Metal-based imaging agents: progress towards interrogating neurodegenerative disease
Bibliographic record
Abstract
Central nervous system (CNS) neurodegeneration is defined by a complex series of pathological processes that ultimately lead to death. The precise etiology of these disorders remains unknown. Recent efforts show that a mechanistic understanding of the malfunctions underpinning disease progression will prove requisite in developing new treatments and cures. Transition metals and lanthanide ions display unique characteristics (i.e., magnetism, radioactivity, and luminescence), often with biological relevance, allowing for direct application in CNS focused imaging modalities. These techniques include positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), and luminescent-based imaging (LumI). In this Tutorial Review, we have aimed to highlight the various metal-based imaging techniques developed in the effort to understand the pathophysiological processes associated with neurodegeneration. Each section has been divided so as to include an introduction to the particular imaging technique in question. This is then followed by a summary of key demonstrations that have enabled visualization of a specific neuropathological biomarker. These strategies have either exploited the high binding affinity of a receptor for its corresponding biomarker or a specific molecular transformation caused by a target species, all of which produce a concomitant change in diagnostic signal. Advantages and disadvantages of each method with perspectives on the utility of molecular imaging agents for understanding the complexities of neurodegenerative disease are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".