The Effects of Extracts from St. John's Wort and Kava Kava on Brain Neurotransmitter Levels in the Mouse
Bibliographic record
Abstract
Introduction Extracts of Hypericum perforatum (St. John's Wort, SJW) and Kava Kava are well established in the treatment of psychiatric disorders. Several controlled clinical studies have confirmed that SJW extract represents an effective antidepressant principle superior to placebo [ 13 ] [ 18 ] [ 19 ]. SJW extract contains at least ten constituents or groups of components that may contribute to its pharmacological effects [ 4 ]. SJW extracts have been shown to inhibit synaptosomal uptake of serotonin, dopamine and noradrenaline, implying a biochemical mechanism similar to classical antidepressants [ 14 ] [ 15 ]. The reuptake inhibiting properties of SJW extract have mostly been attributed to the phloroglucinol derivative, hyperforin [ 15 ]; see also Wonnemann et al., this issue. Kava Kava extract is made from the root of Piper methysticum, which is found in the south Pacific region. Clinically, Kava Kava is used for anxiety and insomnia in Europe and the United States. One meta-analysis of various trials has implied that Kava Kava extract is superior to placebo as a symptomatic treatment for anxiety [ 16 ]. A number of compounds, referred to as Kava pyrones, are thought to be responsible for the plant's effects. Although Kava pyrones exert weak effects on benzodiazepine-binding sites in vitro , the biochemical mechanism of action is not yet known [ 7 ]. In the present study, we will address the question of whether acute oral treatment of mice with SJW or Kava Kava extracts alters neurotransmitter levels in the brain. Brain levels of different neurotransmitters and their metabolites were determined in brain homogenates using high-performance liquid chromatography with electrochemical detection.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".