Molecular evidence for the involvement of calcium sensitization in serotonin‐induced cerebrovascular constriction
Bibliographic record
Abstract
The mechanisms contributing to serotonin (5‐HT)‐induced vasoconstriction of cerebral arteries are poorly characterized. This is important as abnormalities in control of cerebral blood flow by 5‐HT are implicated in subarachnoid hemorrhage and may be involved in migraine. Force generation by vascular smooth muscle cells depends on the balance of myosin light chain kinase (MLCK) and phosphatase (MLCP) activities that determine the level of myosin light chain (LC 20 ) phosphorylation. Agonists that activate Rho kinase (ROK) and protein kinase C (PKC) can inhibit MLCP by phosphorylation of myosin light chain targeting subunit 1 (MYPT1) and CPI‐17, respectively. This inhibition of MLCP leads to increased phospho‐LC 20 and force generation at constant [Ca 2+ ] i , i.e. Ca 2+ sensitization. Here, we have quantified LC 20 , MYPT1 and CPI‐17 phosphorylation in protein samples derived from pressurized segments of rat middle cerebral arteries using a novel, highly sensitive western blotting method. 5‐HT induced constriction and increased phospho‐LC 20 ; both were reduced by ROK (H1152, 0.3 µM) or PKC (GF109203, 3 µM) inhibition. Phospho‐MYPT1 level was increased by 5‐HT, but not phospho‐CPI‐17. Our data indicate for the first time that Ca 2+ sensitization via ROK‐mediated phosphorylation of MYPT1 resulting in enhanced LC 20 phosphorylation and force generation may contribute to 5‐HT‐induced cerebral vasoconstriction.
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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) | 0.002 | 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".