The development and validation of tau PET tracers: current status and future directions
Bibliographic record
Abstract
To provide an overview on positron emission tomography (PET) imaging of tau pathology in Alzheimer’s disease (AD) and other neurodegenerative disorders. Different classes of tau tracers such as flortaucipir, THK5317, and PBB3 have been developed and utilized in previous clinical studies. In AD, the topographical distribution of tracer binding follows the known distribution of neurofibrillary tangles and is closely associated with neurodegeneration as well as the clinical phenotype of dementia. Significant retention of tracers has also been observed in the frequent site of the 4-repeat (4R) tau isoform deposits in non-AD tauopathies, such as in progressive supranuclear palsy. However, in vitro binding studies indicate that most tau tracers are less sensitive to straight tau filaments, in contrast to their high binding affinity to paired helical filaments of tau (PHF-tau). The first-generation of tau tracers shows off-target binding in the basal ganglia, midbrain, thalamus, choroid plexus, and venous sinus. Off-target binding of THK5351 to monoamine oxidase B (MAO-B) has been observed in disease-associated brain regions linked to neurodegeneration and is associated with astrogliosis in areas of misfolded protein accumulation. The second generation of tau tracers, such as [ 18 F]MK-6240, is highly selective to PHF-tau with little off-target binding and have enabled the reliable assessment of PHF-tau burden in aging and AD. Tau PET tracers have enabled in vivo quantification of PHF-tau burden in human brains. Tau PET can help in understanding the underlying cause of dementia symptoms, and in patient selection for clinical trials of anti-dementia therapies.
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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 | 0.025 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".