Fluoxetine stimulates anti-inflammatory IL-10 cytokine production and attenuates sensory deficits in a rat model of decompression sickness
Bibliographic record
Abstract
Despite "gold standard" hyperbaric oxygen treatment, 30% of patients suffering from neurological decompression sickness still exhibit incomplete recovery, including sensory impairments. Fluoxetine, a well-known antidepressant, is recognized as having anti-inflammatory effects in the setting of cerebral ischemia. In this study, we focused on the assessment of sensory neurological deficits and measurement of circulating cytokines after decompression in rats treated or not with fluoxetine. Seventy-eight rats were divided into a clinical (n = 38) and a cytokine (n = 40) group. In both groups, the rats were treated with fluoxetine (30 mg/kg po, 6 h beforehand) or with a saccharine solution. All of the rats were exposed to 90 m seawater for 45 min before staged decompression. In the clinical group, paw withdrawal force after mechanical stimulation and paw withdrawal latency after thermal stimulation were evaluated before and 1 and 48 h after surfacing. At 48 h, a dynamic weight-bearing device was used to assess postural stability, depending on the time spent on three or four paws. For cytokine analysis, blood samples were collected from the vena cava 1 h after surfacing. Paw withdrawal force and latency were increased after surfacing in the controls, but not in the fluoxetine group. Dynamic weight-bearing assessment highlighted a better stability on three paws for the fluoxetine group. IL-10 levels were significantly decreased after decompression in the controls, but maintained at baseline level with fluoxetine. This study suggests that fluoxetine has a beneficial effect on sensory neurological recovery. We hypothesize that the observed effect is mediated through maintained anti-inflammatory cytokine IL-10 production.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".