Human mucosa/submucosa interactions during intestinal inflammation: involvement of the enteric nervous system in interleukin-8 secretion
Bibliographic record
Abstract
Interleukin-8 (IL-8) is a key chemokine upregulated in various forms of intestinal inflammation, especially those induced by bacteria such as Clostridium difficile (C. difficile). Although interactions between different mucosal and submucosal cellular components have been reported, whether such interactions are involved in the regulation of IL-8 secretion during C. difficile infection is unknown. Moreover, whether the enteric nervous system, a major component of the submucosa, is involved in IL-8 secretion during an inflammatory challenge remains to be determined. In order to investigate mucosa/submucosa interactions that regulate IL-8 secretion, we co-cultured human intestinal mucosa and submucosa. In control condition, IL-8 secretion in co-culture was lower than the sum of the IL-8 secretion of both tissue layers cultured alone. Contrastingly, IL-8 secretion increased in co-culture after mucosal challenge with toxin B of C. difficile through an IL-1 beta-dependent pathway. Moreover, we observed that toxin B of C. difficile increased IL-8 immunoreactivity in submucosal enteric neurones in co-culture and in intact preparations of mucosa/submucosa, through an IL-1 beta-dependent pathway. IL-1 beta also increased IL-8 secretion and IL-8 mRNA expression in human neuronal cell lines (NT2-N and SH-SY5Y), through p38 and ERK1/2 MAP kinase-dependent pathways. Our results demonstrate that mucosa/submucosa interactions regulate IL-8 secretion during inflammatory processes in human through IL-1 beta-dependent pathways. Finally we observed that human submucosal neurones synthesize IL-8, whose production in neurones is induced by IL-1 beta via MAPK-dependent pathways.
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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".