L-Type Ca2+ Channel Regulation by Pituitary Adenylate Cyclase-Activating Polypeptide in Vascular Myocytes from Spontaneously Hypertensive Rats*
Bibliographic record
Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP), a vasoactive peptide, modulates the L-type Ca(2+) channel current (L channel current) in vascular smooth muscle cells (VSMC) through activation and integration of two intracellular pathways, protein kinase A and protein kinase C (PKC). In the present study we compared the effects of PACAP on the L channel current in VSMC from the spontaneously hypertensive rats (SHR) and normotensive controls, Wistar Kyoto rats (WKY). We found that compared with WKY, VSMC from SHR had a higher L channel current density. Stimulation by PACAP (10 nM) caused an increase in the amplitude of the whole cell current and prolonged open time in VSMC from SHR and WKY, with the increase greater in SHR. These effects of PACAP on the L channel current was mimicked by an activator of PKC. In contrast, PACAP caused a smaller increase in cAMP accumulation in VSMC from SHR than WKY, and there was no difference in the inhibitory effect of 8-bromo-cAMP on the L channel current from both type of cells. The greater increase in amplitude of the L channel current by PACAP in VSMC from SHR persisted in the presence of adenosine cyclic 3',5'-monophosphothioate, Rp-isomer, a cAMP antagonist, but not calphostin C, a PKC inhibitor. Taken together, our results show an increase in L channel current density and an enhanced PACAP effect on the L channel current in VSMC from SHR compared with WKY. This difference in PACAP response appears to be predominately secondary to an increased PKC sensitivity.
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) | 0.000 | 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 | 0.001 |
| 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".