Modulation of cytokine release from colonic explants by bacterial antigens in inflammatory bowel disease
Bibliographic record
Abstract
The intestinal flora play an important role in experimental colitis and inflammatory bowel disease (IBD). Using colonic explant cultures from 132 IBD and control subjects, we examined tumour necrosis factor-alpha (TNF-alpha), interleukin (IL)-1 and interleukin-1 receptor antagonist (IL-1RA) production in vitro in response to bacterial activators. Unstimulated TNF-alpha release was increased significantly in rectal biopsies from involved IBD tissue, correlating with inflammation severity. Whereas lipopolysaccharide (LPS) only moderately stimulated TNF-alpha production from inflamed tissue, pokeweed mitogen (PWM) induced its release in all groups, with a stronger response in involved IBD tissue. Superantigen staphylococcal enterotoxin A (SEA) had a similar, but weaker effect. SEB was observed to be the strongest inducer of TNF-alpha for all groups, again with a more marked response in inflamed tissue. Stimulated release of IL-1 was considerably less than for TNF-alpha. The superantigens' superior potency over LPS was not as marked for IL-1 as it was for TNF-alpha. In addition to IL-1, IL-1RA release was also triggered by the bacterial products. The net effect of activation on the IL-1RA/IL-1 ratio was relatively modest. Release of the proinflammatory cytokines TNF-alpha and IL-1, as well as that of the anti-inflammatory cytokine IL-1RA was increased by incubation of colonic tissue with bacterial factors. TNF-alpha production and release was increased significantly in involved colonic explants from IBD. SEB was even capable of inducing TNF-alpha release from uninvolved colonic tissue.
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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 | 0.000 |
| 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".