Mapping neuroinflammation in frontotemporal dementia with molecular PET imaging
Bibliographic record
Abstract
Recent findings have led to a renewed interest and support for an active role of inflammation in neurodegenerative dementias and related neurologic disorders. Detection of neuroinflammation in vivo throughout the course of neurodegenerative diseases is of great clinical interest. Studies have shown that microglia activation (an indicator of neuroinflammation) may present at early stages of frontotemporal dementia (FTD), but the role of neuroinflammation in the pathogenesis of FTD is largely unknown. The first-generation translocator protein (TSPO) ligand ([(11)C]-PK11195) has been used to detect microglia activation in FTD, and the second-generation TSPO ligands have imaged neuroinflammation in vivo with improved pharmacokinetic properties. This paper reviews related literature and technical issues on mapping neuroinflammation in FTD with positron-emission tomography (PET) imaging. Early detection of neuroinflammation in FTD may identify new tools for diagnosis, novel treatment targets, and means to monitor therapeutic efficacy. More studies are needed to image and track neuroinflammation in FTD. It is anticipated that the advances of TSPO PET imaging will overcome technical difficulties, and molecular imaging of neuroinflammation will aid in the characterization of neuroinflammation in FTD. Such knowledge has the potential to shed light on the poorly understood pathogenesis of FTD and related dementias, and provide imaging markers to guide the development and assessment of new 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".