Titin force enhancement following active stretch of skinned skeletal muscle fibres
Bibliographic record
Abstract
, a genetic mutation with a deletion in titin. Whether loss of titin force enhancement is associated with compensatory mechanisms at higher structural levels of organization, such as single fibres or entire muscles, is unclear. The aim of this study was to determine whether mechanical deficiencies in titin force enhancement are also observed at the fibre level, and whether mechanisms compensate for the loss of titin force enhancement. Single skinned fibres from control and mutant mice were stretched actively and passively beyond filament overlap to observe titin-based force. Mutant fibres generated lower contractile stress (force divided by cross-sectional area) than control fibres. Titin force enhancement was observed in control fibres stretched beyond filament overlap, but was overshadowed in mutant fibres by an abundance of collagen and high variability in mechanics. However, titin force enhancement could be measured in all control fibres and most mutant fibres following short stretches, accounting for ∼25% of the total stress following active stretch. Our results show that the partial loss of titin force enhancement in myofibrils is not preserved in all mutant fibres and this mutation likely affects fibres differentially within a muscle. An increase in collagen helps to reestablish total force at long sarcomere lengths with the loss in titin force enhancement in some mutant fibres, increasing the overall strength of mutant fibres.
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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".