Cleavage of AL amyloid proteins and AL amyloid deposits by cathepsins B, K, and L
Bibliographic record
Abstract
Cathepsin (Cath) B, CathK and CathL are cysteine proteases that participate in the lysosomal protein degradation system and are expressed in macrophages, epithelioid cells, and multinucleated histiocytic giant cells (MGCs). Both macrophages and MGCs are commonly found adjacent to immunoglobulin light chain-associated (AL) amyloid deposits, which raised the question of whether cysteine proteases are able to cleave AL amyloid proteins and AL amyloid deposits. The present study has investigated whether recombinant human CathB, CathK, and CathL are able to degrade AL(VlambdaVI) amyloid proteins and AL amyloid deposits. Using immunohistochemistry, CathB, CathK, and CathL were found adjacent to AL amyloid deposits. In vitro degradation experiments using purified AL amyloid proteins showed that CathB, CathK, and CathL degrade AL(VlambdaVI) amyloid proteins. Furthermore, using unfixed tissue sections from an amyloidotic spleen as an in vitro model for extracellular proteolysis of intact amyloid deposits, it was demonstrated that all three cysteine proteases are also capable of degrading AL amyloid in situ. This is the first study to show that cysteine proteases are able to cleave AL amyloid proteins. However, the efficiency with which proteolysis occurs depends on the concentration of active protease recruited at the sites of amyloid deposition, and possibly on the structure of the AL amyloid proteins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".