Contribution of EDHF and the role of potassium channels in the regulation of vascular tone
Bibliographic record
Abstract
Abstract Potassium channels in both endothelial and vascular smooth muscle play an important role in the regulation of vascular tone and are therefore potential targets for new drug development. Endothelial cell potassium channels, notably small conductance and intermediate conductance calcium‐activated K channels (SK Ca and IK Ca , respectively), are of critical importance for the regulation of synthesis and, possibly, release of nitric oxide (NO) and prostacyclin (PGI 2 ) as well as the elusive endothelium‐derived hyperpolarizing factor (EDHF). EDHF may represent the “third” pathway for endothelium‐dependent relaxation and, if a chemical mediator is involved, EDHF may represent a new class of K‐channel openers. Of interest also is that endothelial cell dysfunction is a common feature of cardiovascular disease and this dysfunction is often associated with a decreased availability of endothelium‐derived NO and, sometimes, an enhanced contribution of EDHF. This raises the question of the possibility that EDHF may play a pathophysiological function. A high degree of heterogeneity exists with respect to the pharmacological properties of EDHF and this may indicate that multiple EDHFs exist with some degree of vessel selectivity. Despite the heterogeneity in the properties of EDHF, a common feature is that a combination of two K‐channel toxins, apamin and charybdotoxin, which target the SK Ca and IK Ca channels, respectively (charybdotoxin also inhibits large‐conductance K Ca , BK Ca , and voltage‐gated, K V channels). The requirement for both toxins for inhibiting EDHF is suggestive that a novel K channel may exist in the vasculature; however, to date the discovery of such a channel has remained elusive. Drug Dev. Res. 58:81–89, 2003. © 2003 Wiley‐Liss, Inc.
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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 | 0.001 |
| 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".