Involvement of Microglial Cathepsin B in Pro-Interleukin-1 Processing and Persistent Pain
Bibliographic record
Abstract
Cathepsin B and neuronal death Neuronal death induced by cathepsin B-secreted from microgliaMicroglia are known to release a number of soluble molecules that can influence neuronal signaling and survival.Kingham and Pocock (2001) focused on cathepsin B, which increased in the culture medium of microglia following stimulation with chromogranin A (CGA), a glycophosphoprotein secreted by degenerating neurons.They demonstrated that cathepsin B is a major causative factor for CGA-activated microglia-induced neuronal apoptosis using neutralizing anti-cathepsin B antibodies.Gan et al. (2004) found that freshly sonicated A42 did not cause neuronal death when added directly to neuron, but activated BV2 microglial cells to release toxic factors that caused significant neuronal death.To determine the toxic molecules secreted from A42-stimulated microglia, they conducted a large scale expression profiling analyses using filter-based cDNA arrays made from BV2 cDNA libraries enriched for A42-activated microglial genes.Cathepsin B was identified to be one of the 554 genes transcriptionally induced by freshly sonicated A42.Furthermore, specific inhibition of cathepsin B using either siRNA-mediated gene silencing or a specific cathepsin B inhibitor completely abolished the neurotoxicity mediated by A42-activated BV2 microglial cells, suggesting that cathepsin B plays a crucial role in neuronal death mediated by A-activated inflammatory responses.However, further studies will be needed to identify the mechanism of secretion of cathepsin B and its extracellular substrates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".