Polymer‐Coated Metal‐Oxide Nanoparticles Inhibit IgE Receptor Binding, Cellular Signaling, and Degranulation in a Mast Cell‐like Cell Line
Bibliographic record
Abstract
Previous reports have shown that nanoparticles (NPs) can both enhance and suppress immune effector functions; however the mechanisms that dictate these responses are still unclear. Here, the effects of polyacrylic acid (PAA) functionalized metal‐oxide NP are investigated on RBL‐2H3 (representative mammalian granulocyte‐like cell line) cell viability, cellular degranulation, immunoglobulin E (IgE) receptor binding, and cell signaling pathways related to immune function. The increasing development of PAA‐NPs as pesticide dispersants and as drug carriers in therapeutics necessitates their investigation for safe production. Using two in vitro experimental approaches, this study demonstrates that pre‐exposing RBL‐2H3 cells, or IgE antibodies, to PAA‐NPs (TiO 2 , CeO 2 , ZnO, Fe 2 O 3 , and PAA‐Capsules (NP coating control) over 24 h, significantly decrease the binding capacity of IgE for Fcε receptors, inhibit the phosphorylation of intracellular signaling proteins (e.g., MAPK ERK) that mediate degranulation, and inhibited RBL‐2H3 cell degranulation. In addition, and unlike the other NPs tested, PAA‐TiO 2 significantly reduced RBL‐2H3 viability, in a time (4–24 h) and dose‐dependent manner (>50 μg mL −1 ). Together, these data demonstrate that PAA‐NPs at sub‐lethal doses can interact with cell surface structures, such as receptors, to suppress various stages of the RBL‐2H3 degranulatory response to external stimuli, and modify immune cell functions that can impact host‐immunity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".