Detecting Degenerative Changes in Myotonic Murine Models of Duchenne Muscular Dystrophy Using High-Frequency Ultrasound
Bibliographic record
Abstract
OBJECTIVE: Ultrasound imaging is an economical and noninvasive technique for studying musculoskeletal diseases such as Duchenne muscular dystrophy (DMD). Duchenne muscular dystrophy results from the loss of the cytoskeletal protein dystrophin. This in turn increases muscle susceptibility to injury, resulting in myofiber membrane leakage, inflammation, and degeneration. The purpose of this study was to detect dystrophic changes in muscle noninvasively. High-frequency ultrasound (HFU; 40 MHz) was used to obtain a resolution of 80 microm, which is not achievable with lower-frequency clinical scanners. METHODS: Using HFU, we were able to visualize musculoskeletal abnormalities as hyperechoic lesions within the dystrophic muscle. To validate the imaging findings, fiducial markers were placed in close proximity to lesions under HFU guidance. The nature of the lesion was then investigated histologically. This was repeated in the lower limbs of 10 mdx (mutated dystrophin gene) mice, a transgenic murine model of DMD. RESULTS: The abnormalities in the dystrophic muscle consisted of large influxes of leukocytic infiltrates, fibrotic scars, and calcified lesions. CONCLUSIONS: Although macrophages and fibrosis are commonly noted in DMD, to our knowledge, the presence of intramuscular calcific necrosis in dystrophic muscle has not been reported. This novel dystrophic feature of muscle degeneration may be useful in longitudinal studies of murine DMD and regenerative studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".