Molecular Pathways of the Therapeutic Effects of Ayahuasca, a Botanical Psychedelic and Potential Rapid-Acting Antidepressant
Bibliographic record
Abstract
Ayahuasca is a psychoactive brew traditionally used in indigenous and religious rituals and ceremonies in South America for its therapeutic, psychedelic, and entheogenic effects. It is usually prepared by lengthy boiling of the leaves of the bush Psychotria viridis and the mashed stalks of the vine Banisteriopsis caapi in water. The former contains the classical psychedelic N,N-dimethyltryptamine (DMT), which is thought to be the main psychoactive alkaloid present in the brew. The latter serves as a source for β-carbolines, known for their monoamine oxidase-inhibiting (MAOI) properties. Recent preliminary research has provided encouraging results investigating ayahuasca’s therapeutic potential, especially regarding its antidepressant effects. On a molecular level, pre-clinical and clinical evidence points to a complex pharmacological profile conveyed by the brew, including modulation of serotoninergic, glutamatergic, dopaminergic, and endocannabinoid systems. Its substances also interact with the vesicular monoamine transporter (VMAT), trace amine-associated receptor 1 (TAAR1), and sigma-1 receptors. Furthermore, ayahuasca’s components also seem to modulate levels of inflammatory and neurotrophic factors beneficially. On a biological level, this translates into neuroprotective and neuroplastic effects. Here we review the current knowledge regarding these molecular interactions and how they relate to the possible antidepressant effects ayahuasca seems to produce.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".