The Role of the Selective Serotonin Reuptake Inhibitor Fluoxetine in Temperature Regulation in Ovariectomized Rat Models
Bibliographic record
Abstract
Thermoregulation is an integrated network of neuroendocrine, autonomic and somatosensory responses. Thermoregulatory dysfunction occurs during fluctuations or decline of gonadal hormone levels and results in vasomotor symptoms such as hot flushes and/or night-time sweating. The neurotransmitter serotonin (5-HT), has been reported to play a role in thermoregulation via changes in extracellular 5-HT levels and/or activation of various 5-HT receptors. The purpose of this study was to evaluate the role of the selective 5-HT reuptake inhibitor (SSRI), fluoxetine (FLX), on temperature regulation using ovariectomized (OVX) rodent models of thermoregulation. Single, subcutaneous (s.c.) administration of FLX (3, 10, 30 and 60 mg/kg) dose-dependently reduced core body temperature (CBT). FLX at 3 and 10 mg/kg s.c. showed no statistically significant decrease on tail-skin temperature (TST), whereas at higher doses (30 and 60 mg/kg) a significant decrease in TST was noted in the telemetry model. To mimic chronic SSRI treatment, a 5-HT(1A) antagonist (WAY-100635; 0.3 mg/kg) was administered 20 min prior to FLX (10 mg/kg). This combination showed no significant improvement on temperature dysfunction compared to FLX alone. Similarly, in a morphine-dependent model of temperature dysfunction FLX, was inactive at 10 mg/kg whereas the 30 and 60 mg/kg s.c. dose abated the naloxone-induced increase in TST by 55 and 81%, respectively. In summary, FLX affected CBT at all doses, but alleviated thermoregulatory dysfunction only at higher doses that are non-selective for the 5-HT system.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".