Canadian Association of Neurosciences Review: Polyglutamine Expansion Neurodegenerative Diseases
Bibliographic record
Abstract
Since the early 1990s, DNA triplet repeat expansions have been found to be the cause in an ever increasing number of genetic neurologic diseases. A subset of this large family of genetic diseases has the expansion of a CAG DNA triplet in the open reading frame of a coding exon. The result of this DNA expansion is the expression of expanded glutamine amino acid repeat tracts in the affected proteins, leading to the term, Polyglutamine Diseases, which is applied to this sub-family of diseases. To date, nine distinct genes are known to be linked to polyglutamine diseases, including Huntington's disease, Machado-Joseph Disease and spinobulbar muscular atrophy or Kennedy's disease. Most of the polyglutamine diseases are characterized clinically as spinocerebellar ataxias. Here we discuss recent successes and advancements in polyglutamine disease research, comparing these different diseases with a common genetic flaw at the level of molecular biology and early drug design for a family of diseases where many new research tools for these genetic disorders have been developed. Polyglutamine disease research has successfully used interdisciplinary collaborative efforts, informative multiple mouse genetic models and advanced tools of pharmaceutical industry research to potentially serve as the prototype model of therapeutic research and development for rare neurodegenerative diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.022 |
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".