Do gap junctions couple interstitial cells of Cajal pacing and neurotransmission to gastrointestinal smooth muscle?
Bibliographic record
Abstract
Interstitial cells of Cajal (ICC) pace gastrointestinal phasic activity and transmit nerve activity. Gap junctions may couple these cells to smooth muscle, but no functional evidence exists. The objective of this study was to use uncouplers of gap junctions, 18 alpha-glycyrrhetenic acid and its water-soluble analogue carbenoxolone, to evaluate if gap junctions function in pacing and neurotransmission. After inhibition of nerve function with tetrodotoxin (TTX) and N(G)-nitro-L-arginine (L-NOARG), ionomycin- or carbachol-initiated regular phasic activities of circular muscle strips from canine colon and ileum. In some cases, the primary ICC network responsible for pacing was removed. The effects of inhibitors of gap junction conductance (10(-5)-10(-4) mol L(-1)) on frequencies and amplitudes of contraction were compared to appropriate time controls. Lower oesophageal sphincter (LOS) relaxations to nerve stimulation were studied before and after inhibition of gap junction functions. No major changes in LOS relaxations or frequencies of colonic or ileal contractions occurred, but amplitudes of contractions decreased from these agents. Similar results were obtained when the myenteric plexus-ICC network of ileum was removed. Regular phasic activity was not obtained after removal of the colon submuscular plexus ICC. These findings suggest that mechanisms other than gap junctions couple gut pacemaking activity and nerve transmission.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".