Microparticles and Their Impact on Intestinal Immunity
Bibliographic record
Abstract
Microparticles are small (<1 µm), nonbiological particles that are used in many areas of daily life. As food additive they are used as anticaking agents or food colorants. The most common food-derived ingested compounds are aluminium silicate and titanium dioxide (TiO(2)), the latter being a white pigment used in toothpaste or sugar toppings. The increasing abundance of microparticles in the Western diet raises the question of the potential risks associated with gastrointestinal diseases such as Crohn's disease (CD). Accumulation of particles has been shown in cells of Peyer's patches, but it is not clear whether this also has pathological effects. NLRP3 is a member of the intracellular pattern recognition receptor family and it is part of the inflammasome, a multiprotein complex containing caspase-1 which activates the proinflammatory cytokines interleukin (IL)-1β and IL-18. With regard to recent findings identifying small particles such as asbestos and monosodium urate as NLRP3 activators, TiO(2) may be another potential target for inflammasome studies. We found that macrophage-like cells readily take up TiO(2) after 6 h. Incubation of cells with TiO(2) resulted in the assembly of NLRP3 with caspase-1. This inflammasome assembly correlated with secretion of IL-1β. In intestinal epithelial cells, TiO(2) also was found to be ingested. The counting of particles localized intracellularly revealed a dose-dependent increase of TiO(2)-positive cells. This points to the fact that in humans with a leaky intestinal barrier (such as IBD patients), TiO(2) microparticles may be taken up by macrophages and intestinal epithelial cells, may activate the inflammasome and induce IL-1β and IL-18 secretion. This may aggravate inflammation in susceptible individuals.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".