In vivo tracking of tau pathology using positron emission tomography (PET) molecular imaging in small animals
Bibliographic record
Abstract
Hyperphosphorylation of the tau protein leading to the formation of neurofibrillary tangles (NFTs) is a common feature in a wide range of neurodegenerative diseases known as tauopathies, which include Alzheimer's disease (AD) and the frontotemporal dementias (FTDs). Although heavily investigated, the mechanisms underlying the pathogenesis and progression of tauopathies have yet to be fully understood. In this context, several rodent models have been developed that successfully recapitulate the behavioral and neurochemical features of tau pathology, aiming to achieve a better understanding of the link between tau and neurodegeneration. To date, behavioral and biochemical parameters assessed using these models have been conducted using a combination of memory tasks and invasive methods such as cerebrospinal fluid (CSF) sampling or post-mortem analysis. Recently, several novel positron emission tomography (PET) radiopharmaceuticals targeting tau tangles have been developed, allowing for non-invasive in vivo quantification of tau pathology. Combined with tau transgenic models and microPET, these tracers hold the promise of advancing the development of theoretical models and advancing our understanding of the natural history of AD and non-AD tauopathies. In this review, we briefly describe some of the most important insights for understanding the biological basis of tau pathology, and shed light on the opportunity for improved modeling of tau pathology using a combination of tau-radiopharmaceuticals and animal models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".