Are amyloid‐degrading enzymes viable therapeutic targets in Alzheimer’s disease?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it