<i>In-vivo</i> Measurement of LDOPA Uptake, Dopamine Reserve and Turnover in the Rat Brain Using [ <sup>18</sup> F]FDOPA PET
Bibliographic record
Abstract
Longitudinal measurements of dopamine (DA) uptake and turnover in transgenic rodents may be critical when developing disease-modifying therapies for Parkinson's disease (PD). We demonstrate methodology for such measurements using [(18)F]fluoro-3,4-dihydroxyphenyl-L-alanine ([(18)F]FDOPA) positron emission tomography (PET). The method was applied to 6-hydroxydopamine lesioned rats, providing the first PET-derived estimates of DA turnover for this species. Control (n=4) and unilaterally lesioned (n=11) rats were imaged multiple times. Kinetic modeling was performed using extended Patlak, incorporating a k(loss) term for metabolite washout, and modified Logan methods. Dopaminergic terminal loss was measured via [(11)C]-(+)-dihydrotetrabenazine (DTBZ) PET. Clear striatal [(18)F]FDOPA uptake was observed. In the lesioned striatum the effective DA turnover increased, shown by a reduced effective distribution volume ratio (EDVR) for [(18)F]FDOPA. Effective distribution volume ratio correlated (r>0.9) with the [(11)C]DTBZ binding potential (BP(ND)). The uptake and trapping rate (k(ref)) decreased after lesioning, but relatively less so than [(11)C]DTBZ BP(ND). For normal controls, striatal estimates were k(ref)=0.037±0.005 per minute, EDVR=1.07±0.22 and k(loss)=0.024±0.003 per minute (30 minutes turnover half-time), with repeatability (coefficient of variation) ≤11%. [(18)F]fluoro-3,4-dihydroxyphenyl-L-alanine PET enables measurements of DA turnover in the rat, which is useful for developing novel therapies for PD.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".