Myosin filaments in smooth muscle cells do not have a constant length
Bibliographic record
Abstract
The length of myosin filaments was measured in three types of smooth muscle using serial electron microscopy. The frequency distribution of myosin filament length for all three types of smooth muscle followed an exponential decay pattern. The same frequency distribution pattern was observed in activated tracheal smooth muscle, although the average length was shorter compared with the filaments in relaxed smooth muscle. Analysis suggests that the distribution pattern reflects a dynamic equilibrium between competing processes of linear polymerization and de-polymerization of myosin dimers. Abstract Myosin molecules from smooth muscle and non-muscle cells are known to self-assemble into side-polar filaments in vitro. However, the in situ mechanism of filament assembly is not clear and the question of whether there is a unique length for myosin filaments in smooth muscle is still under debate. In this study we measured the lengths of 16,587 myosin filaments in three types of smooth muscle cells using serial electron microscopy (EM). Sheep airway and pulmonary arterial smooth muscle as well as rabbit carotid arterial smooth muscle were fixed for EM and serial ultra-thin (50–60 nm) sections were obtained. Myosin filaments were traced in consecutive sections to determine their lengths. The results indicate that there is not a single length for the myosin filaments; instead there is a wide variation in lengths. The plots of observation frequency versus myosin filament length follow an exponential decay pattern. Analysis suggests that in situ assembly of myosin filaments in smooth muscle is governed by random processes of linear polymerization and de-polymerization, and that the dynamic equilibrium of these processes determines the observed length distribution.
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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) | 0.001 | 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".