Hemp proteins conjugated with green tea polyphenol extract form de novo plant-sourced emulsifiers suitable for nanodelivery systems bearing lipophilic psychopharmaceuticals
Bibliographic record
Abstract
Nanoformulation is often used to improve the solubility and uptake of bioactives; however, it also protects sensitive bioactives from chemical decomposition. We report a class of biocompatible emulsifiers created by conjugating hemp protein with green tea polyphenols. A simple pH-assisted coupling protocol was employed to synthesize covalent and non-covalent conjugates, which were then used to produce 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) enriched hemp oil nanoemulsions in water with an average droplet sizes of ca. 200 nm and ζ potential values of ca. -40 mV. Our de novo emulsifiers protected the sensitive drug under conditions of simulated oxidative stress, an indication that the antioxidant properties of polyphenols are retained. These emulsions were resistant to a wide variety of emulsion-breaking stressors and demonstrated remarkable colloidal stability over a period of 4 weeks with no evidence of phase separation. Fluorescence and confocal imaging confirmed cellular uptake of the formulation, while in vitro cytotoxicity assays showed acceptable cell viability with drug-loaded nanoemulsions. This represents an ingestible 5-MeO-DMT formulation; the sensitivity of this molecule mandates some form of formulation for reasonable bioavailability and reproducible dosages.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".