Post-Sterilization Physicochemical Characterization and Biological Activity of Cellulose Nanocrystals Coated with PDDA
Bibliographic record
Abstract
The rapid expansion of medical nanotechnology has significantly broadened the potential applications of cellulose nanocrystals (CNCs). While CNCs were initially developed for drug delivery, they are now being investigated for a range of advanced biomedical applications. As these applications evolve, it becomes crucial to understand the physicochemical behavior of CNCs in biologically relevant media to optimize their design and ensure biocompatibility. Functionalized CNCs can adsorb biomolecules, forming a "protein corona" that can impact their physicochemical properties, including alterations in particle size, zeta potential, and overall functionality. In this study, CNCs were coated with low (8500 Da)- and high (400,000-500,000 Da)-molecular-weight cationic polymer (poly(diallyldimethylammonium chloride-(PDDA) via non-covalent grafting, and their physicochemical characteristics, as well as their biological effects, were assessed in physiologically relevant media after sterilization. Our findings show that autoclaving significantly alters the physicochemical properties of CNC-PDDA, particularly when coated with low-molecular-weight (LMW) polymer. Furthermore, we observed that CNC-PDDA of a high molecular weight (HMW) has a greater impact on cell viability and blood biocompatibility than its LMW counterpart. Moreover, cellular immune responses to both CNC-PDDA LMW and HMW vary in the presence or absence of serum, implying that protein adsorption influences cell-nanomaterial recognition and their biological activity. This study provides valuable insights for optimizing CNC-based nanomaterials for therapeutic applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".