Bibliographic record
Abstract
Recent studies suggest that caspases are involved in neuronal apoptosis and Alzheimer's disease (AD). However, which effector caspase plays an essential role in AD pathogenesis is still elusive. Caspase-6 is implicated as a main effector caspase in human primary neuron culture (LeBlanc et al., 1999; Zhang et al., 2000). Therefore, the first objective of my study is to investigate whether caspase-6 is involved in AD. We generated an antiserum (1277) recognizing the p20 subunit of caspase-6. By using the 1277 antiserum, we investigate fifteen AD and eight control brains from Canadian brain tissue bank. We observe 2-3 fold increase of p20 subunit of caspase-6 in AD (p<0.03, ANOVA) compared to controls. The results imply caspase-6 activation in AD. Although caspase-6 is thought to be essential in neuronal apoptosis and AD, it is elusive as to how caspase-6 is activated. The second objective of my study is to determine if another caspase is activating caspase-6. We observe that caspase-1 activation precedes caspase-6 activity in serum-deprived human neurons. A caspase-1 inhibitor prevents caspase-6 activation and resultant cell death. These results suggest that caspase-1 is essential in caspase-6 activation in apoptotic human neurons. Our study demonstrates the involvement of caspase-6 in AD and the role of caspase-1 in caspase-6 activation, which may help find a possible treatment for AD in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".