Cellulose nanocrystal (CNC) cationic derivatives induce NLRP3 inflammasome-dependent IL-1? secretion associated with mitochondrial ROS production
Bibliographic record
Abstract
Erinolaoluwa Araoye1, Dejhy Pyram1, Usha D. Hemraz2, Rajesh Sunasee1, Karina Ckless1*\n1 Chemistry Department, State University of New York at Plattsburgh, Plattsburgh NY\n2 National Research Council of Canada, Montreal Canada\nCrystalline cellulose nanocrystal (CNC) has emerged as a novel material for a wide variety of important applications such as nanofillers, nanocomposites, surface coatings, regenerative medicine and drug and DNA delivery. CNC has a fiber-like structure with sizes in the range of 200-300 nm long and 5-50 nm wide. Despite the great potential applicability of CNC and its derivatives very little is known about their potential immunogenicity. Fiber-like materials have been known for evoking an immune response in particular for activating the NLRP3-inflammasome/IL-1? pathway. In this study we evaluated the capacity of CNC and its cationic derivatives CNC-g-poly(AEM)-1, CNC-g-poly(AEM)-2, CNC-g-poly(AEMA)-1 and CNC-g-poly(AEMA)-2 to stimulate NLRP3-inflammasome/IL-1? axis and enhance mitochondrial ROS. Mouse macrophages (J774.A1) were stimulated for 24h with 25, 50 and 100 µg/mL of CNC and its cationic derivatives. IL-1b secretion was analyzed by ELISA, mitochondrial function by JC-1 staining, cytochrome c release, ATP content and total and mitochondrial ROS was assessed by DCF and MitoSox staining, respectively. Mitochondrial ROS and extracellular ATP was significantly increased in cells treated with CNC-g-poly(AEMA)-2, which correlates with the strongest effects on IL-1? secretion. Our data also suggest that the increases in mitochondrial ROS and ATP release induced by this compound may be associated with their capability to evoke immune response.
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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".