Interleukin-1β activates specific populations of enteric neurons and enteric glia in the guinea pig ileum and colon
Bibliographic record
Abstract
Fos expression was used to assess whether the proinflammatory cytokine interleukin-1beta (IL-1beta) activated specific, chemically coded neuronal populations in isolated preparations of guinea pig ileum and colon. Whether the effects of IL-1beta were mediated through a prostaglandin pathway and whether IL-1beta induced the expression of cyclooxygenase (COX)-2 was also examined. Single- and double-labeling immunohistochemistry was used after treatment of isolated tissues with IL-1beta (0.1-10 ng/ml). IL-1beta induced Fos expression in enteric neurons and also in enteric glia in the ileum and colon. For enteric neurons, activation was concentration-dependent and sensitive to indomethacin, in both the myenteric and submucosal plexuses in both regions of the gut. The maximum proportion of activated neurons differed between the ileal (approximately 15%) and colonic (approximately 42%) myenteric and ileal (approximately 60%) and colonic (approximately 75%) submucosal plexuses. The majority of neurons activated in the myenteric plexus of the ileum expressed nitric oxide synthase (NOS) or enkephalin immunoreactivity. In the colon, activated myenteric neurons expressed NOS. In the submucosal plexus of both regions of the gut, the majority of activated neurons were vasoactive intestinal polypeptide (VIP) immunoreactive. After treatment with IL-1beta, COX-2 immunoreactivity was detected in the wall of the gut in both neurons and nonneuronal cells. In conclusion, we have found that the proinflammatory cytokine IL-1beta specifically activates certain neurochemically defined neural pathways and that these changes may lead to disturbances in motility observed in the inflamed bowel.
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 | 0.000 |
| 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".