Protection of Intestinal Occludin Tight Junction Protein by Dietary Gangliosides in Lipopolysaccharide‐induced Acute Inflammation
Bibliographic record
Abstract
OBJECTIVES: Intestinal permeability and barrier function are regulated by expression of tight junction proteins. Lipopolysaccharide (LPS), tumor necrosis factor-alpha, and interleukin-1beta induce expression of nitric oxide (NO) and reduce the expression of gut tight junction proteins. The purpose of this study was to determine whether dietary gangliosides (GGs) increase the concentration of the anti-inflammatory cytokine interleukin-10 (IL-10) in response to LPS, thereby inhibiting NO production and protecting gut occludin tight junction protein from degradation. MATERIALS AND METHODS: Rats were fed semipurified diets with (n = 16) or without (n = 16) GGs (0.1% w/w of total lipid). After 2 weeks of feeding, animals were injected with saline (n = 8/diet group) or LPS (n = 8/diet group) (IP, 3 mg mL(-1) kg(-1)). Intestinal tissue, mucosa, and blood sample were collected 6 hours post-LPS exposure. The effect of dietary GGs on production/expression of IL-10, NO, inducible NO synthase, and occludin protein was determined. RESULTS: Dietary GGs increased IL-10 content in intestinal mucosa significantly by 32-fold (P < 0.0001) and in plasma by 2.4-fold (P < 0.001). Feeding animals a ganglioside-enriched diet decreased total NO content in intestinal mucosa and plasma by 44% and 30%, respectively, and inhibited inducible NO synthase expression following LPS exposure compared with control animals. Dietary GGs reduced the degradation of occludin tight junction protein in response to LPS. CONCLUSIONS: Dietary GGs inhibit degradation of gut occludin tight junction protein during LPS-induced acute inflammation. Thus, dietary GGs have a role in protecting the integrity of the intestinal barrier during acute gut inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".