Filament lattice changes in smooth muscle assessed using birefringence
Bibliographic record
Abstract
The long functional range of some types of smooth muscle has been the subject of recent study. It has been proposed that the muscle filament lattice adapts to longer lengths by placing more filaments in series and that lattice plasticity is facilitated by myosin filament evanescence, with filaments dissociating during relaxation and reforming upon activation. Support for these dynamic changes in the filament lattice has been provided partly by changes in contractile parameters at different times in the contraction-relaxation cycle at different lengths. If the changes in contractile parameters result from filament formation and dissociation, these structural changes must occur on the time scale of tension development and relaxation. To assess whether thick-filament formation could account for the contractile changes, we measured birefringence continuously during activation and relaxation and compared these optical changes with the time course of force development and relaxation. Birefringence is a well-known property of muscle; striations in skeletal and cardiac muscle result from the A-bands being anisotropic, i.e., birefringent, and it is now known that this optical property is due to the presence of myosin thick filaments in the A-bands. Thus, the strength of birefringence is expected to represent the density of thick filaments. Here, we describe the principle of the method, the techniques for recording the optical signals, some initial results, and discuss the interpretation of results and some limitations of the method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".