PET quantification of muscarinic cholinergic receptors with [N‐<sup>11</sup>C‐methyl]‐benztropine and application to studies of propofol‐induced unconsciousness in healthy human volunteers
Bibliographic record
Abstract
This work evaluated kinetic analysis methods for estimation of the receptor availability of the muscarinic receptor using dynamic positron emission tomography (PET) studies with [N-(11)C-methyl]-benztropine. The study also investigated the effect of propofol on central muscarinic receptor availability during general anesthesia. Six volunteers were scanned three times, once for baseline while awake, once during unconsciousness, and once after recovery to conscious level. An irreversible two-tissue compartment model was used to estimate the [N-(11)C-methyl]-benztropine specific binding rate constant k(3), a measure of muscarinic receptor availability. Two different estimation methods were used: 1) optimization with positivity constraints on all the parameters; 2) optimization with additional constraints determined from a one-tissue compartment fit to the cerebellum. In regions with low to middle muscarinic receptor density, the k(3) values from method (2) had lower standard errors than that for method (1) and gave a higher correlation with the density of muscarinic receptors measured in human tissue by in vitro studies (r(2) of 0.98 for Method 2 and r(2) of 0.72 for Method 1). But the k(3) values determined by Method 2 had higher errors for regions with high muscarinic receptor density compared to Method 1. For both methods the mean k(3) values during unconsciousness were generally lower than those during awake for most regions evaluated. Therefore, the method with additional constraints derived from the cerebellum (Method 2) was deemed superior for regions with low to middle muscarinic receptor density, while the method with positivity constraint is the better choice in the regions with high muscarinic receptor density. Our results also suggest the existence of propofol-related reductions in muscarinic receptor availability.
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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 | 0.001 | 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 | 0.000 | 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".