Silk fibroin nanoparticles for enhanced bio-macromolecule delivery to the retina
Bibliographic record
Abstract
The aim of this study was to investigate intravitreal injection of silk fibroin nanoparticles (SFNs) encapsulating bio-macromolecules, achieving enhanced drug bioavailability, and extended retention in retina. SFNs were prepared with regenerated silk fibroin using desolvation method with fluorescein isothiocyanate labeled bovine serum albumin (FITC-BSA) as bio-macromolecular model drug encapsulated. In vitro physicochemical properties and in vitro drug release of FITC-BSA loaded SFNs (FITC-BSA-SFNs) were evaluated. Cytotoxicity, cellular uptake, and retention of FITC-BSA-SFNs were determined in human retinal pigment epithelial cell line (ARPE-19). In addition, in vivo distribution and safety of intravitreally administered FITC-BSA-SFNs were investigated in New Zealand white rabbits. The particle size of FITC-BSA-SFNs was 179.1 ± 3.7 nm with polydispersity index of 0.102 ± 0.033 and the zeta potential was greater than -25 mV. FITC-BSA-SFNs exhibited excellent biocompatibility with no cytotoxicity observed within 24 and 48 h in AREP-19 cells. Compared to FITC-BSA solution, FITC-BSA-SFNs showed enhanced cellular uptake and prolonged retention. Furthermore, FITC-BSA-SFNs achieved accumulated distribution and extended retention in retina in vivo following intravitreal injection compared to a single administration of free drug solution. Therefore, this bio-macromolecule delivery platform based on SFNs could have great potential in the treatment of posterior segment disorders.
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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) | 0.000 | 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 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".