Sequential versus Nonsequential Measurement of Density and Affinity of Dopamine D2 Receptors with [<sup>11</sup>C]Raclopride: 2: Effects of DAT Inhibitors
Bibliographic record
Abstract
The multiple ligand concentration receptor assays (MLCRA) method allows, in a stable condition, reliable and reproducible measurements of the density and affinity of the dopamine (DA) D2 receptors with [11C]raclopride, using either a sequential method (two or more scans in one day) or a nonsequential method (two or more scans over days or weeks). We have shown that measurement of receptor density and affinity is also possible after an acute pharmacological challenge with methamphetamine and that both scanning protocols yield similar values. However, our attempts to measure receptor density and affinity after a pharmacological challenge with another class of drugs that lead to the same outcome, increase in synaptic DA concentrations, revealed opposite results with the two scanning methods: a decrease in receptor density with the sequential method and an increase in affinity with a nonsequential method. These results show the impact of the time-dependency of the effects of an 'acute' pharmacological challenge on MLCRA studies. A theoretical simulation is presented to account for the discrepancy in the sequential and nonsequential data. A possible alternate scanning paradigm is proposed to avoid the confounding effect of time variability of the endogenous ligand synaptic concentrations in the sequential condition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".