Central 5-Hydroxytryptamine-2A Receptor Expression in Transgenic Mice Bearing a Glucocorticoid Receptor Antisense
Bibliographic record
Abstract
Transgenic mice bearing a transgene coding for a glucocorticoid receptor antisense mRNA that partially blocks glucocorticoid receptor expression were used to investigate the long-term effect of hypothalamic-pituitary-adrenal dysfunction on brain 5-hydroxytryptamine-2A (5-HT2A) receptor expression. The brain 5-HT2A receptor mRNA levels in transgenic mice were measured by in situ hybridization and compared to those in control mice. We also studied the effect of a 3-week treatment with fluoxetine on brain 5-HT2A receptor expression in the transgenic mice. No difference in 5-HT2A mRNA levels was observed between transgenic and control mice in cortical or striatal regions, and fluoxetine treatment was without effect. No difference in hypothalamic 5-HT2A mRNA levels was observed between transgenic and control mice, while fluoxetine treatment increased these levels in both transgenic as well as in the hypothalamic ventromedial and paraventricular nuclei of control mice. 5-HT2A receptor mRNA levels were similar in hippocampal CA1 and CA2 subregions of control and transgenic, but were lower in the CA3 and CA4 subregions of transgenic mice. Fluoxetine had no effect on 5-HT2A mRNA levels of transgenic mice but reduced control mouse 5-HT2A receptor mRNA levels in the CA3 subregion. These results suggest that impaired glucocorticoid receptor function can affect hippocampal 5-HT2A receptor expression in transgenic mice and that this is not corrected by fluoxetine treatment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".