Dual‐Functional Alginic Acid Hybrid Nanospheres for Cell Imaging and Drug Delivery
Bibliographic record
Abstract
An effective and facile approach to prepare gold-nanoparticle-encapsulated alginic acid-poly[2-(diethylamino)ethyl methacrylate] monodisperse hybrid nanospheres (ALG-PDEA-Au) is developed by using monodisperse ALG-PDEA nanospheres as a precursor nanoparticulate reaction system. This approach utilizes particle-interior chemistry, which avoids additional reductant or laborious separation process and, moreover, elegantly ensures that all the gold nanoparticles are located inside the hybrid nanospheres and every nanosphere is loaded with gold nanoparticles. These obtained ALG-PDEA-Au hybrid nanospheres have not only uniform size, similar surface properties, and good biocompatibility but also unique optical properties provided by the embedded gold nanoparticles. It is demonstrated that negatively charged ALG-PDEA-Au hybrid nanospheres can be internalized by human colorectal LoVo cancer cells and hence act as novel optical-contrast reagents in tumor-cell imaging by optical microscopy. Moreover, these hybrid nanospheres can also serve as biocompatible carriers for the loading and delivery of an anti-cancer drug doxorubicin. In vitro cell viability tests reveal that drug-loaded ALG-PDEA-Au hybrid nanospheres exhibit similar tumor cell inhibition to the free drug doxorubicin. Therefore, the obtained hybrid nanospheres successfully combine two functions, that is, cell imaging and drug delivery, into one single system, and may be of great application potential in other biomedical-related areas.
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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".