High resolution micro‐SPECT scanning in rats using<sup>125</sup>I β‐CIT: Effects of chronic treatment with carbamazepine
Bibliographic record
Abstract
PURPOSE: Carbamazepine (CBZ) is a first-line antiepileptic agent with mood-stabilizing effects in bipolar disorder. It has been reported to influence extracellular concentrations of serotonin and dopamine, suggesting an interaction with monoamine transporters. We have investigated this effect using in vivo single photon emission computed tomography (SPECT) in rats. METHODS: Adult male rats received 3 mg/kg/h CBZ via mini-osmotic pump. After 14 days continuous treatment, animals underwent two consecutive SPECT scans, using 125I beta-CIT as a radiotracer to label serotonin transporter (SERT) and dopamine transporter (DAT) sites in the brain. Pharmacologic distinction was enabled by 125I beta-CIT SPECT imaging in rats acutely exposed to the serotonin and dopamine transporter inhibitors, fluoxetine and GBR12909. The interaction between CBZ and 125I beta-CIT binding to SERT and DAT was investigated using in vitro autoradiography. RESULTS: Carbamazepine (10 microm) did not affect binding of 125I beta-CIT to isolated rat brain slices, thereby excluding a direct effect on ligand binding to SERT and DAT. SPECT studies with fluoxetine and GBR12909 highlighted SERT binding in thalamus, hippocampus, centromedial nuclei, and occipital cortex, and DAT binding in the caudate. Prolonged treatment with CBZ failed to influence 125I beta-CIT binding to either SERT or DAT in any of the brain regions examined. DISCUSSION: This study employed the novel technique of small animal SPECT imaging to investigate the effects of CBZ on monoamine transporters in rat brain. Following prolonged treatment, the drug was without effect on SERT or DAT availability. The mechanism by which CBZ exerts its mood stabilizing effects remains elusive.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".