<i>In Vivo</i> Measurement of Density and Affinity of the Monoamine Vesicular Transporter in a Unilateral 6-Hydroxydopamine Rat Model of PD
Bibliographic record
Abstract
This is the first in vivo determination of the vesicular monoamine transporter (VMAT2) density (B(max)) and ligand-transporter affinity (K(d)(app)) in six unilaterally 6-hydroxydopamine (6-OHDA) lesioned rats using micro-positron emission tomography (PET) imaging with [(11)C]-(+)-alpha-dihydrotetrabenazine (DTBZ). A multiple ligand concentration transporter assay (MLCTA) was used to determine a B(max) value of 178+/-32 pmol/mL and a K(d)(app) of 47.7+/-9.3 pmol/mL for the non-lesioned side and 30.52+/-5.84 and 43.4+/-15.52 pmol/mL for the lesioned side, respectively. While B(max) was significantly different between the two sides, no significant difference was observed for the K(d)(app). In addition to demonstrating the feasibility of in vivo Scatchard analysis in rats, these data confirm the expectation that a 6-OHDA lesion does not affect the affinity; a much simpler binding potential (BP) measure can thus be used as a marker of lesion severity (LS) in this rat model of Parkinson's disease. A transporter occupancy curve demonstrated negligible transporter occupancy ( approximately 1%) at a specific activity (SA) of 1100 nCi/pmol (assuming an injected dose of 100 microCi/100 g), while 10% occupancy was estimated at 100 nCi/pmol. An indirect measurement indicated that the degree of occupancy as a function of SA is independent of LS. Finally, BP measurement reproducibility was assessed and found to be 11%+/-7% for the healthy and 8%+/-12% for the lesioned side. Quantitative PET results can thus be obtained even for severely lesioned animals with the striatum on one side not clearly visible provided accurate image analysis methods are used.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".