Antidepressant fluoxetine suppresses neuronal growth from both vertebrate and invertebrate neurons and perturbs synapse formation between <i>Lymnaea</i> neurons
Bibliographic record
Abstract
Current treatment regimes for a variety of mental disorders involve various selective serotonin reuptake inhibitors such as Fluoxetine (Prozac). Although these drugs may 'manage' the patient better, there has not been a significant change in the treatment paradigm over the years and neither have the outcomes improved. There is also considerable debate as to the effectiveness of various selective serotonin reuptake inhibitors and their potential side-effects on neuronal architecture and function. In this study, using mammalian cortical neurons, a dorsal root ganglia cell line (F11 cells) and identified Lymnaea stagnalis neurons, we provide the first direct and unequivocal evidence that clinically relevant concentrations of Fluoxetine induce growth cone collapse and neurite retraction of both serotonergic and non-serotonergic neurons alike in a dose-dependent manner. Using intracellular recordings and calcium imaging techniques, we further demonstrate that the mechanism underlying Fluoxetine-induced effects on neurite retraction from Lymnaea neurons may involve lowering of intracellular calcium and a subsequent retardation of growth cone cytoskeleton. Using soma-soma synapses between identified presynaptic and postsynaptic Lymnaea neurons, we provide further direct evidence that clinically used concentrations of Fluoxetine also block synaptic transmission and synapse formation between cholinergic neurons. Our study raises alarms over potentially devastating side-effects of this antidepressant drug on neurite outgrowth and synapse formation in a developing/regenerating brain. Our data also demonstrate that drugs such as Fluoxetine may not just affect communication between serotonergic neurons but that the detrimental effects are widespread and involve neurons of various phenotypes from both vertebrate and invertebrate species.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".