Historical efforts to develop 99mTc-based amyloid plaque targeting radiotracers
Bibliographic record
Abstract
Imaging biomarkers have changed the way we study Alzheimer's disease and related dementias, develop new therapeutics to treat the disease, and stratify patient populations in clinical trials. With respect to protein aggregates comprised of amyloid-β plaques and tau neurofibrillary tangles, Positron Emission Tomography (PET) has become the gold standard imaging modality for quantitative visualization. Due to high infrastructural costs, the availability of PET remains limited to large urban areas within high income nations. This limits access to leading edge medical imaging, and potentially access to new treatments, by millions of rural and remote residents in those regions as well as billions of people in middle- and low-income countries. Single Photon Emission Computed Tomography (SPECT) is a more widely available imaging alternative with lower infrastructural costs and decades of familiarity amongst nuclear medicine professionals. Recent technological advances have closed the gap in spatial resolution and quantitation between SPECT and PET. If effective SPECT radiotracers were available to visualize amyloid-β plaques, geographic barriers to imaging could be circumvented. In this review, we will discuss past efforts to develop SPECT radiotracers targeting amyloid-β plaques which incorporate the most used radionuclide in nuclear medicine: technetium-99m (99mTc; t1/2 = 6.01 h; γ = 140 keV). While reviewing the various chemical scaffolds and chelates employed, the focus will be upon the impact to the pharmacological properties of putative 99mTc-based amyloid-targeting radiotracers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".