Second Messenger Communication and the Regulation of Vascular Contractility
Bibliographic record
Abstract
This study examined the role of second messenger communication in the regulation of vascular contractility. We specifically hypothesized that the generation of inositol 1,4,5 trisphosphate (IP 3 ) by smooth muscle‐dependent vasoconstrictors sequentially cross myoendoendothelial gap junctions (MEGJs), elicits Ca 2+ release from IP 3 receptors (IP 3 R) and activates intermediate conductance Ca 2+ sensitive K + channels (K Ca3.1 ) to elicit a negative feedback response. Work began by monitoring the vasomotor/electrical responses of hamster retractor muscle feed arteries to an increasing concentration of superfused phenylephrine (PE) in the absence/presence of K Ca2.3 , K Ca3.1 or an IP 3 R inhibitor. Consistent with the stated hypothesis, K Ca3.1 and IP 3 R blockade increased the contractile and electrical responsiveness of feed arteries to superfused PE, whereas K Ca2.3 inhibition had no significant effect. These changes were absent in vessels constricted to 4‐AP, a K + channel blocker that elicits a receptor independent depolarization. Electron microscopy along with immunohistochemistry confirmed the presence of MEGJs and the punctated expression of K Ca3.1 channels and IP 3 R1/IP 3 R2 in endothelial cells. Further, endothelial Ca 2+ imaging on intact and opened vessels confirmed the presence of IP 3 R‐driven Ca 2+ waves that displayed some sensitivity to global PE application. In summary, we conclude that IP 3 signalling between smooth muscle and endothelial cells contributes to a negative feedback response that moderates agonist‐induced constriction. Supported by HSFC and AHFMR.
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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 | 0.000 |
| 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".