Thirty-five Day Fluoxetine Treatment Limits Sensory-Motor Deficit and Biochemical Disorders in a Rat Model of Decompression Sickness
Bibliographic record
Abstract
According to the OECD statistical base for 2014, antidepressants will, on average, be distributed at a rate of 62 daily doses per 1000 inhabitants for the 25 countries surveyed (Health at a glance: Europe 2014; OECD Health Statistics). Divers must be concerned. On another hand, divers are potentially exposed to decompression sickness including coagulation inflammation and ischaemia, which can result in neurological lesions or even death. The purpose of this study is to assess whether chronic treatment with antidepressants may represent a contraindication to the practice of an at-risk activity, such as scuba diving, or even presents a benefit by attenuating the severity of the symptoms. We study for the first time the effect of a 35-day fluoxetine treatment (20 mg/kg) on the occurrence of decompression sickness in laboratory rats (n=79). Following exposure to the hazardous protocol, there is a significant correlation between the type of treatment and the clinical status of the rats in favour of a better clinical prognosis for the rats treated with fluoxetine with a significantly higher number of No DCS status and a lower number of Severe DCS status in the Flux, compared to Controls. The treatment modifies the rat performances both significantly and favourably during the physical and behavioural tests, just like their biological and biochemical constants. After decompression, rats under treatment display lower sensory-motor deficit and lowers biochemical disorders. From a biological point of view, we conclude fluoxetine should not be seen as a contraindication for diving on the basis of anticipated increased physiological risk.
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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 | 0.001 |
| 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".