Internalization of serotonin 5‐HT<sub>1A</sub>autoreceptors as an imaging biomarker of antidepressant response
Bibliographic record
Abstract
Abstract Serotonin (5‐hydroxytryptamine, 5‐HT) and its various receptors are involved in numerous CNS functions. Among the currently known 5‐HT receptors, the 5‐HT1Areceptor is the best characterized subtype. It is tightly implicated in the pathogenesis of mood disorders, notably in depression, and thus represents an important target for drug therapy. Binding to 5‐HT1Areceptors can be visualized and quantified by positron emission tomography (PET), facilitating the translation from animal research to man. Using the 5‐HT1Aradiotracer [18F]MPPF, recent PET studies in cat and human have provided evidence that internalization of 5‐HT1Aautoreceptors is amenable toin vivoneuroimaging at the very onset of specific serotonin reuptake inhibitor (SSRI) administration. Thein vivodetection of this phenomenon in human is promising in terms of clinical management, particularly as an early biomarker of responsiveness to SSRI treatment. However, several questions are still pending regarding the correlation between 5‐HT1Ainternalization at the onset of the treatment and the ensuing therapeutic efficacy.WIREs Membr Transp Signal2012,1:239–245. doi: 10.1002/wmts.11 For further resources related to this article, please visit the WIREs website .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".