Low striatal serotonin transporter protein in a human polydrug MDMA (ecstasy) user: a case study
Bibliographic record
Abstract
Evidence that the widely used methamphetamine analog MDMA (3,4-methylenedioxymethamphetamine, ecstasy) might damage brain serotonin neurones in humans is derived from imaging investigations showing variably decreased binding of radioligands to the serotonin transporter (SERT), a marker of serotonin neurones. However, in humans, it is not known whether low SERT binding reflects actual loss of SERT protein itself. As this question can only be answered in post-mortem brain, we measured protein levels of SERT and that of the rate-limiting serotonin-synthesizing enzyme tryptophan hydroxylase (TPH) in autopsied brain of a high-dose MDMA user. As compared with control values, SERT protein levels were markedly (-48% to -58%) reduced in striatum (caudate, putamen) and occipital cortex and less affected (-25%) in frontal and temporal cortices, whereas TPH protein was severely decreased in caudate and putamen (-68% and -95%, respectively). The magnitude of the striatal SERT protein reduction was greater than the SERT binding decrease typically reported in imaging studies. Although acknowledging limitations of a case study, these findings extend imaging data based on SERT binding and suggest that high-dose MDMA exposure could cause loss of two key protein markers of brain serotonin neurones, a finding compatible with either physical damage to serotonin neurones or downregulation of components therein.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".