Cholinergic regulation of epithelial ion transport in the mammalian intestine
Bibliographic record
Abstract
Acetylcholine (ACh) is critical in controlling epithelial ion transport and hence water movements for gut hydration. Here we review the mechanism of cholinergic control of epithelial ion transport across the mammalian intestine. The cholinergic nervous system affects basal ion flux and can evoke increased active ion transport events. Most studies rely on measuring increases in short-circuit current (ISC = active ion transport) evoked by adding ACh or cholinomimetics to intestinal tissue mounted in Ussing chambers. Despite subtle species and gut regional differences, most data indicate that, under normal circumstances, the effect of ACh on intestinal ion transport is mainly an increase in Cl- secretion due to interaction with epithelial M3 muscarinic ACh receptors (mAChRs) and, to a lesser extent, neuronal M1 mAChRs; however, AChR pharmacology has been plagued by a lack of good receptor subtype-selective compounds. Mice lacking M3 mAChRs display intact cholinergically-mediated intestinal ion transport, suggesting a possible compensatory mechanism. Inflamed tissues often display perturbations in the enteric cholinergic system and reduced intestinal ion transport responses to cholinomimetics. The mechanism(s) underlying this hyporesponsiveness are not fully defined. Inflammation-evoked loss of mAChR-mediated control of epithelial ion transport in the mouse reveals a role for neuronal nicotinic AChRs, representing a hitherto unappreciated braking system to limit ACh-evoked Cl- secretion. We suggest that: i) pharmacological analyses should be supported by the use of more selective compounds and supplemented with molecular biology techniques targeting specific ACh receptors and signalling molecules, and ii) assessment of ion transport in normal tissue must be complemented with investigations of tissues from patients or animals with intestinal disease to reveal control mechanisms that may go undetected by focusing on healthy tissue only.
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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) | 0.001 | 0.001 |
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".