Development of modular polymeric nanoparticles for drug delivery using amine reactive chemistry
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
Curcumin has been explored for its anti-cancer potential, but is severely limited by its hydrophobicity and sensitivity to light and water. In this study, poly (lactic-co-glycolic) acid (PLGA) nanoparticles (NPs) were synthesized to encapsulate curcumin via single emulsion method to improve curcumin stability and bioavailability. The PLGA NPs were coated with oligomeric chitosan (COS) and RGD peptide (a peptide consisting of Arg-Gly-Asp) using amine-reactive chemistry (NHS and EDC). Both COS and RGD had been previously shown to accumulate and target many different types of cancer cells. NPs were characterised based on size distribution, zeta potential, and binding efficiency of RGD peptide. They were also evaluated on encapsulation efficiency, and stability, of curcumin within the NPs. OVCAR-3 cancer cells were treated with COS and RGD-coated PLGA NPs loaded with Coumarin-6 dye for fluorescent imaging of cell uptake. They were also treated with curcumin-loaded NPs to determine cytotoxicity and effectiveness of delivery. The NPs exhibited size distribution and zeta potential within expected values, though binding efficiency of RGD was low. Curcumin-loaded NPs showed significant increase in cytotoxicity over free (unencapsulated) curcumin, and void (empty) NPs, suggesting successful delivery of curcumin as an anti-cancer agent; the performance of COS and RGD coated NPs over bare PLGA NPs was inconclusive, however, optimization will be required to improve formulation during the coating steps. This method of NP synthesis serves as proof of concept for a modular solution to the development of various coated polymeric NPs for other drugs or applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it