Hemp Proteins Conjugated with Green Tea Polyphenol Extract Form <i>De Novo</i> Plant‐Sourced Emulsifiers Suitable for Nanodelivery Systems Bearing Lipophilic Psychopharmaceuticals
Bibliographic record
Abstract
Abstract Nanoformulation is often used to improve the solubility and uptake of bioactives; however, it also protects sensitive bioactives from chemical decomposition. A class of biocompatible emulsifiers created by conjugating hemp protein with green tea polyphenols is reported. A simple pH‐assisted coupling protocol is employed to synthesize covalent and non‐covalent conjugates, which are then used to produce 5‐methoxy‐ N,N‐ dimethyltryptamine (5‐MeO‐DMT) enriched hemp oil nanoemulsions (NEs) in water with an average droplet size of ca. 200 nm and ζ potential values of ca. −40 mV. The de novo emulsifiers protect the sensitive drug under conditions of simulated oxidative stress, an indication that the antioxidant properties of polyphenols are retained within the emulsifier. These emulsions are 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. The sensitivity of 5‐MeO‐DMT mandates some form of formulation for reasonable bioavailability and reproducible dosages; our novel nanodelivery platform provides an elegant solution to this problem.
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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) | 0.000 | 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) | 0.001 | 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".