Simple In Vitro Assays to Identify Amyloid-β Aggregation Blockers for Alzheimer's Disease Therapy
Bibliographic record
Abstract
Compounds that will inhibit buildup of amyloid-beta(Abeta) deposits in Alzheimer's disease (AD) brain are potential therapeutic agents. Here we report the development of two simple in vitro screening assays to identify such agents. We use these assays to evaluate the relative potency of some possible candidates. One assay is based on binding of fluorescence-tagged Abeta{1-42} to synthetic Abeta{1-42} plated in wells of fluorescent black-wall microplates. Fluorescence-tagged Abeta{1-42} solutions with and without blockers are then added to the plates, and the amount of bound fluorescence is measured. Another is a tissue type assay, where sections of unfixed AD or AD model transgenic mouse brains are mounted on glass slides. The same solutions assayed in the microplate test are then added to tissue sections. Binding of fluorescence-tagged Abeta{1-42} to the Abeta deposits in AD or transgenic brain tissue is detected with a fluorescence microscope. Good agreement is obtained between the two methods. Most of the tested agents have too low an affinity for Abeta {1-42} to be effective clinically. Agents that may have marginal affinity according to these tests include 1,2,3,4,6-penta-O-galloyl-b-D-glucopyranose (PGG), S-diclofenac, epigallocatechin gallate (EGCG), resveratrol, and extracts of spirulina, ginger, rhubarb, cinnamon, blueberries, and turmeric. Compounds which failed to show binding include scyllo-inositol, myo-inositol, rhamnose, ginkgolide A, emodin, rhein, caryophellene, curcumin, valproic acid, tramiprosate, and garlic extract.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".