Does partial titin degradation affect sarcomere length nonuniformities and force in active and passive myofibrils?
Bibliographic record
Abstract
The aim of this study was to determine the role of titin in preventing the development of sarcomere length nonuniformities following activation and after active and passive stretch by determining the effect of partial titin degradation on sarcomere length nonuniformities and force in passive and active myofibrils. Selective partial titin degradation was performed using a low dose of trypsin. Myofibrils were set at a sarcomere length of 2.4 µm and then passively stretched to sarcomere lengths of 3.4 and 4.4 µm. In the active condition, myofibrils were set at a sarcomere length of 2.8 µm, activated, and actively stretched by 1 µm/sarcomere. The extent of sarcomere length nonuniformities was calculated for each sarcomere as the absolute difference between sarcomere length and the mean sarcomere length of the myofibril. Our main finding is that partial titin degradation does not increase sarcomere length nonuniformities after passive stretch and activation compared with when titin is intact but increases the extent of sarcomere length nonuniformities after active stretch. Furthermore, when titin was partially degraded, active and passive stresses were substantially reduced. These results suggest that titin plays a crucial role in actively stretched myofibrils and is likely involved in active and passive force production.
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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".