Evaluation of Synaptosomal Uptake Inhibition of most Relevant Constituents of St. John's Wort
Bibliographic record
Abstract
In our previous investigations, we could demonstrate that extract preparations of Hypericum perforatum (St. John's wort, SJW) inhibit the uptake of several neurotransmitters (serotonin, norepinephrine, dopamine, GABA, L-glutamate) in synaptosomal preparations of rodent brain. Hyperforin, the lipophilic constituent, was identified as the main component responsible for these effects. The properties seen for hyperforin in these and other pharmacological models present a plausible and logical explanation for the well documented antidepressive effects of SJW extract preparations in clinical studies. However, evidence for other active principles in SJW extract have been reported (See also communications by Misane & Ogren and Philippu in this issue). Accordingly, we tested various SJW extract preparations and all relevant constituents as possible inhibitors of synaptosomal uptake of neurotransmitters. Two further components were found to be active in those models. Adhyperforin, like hyperforin, showed a strong inhibiting profile in all uptake systems investigated. Moreover, we could observe a weak to moderate inhibiting profile for the oligomeric procyanidins fraction (OPC). Further investigations would have to clarify any possible contribution of these two constituents to the antidepressive effects of SJW extract seen in animal experiments and clinical trials.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".