Evaluation of the Antidepressant Activity of Beta vulgaris Alone and in Combination with Fluoxetine in Mice
Bibliographic record
Abstract
Background and Objective: Beta vulgaris (BV) possesses strong antioxidant and anti-inflammatory properties that may play a vital role in mitigating mental disorders like depression.The present study was designed to evaluate the antidepressant effects of aqueous and methanolic extracts of BV using standardised mouse models of depression.Methodology: The extracts were analysed for phytochemical ingredients and in vitro experiments were done to determine antioxidant properties of BV extracts.After preliminary dose range finding studies for any adverse effects, the antidepressant activities of aqueous and methanolic extracts of BV were evaluated in mouse models of depression.Animals were randomly divided into 8 groups (6 animals per group): Group 1 and 2 served as vehicle control and fluoxetine (20 mg kgG 1 ) standard control, respectively.Groups 3 and 4 were given aqueous extract of BV orally at doses of 100 and 200 mg kgG 1 dayG 1 , respectively.Groups 5 and 6, received methanolic extract of BV at doses of 200 and 400 mg kgG 1 dayG 1 , respectively.Groups 7 and 8 received 200 and 400 mg kgG 1 dayG 1 methanolic extract of BV, respectively, +10 mg kgG 1 dayG 1 dose of fluoxetine.Following 14 days daily dosing, all animals were tested using behavioural tests of depression on day 15th using Forced Swim Test (FST), Tail Suspension Test (TST) and Locomotor Activity Test (LAT).Results: In comparison with the control groups 1 and 2, marked changes were observed in all parameters in extract-dosed mice.Especially, significant antidepressant effects were found in mice given simultaneously combined doses of 200 or 400 mg kgG 1 dayG 1 of BV methanolic extract+10 mg kgG 1 dayG 1 fluoxetine, suggesting an additive serotonergic effect.Conclusion: Overall, the findings suggest that Beta vulgaris has a potential for developing an alternative plant-derived antidepressant therapy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".