Positron Emission Tomography Quantification of [ <sup>11</sup> C]-DASB Binding to the Human Serotonin Transporter: Modeling Strategies
Bibliographic record
Abstract
[(11) C]-DASB, namely [(11) C]-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile, is a new highly selective radioligand for the in vivo visualization of the serotonin transporter (SERT) using positron emission tomography (PET). The current study evaluates different kinetic modeling strategies for quantification of [(11)C]-DASB binding in five healthy humans. Kinetic analyses of tissue data were performed with a one-tissue (1CM) and a two-tissue (2CM) compartment model. Time-activity curves were well described by a 1CM for all regions. A 2CM model with four parameters failed to converge reliably. Reliable fits of the data were obtained only if no more than three parameters were allowed to vary. However, even then, the rate constants k(3) and k(4) were estimated with poor precision. Only the ratio k(3)/k(4) was stable. Goodness of fit was not improved by using a 2CM as compared with a 1CM. The minimal study duration required to obtain stable k(3)/k(4) estimates was 80 minutes. For routine use of [(11)C]-DASB, several simplified methods using the cerebellum as a reference region to estimate nonspecific binding were also evaluated. The transient equilibrium, the linear graphical analysis, the ratio of target to reference region, and the simplified reference tissue methods all gave binding potential values consistent with those obtained with the 2CM. The suitability of [(11)C]-DASB for research on the SERT using PET is thus supported by the observations that tissue data can be described using a kinetic analysis and that simplified quantitative methods, using the cerebellum as reference, provide reliable estimates of SERT binding parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".