The capacity of <i>mdx</i> mouse diaphragm muscle to do oscillatory work
Bibliographic record
Abstract
1. Mdx mice were used as a model for Duchenne muscular dystrophy; both lack dystrophin. It was hypothesized that the mdx condition would have a marked effect on the ability of diaphragm muscle from mdx mice to do active net work and generate power. This hypothesis was tested using the work-loop technique. 2. Specific twitch force, specific tetanic force and maximum power were all significantly less in diaphragm strips from mdx mice than those from control mice. 3. In all preparations muscle length at which maximum power was achieved (Lw) was about 8% less than that at which maximum tetanic force was achieved (L0), both in mdx and control muscle. 4. The isometric force-length curve for mdx muscle was steeper on both sides of the plateau. Similarly, the curve relating net work per cycle to muscle length was steeper for mdx muscle on both sides of the plateau. 5. Maximum power of mdx muscle was achieved at a lower strain than for control muscle; maximum power occurred at a strain of 10.2% for mdx and 14.7% for control. Further increases in strain caused a marked decrease of power production in mdx muscle, whereas they caused a smaller decrease in control muscle. 6. In summary, at muscle lengths longer than Lw and at high strains, performance of mdx muscle was compromised relative to that of control muscle. Work and power were compromised more than isometric force.
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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) | 0.002 | 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".