Green synthesis of cannabinoids loaded gold nanoparticles displaying enhanced anti-cancer properties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the anti-cancer potential of cannabinoids loaded onto gold nanoparticles (AuNPs) for targeted cancer treatment. We used a multifaceted approach to explore and design this nano system, which involved synthesis, characterization, product stability and demonstration of anti-cancer potential in 2D and 3D in vitro models. In this research, we have demonstrated that two highly hydrophobic phytocannabinoids like delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), can be loaded on the surface of AuNPs with a one-pot synthesis protocol using trisodium citrate and l -tyrosine as a reducing and stabilizing agents. l -tyrosine plays a crucial role in cannabinoid loading, stability, and shelf life of the AuNPs. After synthesis, the cannabinoid-loaded nanoparticles were characterized with UV-vis spectroscopy, dynamic light scattering (DLS), dark field hyperspectral microscopy, and electron microscopy. The AuNPs function as a scaffold for the attachment and enhanced transport of both cannabinoids inside cancer cells, thus increasing their bioavailability. Hyperspectral microscopy was used to confirm AuNPs uptake. IC50 values in SK-BR-3 human breast cancer cell line for both THC and CBD loaded onto AuNPs were lower by 70.75% and 37.04% than those of the aqueous suspension of pure molecules. Compared to the aqueous suspension of pure cannabinoids, this approach induced and enhanced cancer cell death more efficiently. This enhanced efficacy was associated with a decline in cell viability, which is attributed to apoptosis, as indicated by flow cytometry results. Our findings offer a significant step towards the green design and utilization of AuNPs to deliver cannabinoids into cells efficiently.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 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 it