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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.011 | 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 teacher head, 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".