Engineering large gelatin nanospheres coated with quantum dots for targeted delivery of human osteosarcoma with enhanced cellular internalization
Bibliographic record
Abstract
Due to the special structures of cell membrane, good internalization is one of major concerns on using large nanospheres as carriers for labeling and treatment of cancer. We herein report fluorescent gelatin nanosphers (GNs) coated with CdSe/ZnS quantum dots (QDs) to form a viable vehicle for theranostic applications. Anti-human immunoglobulin G Fab formation, anti-IgG Fab, is bioconjugated on to the hybrid fluorescent GNs for targeted delivery (QDs-GNs-anti-IgG Fab). Human osteosarcoma cell line is used in studying the interaction between hybrid fluorescent GNs with and without anti-IgG Fab. The average particle size of the fluorescent GNs bioconjugated with antibodies is estimated at 480±50 nm. The emission (λem) of the fluorescent GNs is around 652 nm. The quantitative analysis on the surface modification and bioconjugation of GNs has been discussed in this paper. The three-dimensional z-stacking fluorescent images reveal that the hybrid GNs with anti-IgG Fab has ~1.5 times increase in internalization with human osteosarcoma cells than GNs without the antibody fragment. The improved cellular interaction of the QDs-GN-anti IgG Fab is attributed to the bioconjugation of antibodies which can provide specificity for targeted drug delivery. Relative cell viability (%) is larger than 80% for each type of functionalized GNs up to 40 μg/mL. We expect that the functionalized gelatin sphere can offer an effective theranostic tool for targeted drug delivery and direct imaging confirmation simultaneously.
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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".