Are amyloid‐degrading enzymes viable therapeutic targets in Alzheimer’s disease?
Bibliographic record
Abstract
: The amyloid cascade hypothesis of Alzheimer's disease envisages that the initial elevation of amyloid β-peptide (Aβ) levels, especially of Aβ(1-42) , is the primary trigger for the neuronal cell death specific to onset of Alzheimer's disease. There is now substantial evidence that brain amyloid levels are manipulable because of a dynamic equilibrium between their synthesis from the amyloid precursor protein and their removal by amyloid-degrading enzymes (ADEs) providing a potential therapeutic strategy. Since the initial reports over a decade ago that two zinc metallopeptidases, insulin-degrading enzyme and neprilysin (NEP), contributed to amyloid degradation in the brain, there is now an embarras de richesses in relation to this category of enzymes, which currently number almost 20. These now include serine and cysteine proteinases, as well as numerous zinc peptidases. The experimental validation for each of these enzymes, and which to target, varies enormously but up-regulation of several of them individually in mouse models of Alzheimer's disease has proved effective in amyloid and plaque clearance, as well as cognitive enhancement. The relative status of each of these enzymes will be critically evaluated. NEP and its homologues, as well as insulin-degrading enzyme, remain as principal ADEs and recently discovered mechanisms of epigenetic regulation of NEP expression potentially open new avenues in manipulation of AD-related genes, including ADEs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".