Inhibition of the serotonin transporter induces microglial activation and downregulation of dopaminergic neurons in the substantia nigra
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Bibliographic record
Abstract
Drugs that selectively inhibit the serotonin transporter (SERT) are widely used in the treatment of depression and anxiety disorders. These agents are associated with a range of extrapyramidal syndromes such as akathisia, dystonia, dyskinesia and parkinsonism, suggesting an effect on dopaminergic transmission. We studied the time course of changes in dopaminergic neurons in the substantia nigra (SN) after initiation of two different SERT inhibitors, citalopram and fluoxetine. In the first experiment, groups of Sprague-Dawley rats received daily meals of rice pudding either alone (N = 9) or mixed with citalopram 5 mg/kg/day (N = 27). Rats were sacrificed after 24 h, 7 days or 28 days of treatment. Sections of SN were processed for tyrosine hydroxylase (TH) immunohistochemistry. Citalopram induced a significant decrease in TH-positive cell counts at 24 h (44%), 7 days (38%) and 28 days (33%). No significant differences among the citalopram treatment groups were observed in the SN. To determine whether these changes would occur with other SERT inhibitors, we conducted a second experiment, this time with a 28 day course of fluoxetine. As was observed with citalopram, fluoxetine induced a significant 21% reduction of TH cell counts in the SN. Immunoblot analysis showed that fluoxetine also induced a 45% reduction of striatal TH. To investigate a possible role for the innate immune system in mediating these changes, we also studied the microglial marker OX42 after administration of fluoxetine and noted a significant 63% increase in the SN of fluoxtine-treated animals. These results indicate that SERT inhibition can activate microglia and alter the regulation of TH, the rate limiting enzyme for dopamine biosynthesis. These changes may play a role in mediating the extrapyramidal side effects associated with SERT inhibitors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it