High-Frequency Ultrasound to Grade Disease Progression in Murine Models of Duchenne Muscular Dystrophy
Bibliographic record
Abstract
OBJECTIVE: This study used high-frequency ultrasound (HFU) imaging to assess muscle damage noninvasively in a longitudinal study of 2 transgenic murine models of Duchenne muscular dystrophy (DMD): mdx, which has mutated cytoskeletal protein dystrophin; and udx, which has mutated dystrophin and lacks another cytoskeleton protein, utrophin. The mdx group was further subdivided into exercised and nonexercised subgroups to assess exercise-induced damage. METHODS: Muscle damage was assessed with HFU imaging (40 MHz) at biweekly intervals for 16 weeks. The assessment was based on the number of hyperechoic lesions, the lesion diameter, and muscle disorganization, giving a combined grade according to a 5-point scale. RESULTS: High-frequency ultrasound discriminated the severity of muscle damage between wild-type and transgenic models of DMD and between mdx and udx models. Qualitative comparisons of 3-dimensional HFU images with serial histologic sections of the skeletal muscle showed the ability of ultrasound to accurately depict changes seen in the muscle architecture in vivo. CONCLUSIONS: High-frequency ultrasound images soft tissue in mice at high contrast and spatial resolution, thereby showing that this microimaging modality has the capability to assess architectural changes in muscle fibers due to myotonic dystrophy-related diseases such as DMD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".