In vivo metabolism and partitioning of 6-[18F]fluoro-L-meta-tyrosine in whole blood: a unified compartment model
Bibliographic record
Abstract
Physiological quantification of dynamic PET data requires the determination of an input function, preferably from plasma. A compartmental model relating a parent radiotracer, its radiolabelled metabolites and their exchange between plasma and erythrocytes is presented. This model allows for the time course of radioactivity measured in whole blood to be transformed into the time course of the radiotracer in plasma. The utility of this approach is illustrated with blood data collected on 30 human subjects injected with 6-[18F]fluoro-L-meta-tyrosine (FmT), a pre-synaptic dopaminergic radiotracer. A three-compartment four-parameter model is shown to yield significantly better fits to the blood data than related lower and higher order models. This model is found to be robust to measurement noise, and yet sensitive to metabolic changes induced by pretreatment with carbidopa. For FmT, the between-subject variations are shown to be small enough to warrant the use of a population-based correction; tissue time-activity curves were simulated to verify that this correction does not significantly affect the precision and accuracy of the derived rate constants. The unified blood model can be adapted for radiotracers other than FmT as long as the blood partition ratio of the parent radiotracer differs from that of its metabolites and/or the rate at which they equilibrate between plasma and erythrocytes is different.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".