Surface functionalization and size modulate the formation of reactive oxygen species and genotoxic effects of cellulose nanofibrils
Bibliographic record
Abstract
BACKGROUND: Cellulose nanofibrils (CNFs) have emerged as a sustainable and environmentally friendly option for a broad range of applications. The fibrous nature and high biopersistence of CNFs call for a thorough toxicity assessment, but it is presently unclear which physico-chemical properties could play a role in determining the potential toxic response to CNF. Here, we assessed whether surface composition and size could modulate the genotoxicity of CNFs in human bronchial epithelial BEAS-2B cells. We examined three size fractions (fine, medium and coarse) of four CNFs with different surface chemistry: unmodified (U-CNF) and functionalized with 2,2,6,6-tetramethyl-piperidin-1-oxyl (TEMPO) (T-CNF), carboxymethyl (C-CNF) and epoxypropyltrimethylammonium chloride (EPTMAC) (E-CNF). In addition, the source fibre was also evaluated as a non-nanosized material. RESULTS: The presence of the surface charged groups in the functionalized CNF samples resulted in higher amounts of individual nanofibrils and less aggregation compared with the U-CNF. T-CNF was the most homogenous, in agreement with its high surface group density. However, the colloidal stability of all the CNF samples dropped when dispersed in cell culture medium, especially in the case of T-CNF. CNF was internalized by a minority of BEAS-2B cells. No remarkable cytotoxic effects were induced by any of the cellulosic materials. All cellulosic materials, except the medium fraction of U-CNF, induced a dose-dependent intracellular formation of reactive oxygen species (ROS). The fine fraction of E-CNF, which induced DNA damage (measured by the comet assay) and chromosome damage (measured by the micronucleus assay), and the coarse fraction of C-CNF, which produced chromosome damage, also showed the most effective induction of ROS in their respective size fractions. CONCLUSIONS: Surface chemistry and size modulate the in vitro intracellular ROS formation and the induction of genotoxic effects by fibrillated celluloses. One cationic (fine E-CNF) and one anionic (coarse C-CNF) CNF showed primary genotoxic effects, possibly partly through ROS generation. However, the conclusions cannot be generalized to all types of CNFs, as the synthesis process and the dispersion method used for testing affect their physico-chemical properties and, hence, their toxic effects.
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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".