Tissue length modulates “stimulated actin polymerization,” force augmentation, and the rate of swine carotid arterial contraction
Bibliographic record
Abstract
"Stimulated actin polymerization" has been proposed to be involved in force augmentation, in which prior submaximal activation of vascular smooth muscle increases the force of a subsequent maximal contraction by ∼15%. In this study, we altered stimulated actin polymerization by adjusting tissue length and then measured the effect on force augmentation. At optimal tissue length (1.0 L(o)), force augmentation was observed and was associated with increased prior stimulated actin polymerization, as evidenced by increased prior Y118 paxillin phosphorylation without changes in prior S3 cofilin or cross-bridge phosphorylation. Tissue length, per se, regulated Y118 paxillin, but not S3 cofilin, phosphorylation. At short tissue length (0.6 L(o)), force augmentation was observed and was associated with increased prior stimulated actin polymerization, as evidenced by reduced prior S3 cofilin phosphorylation without changes in Y118 paxillin or cross-bridge phosphorylation. At long tissue length (1.4 L(o)), force augmentation was not observed, and there were no prior changes in Y118 paxillin, S3 cofilin, or cross-bridge phosphorylation. There were no significant differences in the cross-bridge phosphorylation transients before and after the force augmentation protocol at all three lengths tested. Tissues contracted faster at longer tissue lengths; contractile rate correlated with prior Y118 paxillin phosphorylation. Total stress, per se, predicted Y118 paxillin phosphorylation. These data suggest that force augmentation is regulated by stimulated actin polymerization and that stimulated actin polymerization is regulated by total arterial stress. We suggest that K(+) depolarization first leads to cross-bridge phosphorylation and contraction, and the contraction-induced increase in mechanical strain increases Y118 paxillin phosphorylation, leading to stimulated actin polymerization, which further increases force, i.e., force augmentation and, possibly, latch.
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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".