Effects of traditionally used anxiolytic botanicals on enzymes of the γ-aminobutyric acid (GABA) systemThis article is one of a selection of papers published in this special issue (part 1 of 2) on the Safety and Efficacy of Natural Health Products.
Bibliographic record
Abstract
In Canada, the use of botanical natural health products (NHPs) for anxiety disorders is on the rise, and a critical evaluation of their safety and efficacy is required. The purpose of this study was to determine whether commercially available botanicals directly affect the primary brain enzymes responsible for gamma-aminobutyric acid (GABA) metabolism. Anxiolytic plants may interact with either glutamic acid decarboxylase (GAD) or GABA transaminase (GABA-T) and ultimately influence brain GABA levels and neurotransmission. Two in vitro rat brain homogenate assays were developed to determine the inhibitory concentrations (IC50) of aqueous and ethanolic plant extracts. Approximately 70% of all extracts that were tested showed little or no inhibitory effect (IC50 values greater than 1 mg/mL) and are therefore unlikely to affect GABA metabolism as tested. The aqueous extract of Melissa officinalis (lemon balm) exhibited the greatest inhibition of GABA-T activity (IC50 = 0.35 mg/mL). Extracts from Centella asiatica (gotu kola) and Valeriana officinalis (valerian) stimulated GAD activity by over 40% at a dose of 1 mg/mL. On the other hand, both Matricaria recutita (German chamomile) and Humulus lupulus (hops) showed significant inhibition of GAD activity (0.11-0.65 mg/mL). Several of these species may therefore warrant further pharmacological investigation. The relation between enzyme activity and possible in vivo mode of action is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".