Caspase‐generated neoepitopes as markers of axonal degeneration in neural development and injury
Bibliographic record
Abstract
Neuronal death, axonal degeneration, and synapse loss are hallmarks of central nervous system development and injury. Commonly employed markers of apoptosis are TUNEL and immunohistochemistry for activated caspases, but these often fail to report axonal degeneration associated with somatic apoptosis. Axonal degeneration is classically detected by silver degeneration stains, but these have limitations (non‐selectivity, minimal reproducibility and incompatibility with co‐labeling). We demonstrate that antibodies to caspase‐cleaved protein substrates beta‐actin (cleaved by caspase 3 at D244) and alpha‐tubulin (cleaved by caspase 6 at D438) enable superior visualization of neurite degeneration. These antibodies label apoptotic cell bodies and neurites during embryonic and postnatal developmental pruning. In addition, they reveal injured neurons and neurites after induction of apoptosis in a mouse model of fetal alcohol syndrome and in sympathetic neuron cultures induced to undergo apoptosis after nerve growth factor withdrawal. Finally, the markers may have clinical utility, as they highlight degenerating neurites in neonatal hypoxic/ischemic injury and in adult patients with multiple sclerosis. Ongoing work aims to identify novel compartment‐specific neoepitopes that will allow monitoring of somatodendritic, axonal, and synaptic remodeling and degeneration. Supported by NS075869.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".