Standardised ginseng extract G115® potentiates the antidepressant-like properties of fluoxetine in the forced swim test
Bibliographic record
Abstract
OBJECTIVE: Ginsenosides, biologically active components of the root of Panax ginseng, have been reported to have therapeutic benefits in a number of disease states including psychiatric conditions such as major depressive disorder. Our objective was to determine if a standardised commercial ginseng extract, G115®, could reduce the signs of behavioural despair commonly observed in animal models of depression either alone or in combination with the selective serotonin reuptake inhibitor (SSRI) fluoxetine. METHODS: Male Sprague-Dawley (SD) rats (N = 51) were divided into four groups: vehicle control, G115® ginseng root extract, fluoxetine and fluoxetine plus G115®. Rats were trained to voluntarily consume treatments twice daily for 14 days and were then tested in an open field (OF), elevated plus maze (EPM) and forced swim test (FST). Post-mortem hippocampal and prefrontal cortex tissue was analysed for expression of brain-derived neurotrophic factor (BDNF) and tropomyosin receptor kinase B (TrkB) by western blot. RESULTS: One-way Analysis of Variance revealed no significant group differences in the OF or plus-maze performance on any variable examined. In the FST, fluoxetine significantly reduced immobility time and increased latency to immobility. The effects of fluoxetine were further significantly potentiated by co-administration of G115®. Post-mortem tissue analysis revealed significant group differences in BDNF expression in the left hippocampus and left prefrontal cortex without any accompanying changes in TrkB expression. CONCLUSIONS: We conclude that oral G115® significantly potentiates the antidepressant-like effect of fluoxetine in the FST in the absence of potentially confounding effects on locomotion and anxiety.
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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 | 0.000 |
| 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".