Noradrenaline inhibits neurogenic propulsive motor patterns but not neurogenic segmenting haustral progression in the rabbit colon
Bibliographic record
Abstract
Abstract Background Excessive sympathetic inhibition may be a cause of colon motor dysfunction. Our aim was to better understand the mechanisms of sympathetic inhibition on colonic motor patterns using the rabbit colon, hypothesizing that noradrenaline selectively inhibits propulsive motor patterns. Methods Changes in motor patterns of the rabbit colon were studied ex vivo using noradrenaline and adrenoceptor antagonists and analyzed using spatiotemporal diameter maps. Key Results Noradrenaline abolished propulsive contractions: it abolished the long‐distance contractions (LDCs) from a baseline frequency of 0.8 ± 0.3 and the clusters of fast propagating contractions (FPCs) at a frequency of 14.4 ± 2.8 cpm. Both motor patterns recovered after addition of the α2‐adrenoceptor antagonist yohimbine to a frequency of 0.5 ± 0.2 and 9.9 ± 3.3 cpm, respectively. The β‐adrenoceptor antagonist propranolol did not prevent the loss of propulsive motor patterns with noradrenaline. Noradrenaline did not inhibit haustral boundary contractions and increased the frequency of the myogenic ripples from 8.3 ± 1.4 to 10.5 ± 1.3 cpm which was not affected by yohimbine, propranolol nor the α1‐adrenoceptor blocker prazosin. Conclusions and Inferences Noradrenergic inhibition of propulsive motor patterns is mediated by the α2‐adrenoceptor to inhibit the neurogenic LDCs and the neurogenic clustering of FPCs. The neurogenic haustral boundary contractions are not affected, suggesting that α2‐receptors are on selective neural circuits. The excitatory effect of noradrenaline on ripples may be due to the activation of adrenoceptors on interstitial cells of Cajal, but action on α1‐receptors was excluded. No role for the β‐adrenoceptor was found.
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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 | 0.001 |
| 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".