Detecting Biogenic Amines in Food and Drug Plants with HPLC: Medical and Nutritional Implications
Bibliographic record
Abstract
Background: This work reports the results of the initial stage of the project aimed at detecting neuroactive substances in tropical plants that are widely used as food and/or drugs.Methods: The content of neuroactive biogenic amines, e.g, dopamine (DA), norepinephrine (NE), epinephrine (E), serotonin (5-HT), and others was determined using high-performance liquid chromatography (HPLC) with amperometric detection in leaf samples from Plumeria rubra L. cv. acutifolia, Syzigium jambos (L.) Alston, Buxus megistophylla (or Euonymus japonicas cv. aureoma), and Cinnamomum bodinieri Levl.Results: The total fraction of disintegrated leaves contained (sub)micromolar concentrations of DA, NE, and 5-HT. They lacked E and the catecholamine precursor 2,3-dihydrophenylalanine (DOPA).Conclusions: From the data obtained, it is evident that heretofore unexplored tropical plants used in drug preparations (P. rubra and S. jambos) and as desserts (S. jambos) and spices (C. bodinieri) contain physiologically active concentrations of neurochemicals. The neurochemicals are expected to produce a significant effect on the people who consume preparations and food additives made from the aforementioned plants. Moreover, such plant preparations can potentially be used as psychoactive drugs for the purpose of intentionally manipulating human behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".