The RPGRIP1-deficient dog, a promising canine model for gene therapy.
Bibliographic record
Abstract
PURPOSE: To evaluate the RPGRIP1-deficient miniature longhaired dachshund (MLHD) dog as a potential candidate for gene therapy. METHODS: Six RPGRIP1-deficient MLHD dogs from our dog colony have been observed for two years using a variety of noninvasive procedures. These included bilateral full-field electroretinograms (ERG) to evaluate retinal function, fundus photographs to evaluate retinal vascularization, and optical coherence tomographs (OCT) to evaluate retinal thickness. We also performed histological examination of hematoxylin- and eosin-stained retinal sections as well as sections labeled in situ by the terminal dUTP nick end labeling (TUNEL) method. RESULTS: ERG findings showed that as early as 2 months of age, cone function was lost while rod function was preserved. However, by 9 months of age, both cone and rod functions could not be detected. Functional visual assessment based on the ability to avoid obstacles showed that vision was retained up to the age of 11 months. Both OCT and histopathology studies revealed a progressive thinning of the outer nuclear layer (ONL) over the first 2 years of age. TUNEL labeling identified apoptotic photoreceptor cell death as the cause of this thinning of the ONL. CONCLUSIONS: A treatment strategy should consist in initiating gene therapy as early as possible after birth to prevent or delay the loss of rod function. In the MLHD, successful subretinal delivery of a therapeutic vector is feasible at 2 months of age and may prevent or delay the loss of rod function.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".