Caspases and neurodegeneration: on the cutting edge of new therapeutic approaches
Bibliographic record
Abstract
Unregulated apoptosis underlies many pathological conditions, including neurodegenerative diseases. In this review, we focus on the role of cysteine aspartate-specific proteases (caspase) activity in Huntington disease (HD) and Alzheimer disease (AD) as two representative neurodegenerative disorders that normally manifest in mid- to late-life. Caspases appear to be involved in the molecular pathology of HD by directly cleaving huntingtin and generating toxic protein fragments containing the polyglutamine tract, and by being recruited and activated by polyglutamine-containing aggregates composed mainly of truncated huntingtin fragments. Several proteins involved in AD, including beta-amyloid precursor protein (APP) and presenilins (PSs), are also cleaved by caspases. For APP, caspase cleavage may contribute to toxicity by generating toxic fragments or by shifting APP processing toward an amyloidogenic pathway. For PSs, caspase cleavage disables antiapoptotic functions attributed to PS C-terminal fragments. These observations suggest that caspases actively contribute to the molecular pathogenesis of these diseases and support the development of caspase inhibitors as potential therapeutic approaches for chronic neurodegenerative disorders.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".