Potential biomarkers to follow the progression and treatment response of Huntington’s disease
Bibliographic record
Abstract
Huntington's disease (HD) is a rare genetic disease caused by expanded polyglutamine repeats in the huntingtin protein resulting in selective neuronal loss. Although genetic testing readily identifies those who will be affected, current pharmacological treatments do not prevent or slow down disease progression. A major challenge is the slow clinical progression and the inability to biopsy the affected tissue, the brain, making it difficult to design short and effective proof of concept clinical trials to assess treatment benefit. In this study, we focus on identifying peripheral biomarkers that correlate with the progression of the disease and treatment benefit. We recently developed an inhibitor of pathological mitochondrial fragmentation, P110, to inhibit neurotoxicity in HD. Changes in levels of mitochondrial DNA (mtDNA) and inflammation markers in plasma, a product of DNA oxidation in urine, mutant huntingtin aggregates, and 4-hydroxynonenal adducts in muscle and skin tissues were all noted in HD R6/2 mice relative to wild-type mice. Importantly, P110 treatment effectively reduced the levels of these biomarkers. Finally, abnormal levels of mtDNA were also found in plasma of HD patients relative to control subjects. Therefore, we identified several potential peripheral biomarkers as candidates to assess HD progression and the benefit of intervention for future clinical trials.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".