Cellular interaction influenced by surface modification strategies of gelatin-based nanoparticles
Bibliographic record
Abstract
Theranostic applications of gelatin nanospheres require two major components, a method of detection and good biocompatibility. We characterized the response of UTA-6 human osteosarcoma cells to the introduction of functionalized 90 bloom-based gelatin nanospheres (158 ± 49 nm) modified with three elements in different order: (a) hybridization with cadmium-based quantum dots for optical detection, (b) bioconjugation with anti-human IgG FAB (anti-IgG) for cell targeting, with/without (c) capping with polyethylene glycol on the surface for enhanced biocompatibility. A one-pot process is developed for incorporating quantum dots and antibody with gelatin nanospheres. Path A of modifying gelatin nanospheres with quantum dots first followed by anti-IgG resulted in a significantly greater cellular viability than Path B with anti-IgG first followed by quantum dots. Capping with polyethylene glycol as the final step in modification yielded significantly opposing results with decreases in Path A and increases in Path B. Three-dimensional z-stacking fluorescent images of hybrid gelatin nanospheres with anti-IgG is observed to have an increase in cellular association. The observed results suggest the modification order for building hybrid nanospheres may have an impact on cellular response.
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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".