St. John's Wort (Hypericum perforatum): A Review of its Potential Antidepressant Efficacy and Safety
Bibliographic record
Abstract
Background: St. John's Wort (SJW), scientifically known as Hypericum perforatum, is widely utilized as an over-the-counter remedy for various ailments, particularly depression, despite lacking FDA approval. The active compounds in SJW, including hypericin, hyperforin, and flavonoids, are believed to alleviate depressive symptoms by inhibiting neurotransmitter reuptake. However, concerns regarding its safety arise from extensive drug-drug interactions, pointing to deficiencies in quality control and regulation of SJW distribution. Methods: This narrative review synthesizes existing literature on the therapeutic effects, safety profile, indications, and adverse effects of SJW in treating depression. It also examines the regulatory landscape surrounding SJW and highlights the necessity for standardized practices and comprehensive clinical trials. Results: SJW has shown efficacy in managing mild to moderate depression, with some studies suggesting it may offer benefits comparable to conventional antidepressants while exhibiting a more favorable side effect profile. However, significant safety concerns persist due to potential interactions with other medications, leading to adverse reactions such as serotonin syndrome. Current regulatory frameworks vary globally, with SJW classified as a dietary supplement in the U.S. and subjected to stricter regulations in Europe. Conclusion: Despite its widespread use and potential therapeutic benefits, the safety and efficacy of SJW in treating depression remain inadequately substantiated by clinical evidence. Enhanced regulatory measures and rigorous clinical research are imperative to clarify SJW's role in depression treatment and ensure its safe application in clinical practice. Inclusion in pharmacopeias and adherence to good manufacturing practices could further improve the quality and safety of SJW products.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".