P0149 PP DIETARY GANGLIOSIDE INHIBITS PRO‐INFLAMMATORY SIGNALS, PAF, PGE2, LTB4, TNF‐ALPHA AND IL‐1BETA IN THE INFLAMED INTESTINE BY ALTERING THE STRUCTURE AND FUNCTION OF MICRODOMAINS
Bibliographic record
Abstract
Introduction: Cholesterol and sphingolipids enriched micro-domains are important for modulating cellular entry of enterotoxins. Cholesterol depletion by drugs inhibits entry of pathogens by disrupting microdomain structure. Our previous study showed that dietary ganglioside (GG) increased intestinal total GG and decreased cholesterol content. Thus we hypothesized that dietary GG will reduce cholesterol content in intestinal microdomains, thereby altering microdomain structure resulting in anti-inflammatory effects. Methods: To test this hypothesis, we determined if dietary GG decreased microdomain cholesterol and caveolin content, and reduced the levels of pro-inflammatory mediators such as PAF, PGE2, LTB4, TNF-alpha and IL-1beta in inflamed mucosa and blood after inducing inflammation with LPS. Weanling rats were fed semi-purified diets with or without (Control) 0.1% (w/w) GG. After 2 wks of feeding, animals were injected with LPS (ip, 3mg/kg body wt) to induce inflammation of the gut. Intestinal mucosa was collected after 6h for caveolin, cytokine, lipid, and eicosanoid analysis. Results: Feeding animals the GG diet showed increased total GD3 and decreased cholesterol content in microdomains with a concomitant decrease of 50% in caveolin content compared to control animals. Animals fed the GG diet resulted in remarkably lower levels of PAF (decreased by 45%), PGE2 and LTB4 (decreased by 20%) in inflamed mucosa and TNF-alpha (decreased by 55%) and IL-1beta (decreased by 40%) in peripheral blood compared to control animals. Conclusion: The present study indicates that dietary GG has important anti-inflammatory signals during endotoxin challenge.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".