Mitochondrially inherited sensory ataxic neuropathy in golden retriever dogs
Bibliographic record
Abstract
A novel neurological syndrome, mitochondrially inherited sensory ataxic neuropathy (SAN), was discovered in Golden Retriever dogs in Sweden. The purpose of the work described in the present thesis was to investigate the phenotype, clinical course and genotype of this syndrome by clinical, neurological and pathological examination of affected dogs, as well as to determine the mode of inheritance and identify the causative mutation. Mitochondrially inherited SAN in Golden Retriever dogs has an insidious onset during puppyhood. Affected dogs develop ataxia and dysmetria, with abnormal postural reactions and depressed spinal reflexes. The disease has a chronic, slowly progressive clinical course. Of the affected dogs investigated within the scope of this thesis, none became non-ambulatory or died spontaneously during the study period. However, about half of the affected dogs were euthanized because of neurological impairment before attaining 4 years of age. Post mortem examinations of affected dogs revealed degenerative changes in both the central and the peripheral nervous system. A chronic active central–peripheral axonopathy, neuroaxonal dystrophy-like alterations in spinal cord and brainstem, and a neuron-sparing encephalopathy with spongiosis in the basal nuclei were the most prominent findings. A maternal mode of inheritance was concluded from pedigree analysis, indicating a causative mutation in the mitochondrial DNA. Laboratory data confirmed that affected dogs had malfunctioning mitochondria. A single base-pair deletion in the mitochondrial tRNATyr gene was found and proven to be pathogenic. In summary, canine SAN is a slowly progressive neurodegenerative disease with onset in puppyhood. The disease is maternally inherited and is caused by a mutation in the mitochondrial tRNATyr gene.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".