Herbal Anxiolytics: Sources and Their Preparation Methods
Bibliographic record
Abstract
Anxiety is a disorder with known etiology and clinical symptoms which is managed by combination therapy or the use of complementary and alternative medicine (CAM), such as psychopharmacotherapy, cognitive behavioral therapy, and herbal medicine. The approach of scientists is to identify natural anxiolytics, based on their active components and their mechanism of action. So far, several medicinal plants have been identified and their effective components have been isolated and characterized as having cellular and molecular targets to the central nervous system (CNS). Despite the progress made in identification, application and drug interaction issues of such products, further studies should be planned to minimize their side effects and enhance their efficiency and specificity for a given health condition. The use of natural anxiolytics, either alone or in combination with other remedies can be improved by managing the preparation protocols, the route and the form of administration. In this context, natural drinks such as coffee with high levels of caffeine may exacerbate the clinical symptoms of anxiety. On the other hands, theanine (present in tea leaves) can alleviate the symptoms of the disorder. The current information available on traditional medicine and pharmacognosy is promising for formulation of nutraceuticals more specifically, with highest efficiency for prevention and treatment of anxiety. This review article attempts to introduce major herbs/plants recognized for their anti-anxiety effects and explain the feasibility for their specific application. The methods for the extract preparation and optimum condition for using such materials as traditional medicine or for their use in new formulations as nutraceuticals is suggested. The review also includes information about anxiety disorders, etiology, symptoms, types, neurobiology and different approaches to ameliorate anxiety conditions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".