Do gap junctions play a role in nerve transmissions as well as pacing in mouse intestine?
Bibliographic record
Abstract
Varicosities of nitrergic and other nerves end on deep muscular plexus interstitial cells of Cajal or on CD34-positive, c-kit-negative fibroblast-like cells. Both cell types connect to outer circular muscle by gap junctions, which may transmit nerve messages to muscle. We tested the hypotheses that gap junctions transmit pacing messages from interstitial cells of Cajal of the myenteric plexus. Effects of inhibitors of gap junction conductance were studied on paced contractions and nerve transmissions in small segments of circular muscle of mouse intestine. Using electrical field stimulation parameters (50 V/cm, 5 pps, and 0.5 ms) which evoke near maximal responses to nitrergic, cholinergic, and apamin-sensitive nerve stimulation, we isolated inhibitory responses to nitrergic nerves, inhibitory responses to apamin-sensitive nerves and excitatory responses to cholinergic nerves. 18beta-Glycyrrhetinic acid (10, 30, and 100 microM), octanol (0.1, 0.3, and 1 mM) and gap peptides (300 microM of (40)Gap27, (43)Gap26, (37,43)Gap27) all failed to abolish neurotransmission. 18beta-Glycyrrhetinic acid inhibited frequencies of paced contractions, likely owing to inhibition of l-type Ca(2+) channels in smooth muscle, but octanol or gap peptides did not. 18beta-Glycyrrhetinic acid and octanol, but not gap peptides, reduced the amplitudes of spontaneous and nerve-induced contractions. These reductions paralleled reductions in contractions to exogenous carbachol. Additional experiments with gap peptides in both longitudinal and circular muscle segments after N(G)-nitro-l-arginine and TTX revealed no effects on pacing frequencies. We conclude that gap junction coupling may not be necessary for pacing or nerve transmission to the circular muscle of the mouse intestine.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".