A Quantitative Method for the Specific Assessment of Caspase-6 Activity in Cell Culture
Bibliographic record
Abstract
Aberrant activation of caspase-6 has recently emerged as a major contributor to the pathogeneses of neurodegenerative disorders such as Alzheimer's and Huntington disease. Commercially available assays to measure caspase-6 activity commonly use the VEID peptide as a substrate. However these methods are not well suited to specifically assess caspase-6 activity in the presence of other, confounding protease activities, as often encountered in cell and tissue samples. Here we report the development of a method that overcomes this limitation by using a protein substrate, lamin A, which is highly specific for caspase-6 cleavage at amino acid 230. Using a neo-epitope antibody against cleaved lamin A, we developed an electrochemiluminescence-based ELISA assay that is suitable to specifically detect and quantify caspase-6 activity in highly apoptotic cell extracts. The method is more sensitive than VEID-based assays and can be adapted to a high-content imaging platform for high-throughput screening. This method should be useful to screen for and characterize caspase-6 inhibitor compounds and other interventions to decrease intracellular caspase-6 activity for applications in neurodegenerative disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".