Cellulose nanocrystals with tunable surface charge for nanomedicine
Bibliographic record
Abstract
Crystalline nanoparticles of cellulose exhibit attractive properties as nanoscale carriers for bioactive molecules in nanobiotechnology and nanomedicine. For applications in imaging and drug delivery, surface charge is one of the most important factors affecting the performance of nanocarriers. However, current methods of preparation offer little flexibility for controlling the surface charge of cellulose nanocrystals, leading to compromised colloidal stability under physiological conditions. We report a synthesis method that results in nanocrystals with remarkably high carboxyl content (6.6 mmol g(-1)) and offers continuous control over surface charge without any adjustment to the reaction conditions. Six fractions of nanocrystals with various surface carboxyl contents were synthesized from a single sample of softwood pulp with carboxyl contents varying from 6.6 to 1.7 mmol g(-1) and were fully characterized. The proposed method resulted in highly stable colloidal nanocrystals that did not aggregate when exposed to high salt concentrations or serum-containing media. Interactions of these fractions with four different tissue cell lines were investigated over a wide range of concentrations (50-300 μg mL(-1)). Darkfield hyperspectral imaging and confocal microscopy confirmed the uptake of nanocrystals by selected cell lines without any evidence of membrane damage or change in cell density; however a charge-dependent decrease in mitochondrial activity was observed for charge contents higher than 3.9 mmol g(-1). A high surface carboxyl content allowed for facile conjugation of fluorophores to the nanocrystals without compromising colloidal stability. The cellular uptake of fluoresceinamine-conjugated nanocrystals exhibited a time-dose dependent relationship and increased significantly with doubling of the surface charge.
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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 | 0.001 | 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".