Targeting Tau to Treat Clinical Features of Huntington's Disease
Bibliographic record
Abstract
Huntington’s disease (HD) is an autosomal dominant neurodegenerative disorder characterized by severe motor, cognitive and psychiatric impairments. While motor deficits often confirm diagnosis, cognitive dysfunctions usually manifest earlier in disease progression and are consistently ranked among the leading factors that impact the patients’ quality of life. The genetic component of HD, a mutation in the huntingtin (HTT) gene, is traditionally presented as the main contributor to disease pathology. However, accumulating evidence suggests the implication of the microtubule-associated tau protein to the pathogenesis, and therefore proposes an alternative conceptual framework where tau and mutant huntingtin (mHTT) act conjointly to drive neurodegeneration and cognitive dysfunction. This perspective on disease etiology offers new avenues to design therapeutic interventions and could leverage decades of research on Alzheimer’s disease (AD) and other tauopathies to rapidly advance drug discovery. In this mini review, we examine the breadth of tau-targeting treatments currently tested in the preclinical and clinical settings for AD and other tauopathies, and discuss the potential application of these strategies to HD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".