Click Chemistry Functionalized Polymeric Nanoparticles Target Corneal Epithelial Cells through RGD-Cell Surface Receptors
Bibliographic record
Abstract
Self-assembled polymeric nanoparticles modified with targeting ligands on the surface provide a means for localized cell delivery. To gain greater insight into the possibility of derivatizing poly(2-methyl-2-carboxytrimethylene carbonate-co-D,L-lactide) (poly(TMCC-co-LA)) nanoparticles using the Huisgen's 1,3 dipolar cycloaddition reaction, we synthesized amphiphilic copolymers comprising a hydrophobic poly(TMCC-co-LA) backbone and a hydrophilic poly(ethylene glycol) (PEG) pendant chain. By coupling amine-terminated PEG-azide to the carboxylic acid group of the poly(TMCC-co-LA) via EDC chemistry, an amphiphilic copolymer was formed. The poly(TMCC-co-LA)-g-PEG-N3 self-assembled in aqueous solution and presented azide groups on the surface of the nanoparticles. Alkyne-modified KGRGDS peptides were synthesized and coupled to the azide-functionalized nanoparticles via Huisgen's 1,3 dipolar cycloaddition, which was catalyzed by copper sulfate and sodium ascorbate in aqueous solution. Using coumarin-modified lysine (K) of the KGRGDS peptide, fluorescence was used to determine that there were approximately 400 peptides bound to each nanoparticle. The bioactivity of the GRGDS nanoparticle was confirmed with a competitive cell attachment assay using rabbit corneal epithelial cells. This GRGDS-nanoparticle system may be suitable for targeted drug delivery.
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.
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".