Functional bias of contractile control in mouse resistance arteries
Bibliographic record
Abstract
Abstract Constrictor agonists set arterial tone through two coupling processes, one tied to (electromechanical), the other independent (pharmacomechanical) of, membrane potential (V M ). This dual arrangement raises an intriguing question: is the contribution of each mechanism (1) fixed and proportionate, or (2) variable and functionally biased. Examination began in mouse mesenteric arteries with a vasomotor assessment to a classic G q/11 (phenylephrine) or G q/11 /G 12/13 (U46619) agonist, in the absence and presence of nifedipine, to separate among the two coupling mechanisms. Each constrictor elicited a concentration response curve that was attenuated and rightward shifted by nifedipine, findings consistent with functional bias. Electromechanical coupling preceded pharmacomechanical, the latter’s importance rising with agonist concentration. In this regard, ensuing contractile and phosphorylation (CPI-17 & MYPT1 (T-855 & T-697)) measures revealed phenylephrine-induced pharmacomechanical coupling was tied to protein kinase C (PKC) activity, while that enabled by U46619 to PKC and Rho-kinase. A complete switch to pharmacomechanical coupling arose when agonist superfusion was replaced by pipet application to a small portion of artery. This switch was predicted, a priori, by a computer model of electromechanical control and supported by additional measures of V M and cytosolic Ca 2+ . We conclude that the coupling mechanisms driving agonist-induced constriction are variable and functionally biased, their relative importance set in accordance with agonist concentration and manner of application. These findings have important implications to hemodynamic control in health and disease, including hypertension and arterial vasospasm.
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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 | 0.001 |
| 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".