Diagnostic Neurosonography in Mouse Models of Demyelinating Charcot-Marie-Tooth Diseae Type 1A (P5.100)
Bibliographic record
Abstract
Objective: In this study, we compared sciatic nerve cross-sectional area in transgenic CMT (Charcot-Marie-Tooth disease) 1A mice with wild type controls to describe the ultrasonographic changes that occur in the nerves of hereditary neuropathic mice. Background: Ultrasonography is a useful tool for evaluating conditions of the peripheral nerve because of high-resolution anatomic information of nerves to complement standard electrodiagnostic studies. There have been several studies in which sonography revealed the enlargement of peripheral nerves in human patients with demyelinating CMT. But few studies have been conducted to reveal the role of ultrasound in mouse models of CMT. Methods: 10 CMT1A transgenic mice and 10 wild type controls underwent ultrasonography of sciatic nerves. We measured nerve cross-sectional areas in proximal hind limb regions. Ultrasound examinations were performed using a Vevo 2100 (Visual Sonics, Toronto, Canada) micro-ultrasound machine with a 55 MHz probe. Statistical analysis was performed using the Mann-Whitney U test. Results: Transgenic mice with CMT 1A have sciatic nerves than wild type controls (p<0.05). Conclusions: The study showed increased cross-sectional area of the sciatic nerves in the proximal hind limb in transgenic mice with CMT1A when compared with wild type controls. These findings might suggest the usefulness of neurosonography for evaluation of treatment outcome in animal models and role of nerve ultrasound as a biomarker. Further studies in larger numbers of other CMT subjects and more nerves in different stages are needed for ultrasonography as a useful diagnostic tool in CMT mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
| 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".