Silk Fibroin Coated Magnesium Oxide Nanospheres: A Biocompatible and Biodegradable Tool for Noninvasive Bioimaging Applications
Bibliographic record
Abstract
Fluorescent nanoparticles (NPs) have been increasingly studied as contrast agents for better understanding of biological processes at the cellular and molecular level. However, their use as bioimaging tools is strongly dependent on their optical emission as well as their biocompatibility. This work reports the fabrication and characterization of silk fibroin (SF) coated magnesium oxide (MgO) nanospheres, containing oxygen, Cr3+ and V2+ related optical defects, as a nontoxic and biodegradable hybrid platform for bioimaging applications. The MgO-SF spheres demonstrated enhanced emission efficiency compared to noncoated MgO NPs. Furthermore, SF sphere coating was found to overcome agglomeration limitations of the MgO NPs. The hybrid nanospheres were investigated as an in vitro bioimaging tool by recording their cellular uptake, trajectories, and mobility in human skin keratinocytes cells (HaCaT), human glioma cells (U87MG) and breast cancer cells (MCF7). Enhanced cellular uptake and improved intracellular mobilities of MgO-SF spheres compared to MgO NPs was demonstrated in three different cell lines. Validated infrared and bright emission of MgO-SF NP indicate their prospects for in vivo imaging. The results identify the potential of the hybrid MgO-SF nanospheres for bioimaging. This study may also open new avenues to optimize drug delivery through biodegradable silk and provide noninvasive functional imaging feedback on the therapeutic processes through fluorescent MgO.
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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".