Inhibition of anion fluxes in Calu‐3 cells by polystyrene nanoparticles: role of lipid rafts
Bibliographic record
Abstract
Introduction There is growing concern about the potential toxicity of non‐engineered and engineered nanoparticles. Lungs are particularly susceptible to the injury brought about by inhalation of gases and aerosols containing nanoparticles. Aims: We investigated the effects of apical exposure (up to 60 min) of cells to polystyrene nanoparticles (PNPs) (positive, negative, uncharged; 20, 50, 100nm) on transepithelial anion fluxes. Methods Calu‐3 cells grown on inserts were mounted into modified Ussing chambers. Anion fluxes (measured as short‐circuit current, I sc ) were stimulated with forskolin (10µM) in the presence or absence of PNPs (88‐104 µg/ml). In some experiments lipid rafts were disrupted by removing cell surface cholesterol with cyclodextrin (CD). Results Forskolin resulted in mean peak increase in I sc from 29.4 to 108.4µA/cm 2 . All charged PNPs caused a significant (p<0.05, n.≥3) reduction of forskolin‐stimulated anion fluxes (from 24 to 40‰), an effect undetectable with uncharged particles. The CD treatment led to a significant potentiation of the inhibitory effects of 20 and 50nm PNPs. In contrast, the treatment abolished the inhibitory effects of 100nm PNPs. Conclusion Our results suggest that charge, size and interactions with lipid rafts are essential factors in determining the extent of PNPs‐mediated inhibition of forskolin‐stimulated anion fluxes in Calu‐3 cells.
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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".