Quantitation of Translocator Protein Binding in Human Brain with the Novel Radioligand [ <sup>18</sup> F]-FEPPA and Positron Emission Tomography
Bibliographic record
Abstract
This article describes the kinetic modeling of [(18)F]-FEPPA binding to translocator protein 18 kDa in the human brain using high-resolution research tomograph (HRRT) positron emission tomography. Positron emission tomography scans were performed in 12 healthy volunteers for 180 minutes. A two-tissue compartment model (2-CM) provided, with no exception, better fits to the data than a one-tissue model. Estimates of total distribution volume (V(T)), specific distribution volume (V(S)), and binding potential (BP(ND)) demonstrated very good identifiability (based on coefficient of variation (COV)) for all the regions of interest (ROIs) in the gray matter (COV V(T)<7%, COV V(S)<8%, COV BP(ND)<11%). Reduction of the length of the scan to 2 hours is feasible as V(S) and V(T) showed only a small bias (6% and 7.5%, respectively). Monte Carlo simulations showed that, even under conditions of a 500% increase in specific binding, the identifiability of V(T) and V(S) was still very good with COV<10%, across high-uptake ROIs. The excellent identifiability of V(T) values obtained from an unconstrained 2-CM with data from a 2-hour scan support the use of V(T) as an appropriate and feasible outcome measure for [(18)F]-FEPPA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".