Loss of metabolites from monkey striatum during PET with FDOPA
Bibliographic record
Abstract
The decarboxylation of 6-[(18)F]fluorodopa (FDOPA) and retention of the product [(18)F]fluorodopamine within vesicles of catecholamine fibers results in the labeling of dopamine-rich brain regions during FDOPA/PET studies. However, this metabolic trapping is not irreversible due to the eventual diffusion of [(18)F]fluorodopamine metabolites from brain. Consequently, time-radioactivity recordings of striatum are progressively influenced by metabolite loss. In linear analyses, the net blood-brain clearance of FDOPA (K(D)(i), ml g(-1) min(-1)) can be corrected for this loss by the elimination rate constant k(Lin)(cl) (min(-1)). Similarly, the DOPA decarboxylation rate constant (k(D)(3), min(-1)) calculated by compartmental analysis can also be corrected for metabolite loss by the elimination rate constant k(DA)(9) (min(-1)). To compare the two methods, we calculated the two elimination rate constants using data recorded during 240 min of FDOPA circulation in normal monkeys and in monkeys with unilateral 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) lesions. Use of the extended models increased the magnitudes of K(D)(i) and k(D)(3) in striatum; in the case of k(D)(3), variance of the estimate was substantially improved upon correction for metabolite loss. The rate constants for metabolite loss were higher in MPTP-lesioned monkey striatum than in normal striatum. The high correlation between individual estimates of k(Lin)(cl) and k(DA)(9) suggests that both rate constants reveal loss of decarboxylated metabolites from brain.
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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) | 0.000 | 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) | 0.000 | 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".