[<sup>11</sup>C]‐NS 4194 versus [<sup>11</sup>C]‐DASB for PET imaging of serotonin transporters in living porcine brain
Bibliographic record
Abstract
In vitro, the novel diazabicyclononane NS 4194 has several thousand-fold selectivity for blocking the transport into rat brain synaptosomes of [(3)H]-serotonin in comparison to [(3)H]-dopamine or [(3)H]-noradrenaline. We have prepared [(11)C]-NS 4194 in order to test its properties for PET imaging of brain serotonin transporters in comparison with the well-documented tracer [(11)C]-DASB. Both compounds had rapid clearance from blood to brain of living pigs. The apparent equilibrium distribution volumes in cerebellum were 35 ml g(-1) for [(11)C]-NS 4194 and 11 ml g(-1) for [(11)C]-DASB. Pretreatment of pigs with citalopram did not reduce the uptake of either tracer in cerebellum, validating the use of that tissue as a nonbinding reference tissue for kinetic analysis of specific binding. The binding potential (pB) calculated for [(11)C]-NS 4194 using arterial input models was close to 0.5 in the telencephalon, and was 60% displaced by citalopram. However, the reference tissue method of Lammertsma was unsuited to calculate pB for this tracer, apparently due to its excessive nonspecific binding. In contrast to the relatively homogeneous binding of [(11)C]-NS 4194, the pB of [(11)C]-DASB ranged from 0.6 in frontal cortex to 2 in the mesencephalon when calculated by the method of Lammertsma. Parametric maps of the pB of [(11)C]-DASB showed a pattern consistent with the known distribution of serotonin transporters in pig brain in vitro, and there was a uniform displacement of 80% of the specific binding after citalopram treatment in vivo. In conclusion, [(11)C]-DASB is in several respects superior to [(11)C]-NS 4194 for the detection of serotonin uptake sites by PET.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".