Production of a canine model of Parkinson’s disease using somatic cell nuclear transfer
Bibliographic record
Abstract
Abstract Dogs have been considered a suitable model to study human neurodegenerative diseases, such as Alzheimer’s disease and Parkinson’s disease (PD), and brain aging, because of their long lifespan and the similarities of disease presentation and clinical response in humans and dogs. Further, it is possible to evaluate the visible cognition/motion ability of patients with brain dysfunction. In the present study, we aimed to generate a canine model of PD that overexpresses the human DJ-1 (hDJ-1) gene using the somatic cell nuclear transfer (SCNT) technique. The hDJ-1 gene was transfected into canine fetal fibroblasts, which were used to produce cloned embryos. Reconstructed embryos were transferred into the oviduct of surrogate mothers, one of which gave birth to one dog. The cloned dog was depressed, and his movements were slow and fewer, resulting in an abnormal phenotype, as observed via imaging analyses, such as positron emission tomography (PET) and magnetic resonance imaging (MRI) analyses; these findings were similar to the symptoms of PD in humans. In addition, exogenous hDJ-1 was successfully transmitted to the next generation without silencing. We confirmed that the puppies exhibited the same behavior and imaging analysis as the hDJ-1 transgenic (TG) dog. Using SCNT, we generated TG dogs with PD that overexpressed the hDJ-1 gene. Human PD-like phenotypes have been confirmed in the TG dogs. To the best of our knowledge, this is the first report that describes the establishment of a canine model of PD. Furthermore, TG dogs could be used in preclinical trials of drugs for the treatment of PD.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".