Levodopa effect on [18F]fluorodopa influx to brain: normal volunteers and patients with Parkinson's disease
Bibliographic record
Abstract
OBJECTIVES: Levodopa is the immediate precursor of dopamine and the substrate for DOPA decarboxylase, an enzyme subject to regulation in living brain. To test whether this regulation changes in disease, we used Positron Emission Tomography (PET) with parametric mapping to measure the effect of levodopa on the net clearance of [(18)F]fluorodopa to brain (K, ml/g/min). METHODS: Five patients with early Parkinson's disease with pause of medication for 3 days and six age-matched healthy volunteers were studied in a baseline condition and after levodopa challenge. RESULTS: Levodopa (200 mg as Sinemet) increased the magnitude of the net clearance K in the left and right putamen of the healthy volunteers by 11% relative to the baseline condition. In contrast, resumption of medication with levodopa did not significantly alter the magnitude of K in putamen of the Parkinson's disease patients. Compartmental analysis was used to probe the physiological basis of the activation of K: levodopa treatment increased by 15% the apparent distribution volume of [(18)F]fluorodopa in cerebellum (, ml/g) of both patients and control subjects, without significantly altering the unidirectional blood-brain clearance (, ml/g/min) or the relative activity of DOPA decarboxylase (, min(-1)) in putamen. CONCLUSION: We conclude that levodopa treatment increases the distribution volume of [(18)F]fluorodopa in brain, increasing its availability for utilization in dopamine terminals. We speculate that levodopa act as a direct beta-adrenergic agonist at receptors regulating the permeability of the blood-brain barrier to levodopa. However, the PET analytical method was without sufficient power to detect the consequent increase in magnitude of K in brain of only five Parkinson's disease subjects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".