Controllable Engineering and Functionalizing Nanoparticles with Specific Targeting Capability
Bibliographic record
Abstract
<title>Abstract</title> Due to unique properties, nanoparticles been widely used in important biomedical applications such as imaging, drug delivery and disease therapy. Targeting toward specific proteins is essential for the effective utilization of nanoparticles. However, current targeting strategies mainly rely on surface modification with bio-ligands, which often not only fail to provide desired properties but also remain challenging. Here we report an unprecedented approach, called reverse microemulsion-confined epitope-oriented surface imprinting and cladding (ROSIC), for facile, versatile and controllable engineering coreless and core/shell nanoparticles with tunable monodispersed size as well as specific targeting capability towards proteins and peptides. Via engineering coreless imprinted and cladded silica nanoparticles, the effectiveness and superiority over conventional imprinting of the proposed approach was first verified. The prepared nanoparticles exhibited both high specificity and high affinity. Using quantum dots (QDs), superparamagnetic nanoparticles, silver nanoparticles and upconverting nanoparticles as a representative set of core substrates, a variety of dual-functional single-core/shell nanoparticles were then successfully prepared. Finally, selective fluorescence imaging of triple negative breast cancer cells over other breast cancer cell lines using QD-cored nanoparticles was achieved, which well demonstrated the potential of the prepared core/shell nanoparticles in biomedical applications. Thus, this approach opened a new avenue to engineering and functionalization of advanced nanoparticles with targeting capability, holding great prospects in biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".