Functionalization of Cellulose Nanocrystals with PEG-Metal-Chelating Block Copolymers via Controlled Conjugation in Aqueous Media
Bibliographic record
Abstract
Abstract Elongated nanoparticles have recently been shown to have distinct advantages over their spherical counterparts in drug delivery applications. Cellulose nanocrystals (CNCs) have rodlike shapes in nature and have demonstrated biocompatibility in a variety of mammalian cell lines. In this report, CNCs are put forward as a modular platform for the production of multifunctional rod-shaped nanoparticles for cancer imaging and therapy. For the first time, PEGylated metal-chelating polymers containing diethylenetriaminepentaacetic acid (DTPA) (i.e., mPEG-PGlu(DPTA)18-HyNic and PEG-PGlu(DPTA)25-HyNic) are conjugated to CNCs to enable the chelation of radionuclides for diagnostic and therapeutic applications. The entire conjugation is based on UV/vis-quantifiable bis-aryl hydrazone-bond formation, which allows direct quantification of the polymers grafted onto the CNCs. Moreover, it has been shown that the mean number of polymers grafted per CNC could be controlled. The CNCs are also fluorescently labeled with rhodamine and Alexa Fluor 488 by embedding the probes in the polymer corona. Preliminary evaluation in a human ovarian cancer cell line (HEYA8) demonstrated that these CNCs are nontoxic and their penetration properties can be readily assessed in multicellular tumor spheroids (MCTSs) by optical imaging. These findings provide support for biomedical applications of CNCs, and further in vitro and in vivo studies are warranted to evaluate their potential as imaging and therapeutic agents for cancer treatment.
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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".