In vitro characterization of the serotonin biosynthesis pathway by CEST MRI
Bibliographic record
Abstract
Purpose The diagnosis of monoamine‐related psychiatric disorders is based on the phenomenological evaluation of symptoms and behavior by trained clinicians. The CEST technique can be sensitive to monoamines such as serotonin. This study quantifies the CEST properties of the compounds in the serotonin biosynthesis pathway with the goal of developing noninvasive techniques aimed at advancing the diagnostic assessment of serotonin dysfunction. Methods Saturation transfer‐weighted images of L‐tryptophan, 5‐hydroxytryptophan, serotonin, 5‐hydroxyindoleacetic acid, and melatonin phantoms were acquired over a range of saturation amplitudes and frequency offsets along with observed T1, T2, and B1 efficiency maps at physiological temperature and pH of 5.5, 6.7, and 7.4. The CEST and MT data were fitted to a three‐pool Bloch–McConnell model of exchange to estimate the model parameters. Results At a pH of 5.5, tryptophan, 5‐hydroxytryptophan and serotonin exhibited significant CEST contrast at resonance frequency offset, Δω between 2.64 ppm and 2.71 ppm, and magnetization transfer ratio asymmetry amplitudes up to 20% per 30 mM. At a pH of 7.4, all molecules exhibited significant CEST contrast between 5.11 ppm and 5.47 ppm, and magnetization transfer ratio asymmetry amplitudes up to 9.5% per 30 mM. At a pH of 6.7, all studied compounds except melatonin exhibited a CEST peak from each of the preceding two pHs. Conclusion At a pH of 5.5, tryptophan, 5‐hydroxytryptophan, and serotonin CEST contrast originates from the side chain, whereas at a pH of 7.4, CEST contrast is due to the chemical exchange between water and the NH proton on the indole ring. The data in this study could be used to inform future investigations aimed at detecting and measuring in vivo serotonin.
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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) | 0.000 | 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) | 0.001 | 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".