Activation of human AML14.3D10 eosinophils by nanoparticles: Modulatory activity on apoptosis and cytokine production
Bibliographic record
Abstract
Eosinophilic inflammation is frequently observed in response to nanoparticle (NP) exposure in airway rodent models of allergies where the number of eosinophils is increased in lungs. Despite this, it is surprising that the potential cytotoxic effect of NP, as well as their direct role on eosinophils is poorly documented. The present study investigated how different NP can alter the biology of the human eosinophilic cell line AML14.3D10. It was found that among NP forms of CeO2, ZnO, TiO2, and nanosilver of 20 nm (AgNP20) or 70 nm (AgNP70) diameters, only ZnO and AgNP20 induced apoptosis. Caspases-7 and -9 were not activated by the tested NP while caspase-3 was activated by AgNP20 only. However, both ZnO and AgNP20 induced cytoskeletal breakdown as evidenced by the cleavage of lamin B1. Using an ELISArray approach for the simultaneous detection of several analytes (cytokines/chemokines), it was found that only ZnO and AgNP20 increased the production of different analytes including the potent pro-inflammatory CXCL8 (IL-8) chemokine. From the data here, we conclude that toxic effects of some NP could be observed in human eosinophil-like cells and that this could be related, at least partially, by induction of apoptosis and production of cytokines and chemokines involved in inflammation. The results of this study also indicate that distinct NP do not activate similarly human eosinophils, since ZnO and AgNP20 induce apoptosis and cytokine production while others such as TiO2, CeO2, and AgNP70 do not.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".