Dosimetry of [18F]TRACK, the first PET tracer for imaging of TrkB/C receptors in humans
Bibliographic record
Abstract
Abstract Background Reduced expression or impaired signalling of tropomyosin receptor kinases (Trk receptors) are found in a vast spectrum of CNS disorders. [18F]TRACK is the first PET radioligand for TrkB/C with proven in vivo brain penetration and on-target specific signal. Here we report dosimetry data for [18F]TRACK in healthy humans. 6 healthy participants (age 22–61 y, 3 female) were scanned on a General Electric Discovery PET/CT 690 scanner. [18F]TRACK was synthesized with high molar activities (Am = 250 ± 75 GBq/µmol), and a dynamic series of 12 whole-body scans were acquired after injection of 129 to 147 MBq of the tracer. Images were reconstructed with standard corrections using the manufacturer’s OSEM algorithm. Tracer concentration time-activity curves (TACs) were obtained using CT-derived volumes-of-interest. Organ-specific doses and the total effective dose were estimated using the Committee on Medical Internal Radiation Dose equation for adults and tabulated Source tissue values (S values). Results Average organ absorbed dose was highest for liver and gall bladder with 6.1E−2 (± 1.06E−2) mGy/MBq and 4.6 (± 1.18E−2) mGy/MBq, respectively. Total detriment weighted effective dose EDW was 1.63E−2 ± 1.68E−3 mSv/MBq. Organ-specific TACs indicated predominantly hepatic tracer elimination. Conclusion Total and organ-specific effective doses for [18F]TRACK are low and the dosimetry profile is similar to other 18F-labelled radio tracers currently used in clinical settings.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".