Pharmacological Targeting of KCa Channels to Improve Endothelial Function in the Spontaneously Hypertensive Rat
Bibliographic record
Abstract
Systemic hypertension is a major risk factor for the development of cardiovascular disease and is often associated with endothelial dysfunction. KCa2.3 and KCa3.1 channels are expressed in the vascular endothelium and contribute to stimulus-evoked vasodilation. We hypothesized that acute treatment with SKA-31, a selective activator of KCa2.x and KCa3.1 channels, would improve endothelium-dependent vasodilation and transiently lower mean arterial pressure (MAP) in male, spontaneously hypertensive rats (SHRs). Isolated vascular preparations exhibited impaired vasodilation in response to bradykinin (i.e., endothelial dysfunction) compared with Wistar controls, which was associated with decreased bradykinin receptor expression in mesenteric arteries. In contrast, similar levels of endothelial KCa channel expression were observed, and SKA-31 evoked vasodilation was comparable in vascular preparations from both strains. Addition of a low concentration of SKA-31 (i.e., 0.2-0.3 μM) failed to augment bradykinin-induced vasodilation in arteries from SHRs. However, responses to acetylcholine were enhanced. Surprisingly, acute bolus administration of SKA-31 in vivo (30 mg/kg, i.p. injection) modestly elevated MAP compared with vehicle injection. In summary, pharmacological targeting of endothelial KCa channels in SHRs did not readily reverse endothelial dysfunction in situ, or lower MAP in vivo. SHRs thus appear to be less responsive to endothelial KCa channel activators, which may be related to their vascular pathology.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".