A method for the efficient evaluation of substrate-based cholinesterase imaging probes for Alzheimer’s disease
Bibliographic record
Abstract
Cholinesterase (ChE) enzymes have been identified as diagnostic markers for Alzheimer disease (AD). Substrate-based probes have been synthesised to detect ChEs but they have not detected changes in ChE distribution associated with AD pathology. Probes are typically screened using spectrophotometric methods with pure enzyme for specificity and kinetics. However, the biochemical properties of ChEs associated with AD pathology are altered. The present work was undertaken to determine whether the Karnovsky-Roots (KR) histochemical method could be used to evaluate probes at the site of pathology. Thirty thioesters and esters were synthesised and evaluated using enzyme kinetic and KR methods. Spectrophotometric methods demonstrated all thioesters were ChE substrates, yet only a few provided staining in the brain with the KR method. Esters were ChE substrates with interactions with brain ChEs. These results suggest that the KR method may provide an efficient means to screen compounds as probes for imaging AD-associated ChEs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".