Analysis of APL1β28, a Surrogate Marker for Alzheimer Aβ42, Indicates Altered Precision of γ-Cleavage in the Brains of Alzheimer Disease Patients
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common cause of dementia in the elderly. Currently, therapeutic intervention after the disease onset is difficult because progressive neuronal death precedes clinical symptoms. Available medicines for AD, such as AchE inhibitors, transiently slow the progression of the dementia symptoms, but they do not inhibit the pathological process. At present, next generation anti-AD drugs are in development in many pharmaceutical companies. Importantly, most of them are to inhibit the progress of the pathological process and, thus, at the same time, the establishment of a highly probable prediction of future AD onset is inseparable. AD is now diagnosed using clinical criteria coupled with brain imaging systems such as SPECT and PET. To diagnose AD cases before the appearance of clinical symptoms, it will be necessary to (a) establish new, more sensitive clinical criteria, (b) develop methods for detecting the pathological accumulation of proteins (e.g. Abeta) in the brain, or (c) develop biomarkers for predicting the accumulation of Abeta/tau in the brain. Our recent discovery of APL1beta28, a possible biomarker of AD, may help in the development of early detection methods for AD.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".