Tetraethylammonium, glibenclamide, and 4‐aminopyridine modulate post‐occlusive reactive hyperemia in non‐glabrous human skin with no roles of <scp>NOS</scp> and <scp>COX</scp>
Bibliographic record
Abstract
Abstract Objectives Post‐occlusive reactive hyperemia (PORH) following arterial occlusion is widely used to assess cutaneous microvascular function, though the underlying mechanisms remain to be fully elucidated. We evaluated the hypothesis that Ca2+‐activated, ATP‐sensitive, and voltage‐gated K+ channels (KCa, KATP, and KV channels, respectively) contribute to PORH while nitric oxide synthase (NOS) and cyclooxygenase (COX) do not. Methods On separate occasions, cutaneous blood flow (laser Doppler flowmetry) was monitored before and following 5‐min arterial occlusion at forearm skin sites treated via microdialysis with the following: Experiment 1 (n = 11): (a) lactated Ringer solution (Control), (b) 10 mM Nω‐nitro‐L‐arginine (NOS inhibitor), (c) 10 mM ketorolac (COX inhibitor), and (d) combined NOS+COX inhibition; Experiment 2 (n = 14): (a) lactated Ringer solution (Control), (b) 50 mM tetraethylammonium (non‐selective KCa channel blocker), (c) 5 mM glibenclamide (non‐specific KATP channel blocker), and (d) 10 mM 4‐aminopyridine (non‐selective KV channel blocker). Results Separate and combined NOS and COX inhibition did not influence PORH. Conversely, tetraethylammonium and glibenclamide attenuated, whereas 4‐aminopyridine augmented PORH. Conclusions We showed that tetraethylammonium, glibenclamide, and 4‐aminopyridine modulate PORH with no roles of NOS and COX in human non‐glabrous forearm skin in vivo. Thus, cutaneous PORH changes could reflect altered K+ channel function.
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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".