Discriminatory ability of next-generation tau PET tracers for Alzheimer’s disease
Bibliographic record
Abstract
A next generation of tau PET tracers for the imaging of Alzheimer's disease and other dementias has recently been developed. Whilst the new compounds have now entered clinical studies, there is limited information available to assess their suitability for clinical applications. Head-to-head comparisons are urgently needed to understand differences in the radiotracer binding profiles. We characterized the binding of the tau tracers PI2620, RO948, MK6240 and JNJ067 in human post-mortem brain tissue from a cohort of 25 dementia cases and age-matched controls using quantitative phosphorimaging with tritium-labelled radiotracers in conjunction with phospho-tau specific immunohistochemistry. The four radiotracers depicted tau inclusions composed of paired helical filaments with high specificity, both in cases with Alzheimer's disease and in primary tauopathy cases with concomitant Alzheimer's disease pathology. In contrast, cortical binding to primary tauopathy in cases without paired helical filament tau was found to be within the range of age-matched controls. Off-target binding to monoamine oxidase B has been overcome, as demonstrated by heterologous blocking studies in basal ganglia tissue. The high variability of cortical tracer binding within the Alzheimer's disease group followed the same pattern with each tracer, suggesting that all compounds are suited to differentiate Alzheimer's disease from other dementias.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".