249. New Canine Models of Duchenne Muscular Dystrophy: Identification and Molecular Characterization
Bibliographic record
Abstract
Duchenne Muscular Dystrophy (DMD) presents a series of significant challenges to the development of gene therapy approaches, including the frequency of new mutations, the size of the gene and mRNA and the complexity of the mutations involved. Animal models can significantly accelerate the process for development of novel therapies if they accurately mimic the human disease. To date, the only animal that develops disease with a course and severity similar to humans, without requiring additional mutations or manipulations, is the dog. Several canine models have been identified and their mutations characterized, confirming that this disease occurs at a relatively high frequency, can have variable effects, and is the result of many different mutations. Unlike other canine inherited diseases, Duchenne-like Muscular Dystrophy occurs spontaneously in multiple families within in a breed, leading to more than one mutation in a given breed of dog. Using a rapid PCR based screen, we have identified the putative mutations in two canine models of DMD. Data from a Labrador Retriever family and a Welsh Corgi family indicate that the mutations in both families consist of the precise insertion of repetitive DNA elements in the mRNA between exon pairs. The mutations involve different repetitive sequences and different exon pairs in each family. In addition, affected animals from another distinct Labrador Retriever family and a West Highland White family have been identified. Preliminary data supporting the independent nature of these mutations as well as their localization within the gene will be presented. The relatively large size, intact immune system, and potential to measure beneficial effects combined with the assortment of different mutations available provide an excellent resource in which gene therapy approaches for DMD can be tested.
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 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 | 0.000 |
| 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) | 0.000 | 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".