression was unaffected by maternal diet. These findings show that a high‐fat diet during pregnancy may increase intestinal permeability in the offspring, and adversely affect intestinal development. These changes may predispose to the inflammation of adipose tissue and the onset of obesity. Funding sources: NSERC, University of Nottingham.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".