Mechanistic insights into the antidepressant potential of plant-derived flavonoids: A preclinical review
Bibliographic record
Abstract
Flavonoid-based phytomedicines are emerging as promising therapies for combating various disorders, including depression. Depression is a common and serious medical illness that negatively affects the quality of life. It has become a leading cause of disability worldwide. Flavonoids are ubiquitous biologically active phytochemicals in medicinal plants, herbs, fruits, vegetables, teas, and wines. There is a negative association between total flavonoid intake and depression symptoms in humans. This review aims to discuss the recent in vivo and in vitro studies on the effects of dietary flavonoids in depression models and assays to identify the molecular pathways that underlie their actions. Here, we briefly introduce the pathophysiology of depression, the diagnosis of depression, and the models for studying depression. The discovered potential antidepressant flavonoids include flavonols (quercetin, quercitrin, kaemferol, and heptamethoxyflavone), flavones (luteolin, baicalin, apigenin, and cymaroside), isoflavones (ononin), flavanones (pinocembrin), and anthocyanins (callistephin). These plant-derived flavonoids have been shown to reduce neuronal damage in the hippocampus, decrease neurotransmitter depletion, attenuate hypothalamic-pituitary-adrenal axis hyperactivation, inhibit inflammation in the central nervous system, and regulate gut microbiota. The key signaling pathways regulated by flavonoids include brain-derived neurotrophic factor (BDNF)/tropomyosin-related kinase B (TrkB), and nuclear factor kappa-B (NF-κB). Clearly, there is a need to conduct human dietary intervention studies to validate the beneficial physiological functions of flavonoids on the prevention and management of depression.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".