Chronic fluoxetine treatment has a larger effect on the density of a serotonin transporter in the Flinders Sensitive Line (FSL) rat model of depression than in normal rats
Bibliographic record
Abstract
The 5-hydroxytryptamine system is thought to play a crucial role in the pathophysiology of depression and represents the target for selective 5-HT reuptake inhibitors (SSRIs). Flinders Sensitive Line (FSL) and Flinders Resistant Line (FRL) rats were bred from Sprague-Dawley (SPD) rats to produce strains with increased (FSL) or decreased (FRL) sensitivity to the cholinesterase inhibitor. The FSL rats have been identified as a good model of depression. Many studies in normal rats showed that chronic treatments with SSRIs reduce the densities of SERT. The objective of the present investigation was to assess the influence of chronic fluoxetine treatment on SERT density (Bmax; fmol/mg) in the FSL rat model of depression, relative to that in the FRL rats and SPD rats. FSL, FRL and SPD rats were randomly assigned into groups receiving the vehicle or 10 mg/kg of fluoxetine i.p. for 14 days. Binding was assessed by incubating the brain sections in a buffer containing 20 pM of [(125)I]-RTI-55 [[(125)I](-)-2beta-carbomethoxy-3beta-(4-iodophenyl)tropane and 200 nM of GBR12935 [1-(2-(diphenylmethoxy)ethyl)-4-(3-phenylpropyl)piperazine]. The fluoxetine treatment reduced B(max) in all three rat strains when the saline and respective fluoxetine groups were compared (e.g., the FSL-SAL relative to FSL-FLX groups). Chronic fluoxetine treatment reduces the densities of SERT in the FSL rats to a larger extent than in the normal SPD control rats.
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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) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".