Gut hormones: emerging role in immune activation and inflammation
Bibliographic record
Abstract
Gut inflammation is characterized by mucosal recruitment of activated cells from both the innate and adaptive immune systems. In addition to immune cells, inflammation in the gut is associated with an alteration in enteric endocrine cells and various biologically active compounds produced by these cells. Although the change in enteric endocrine cells or their products is considered to be important in regulating gut physiology (motility and secretion), it is not clear whether the change plays any role in immune activation and in the regulation of gut inflammation. Due to the strategic location of enteric endocrine cells in gut mucosa, these gut hormones may play an important role in immune activation and promotion of inflammation in the gut. This review addresses the research on the interface between immune and endocrine systems in gastrointestinal (GI) pathophysiology, specifically in the context of two major products of enteric endocrine systems, namely serotonin (5-hydroxytryptamine: 5-HT) and chromogranins (Cgs), in relation to immune activation and generation of inflammation. The studies reviewed in this paper demonstrate that 5-HT activates the immune cells to produce proinflammatory mediators and by manipulating the 5-HT system it is possible to modulate gut inflammation. In the case of Cgs the scenario is more complex, as this hormone has been shown to play both proinflammatory and anti-inflammatory functions. It is also possible that interaction between 5-HT and Cgs may play a role in the modulation of immune and inflammatory responses. In addition to enhancing our understanding of immunoendocrine interaction in the gut, the data generated from the these studies may have implications in understanding the role of gut hormone in the pathogenesis of both GI and non-GI inflammatory diseases which may lead ultimately to improved therapeutic strategies in inflammatory disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".