Synthesis and preliminary evaluation of 2‐arylhydroxyquinoline derivatives for tau imaging
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common cause of dementia. Senile plaques, consisting of β-amyloid, and neurofibrillary tangles (NFTs), composed of tau protein, are representative pathological hallmarks of AD. It is believed that the accumulation of NFTs precedes the onset of clinical symptoms of AD and correlates with the progression of memory dysfunction. Thus, the use of noninvasive detection techniques including radiolabeled probes and positron emission tomography (PET) will facilitate early diagnosis or staging of AD. In this study, we synthesized and evaluated novel hydroxylated 2-arylquinoline derivatives as tau imaging PET probes. The binding affinities of compounds for tau were evaluated by fluorescent staining of the AD hippocampal section and a competitive binding assay using [(18) F]THK-523. THK-951 showed high binding affinity for tau pathology in an AD brain section and K18Δ280K fibrils (Ki = 20.7 nM); thus, we radiosynthesized a (11) C-labeled THK-951 and further studied its potential as a tau PET probe. The [(11) C]THK-951 demonstrated excellent kinetics in a normal mouse brain (3.23% ID/g at 2 min postinjection and 0.15% ID/g at 30 min postinjection) and showed the labeling of NFTs in an AD brain section by autoradiography assay. These findings indicate the availability of [(11) C]THK-951 for in vivo PET imaging of tau pathology in 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".