Polystyrene nanoparticles activate ion transport in human airway epithelial cells
Bibliographic record
Abstract
BACKGROUND: Over the last decade, nanotechnology has provided researchers with new nanometer materials, such as nanoparticles, which have the potential to provide new therapies for many lung diseases. In this study, we investigated the acute effects of polystyrene nanoparticles on epithelial ion channel function. METHODS: Human submucosal Calu-3 cells that express cystic fibrosis transmembrane conductance regulator (CFTR) and baby hamster kidney cells engineered to express the wild-type CFTR gene were used to investigate the actions of negatively charged 20 nm polystyrene nanoparticles on short-circuit current in Calu-3 cells by Ussing chamber and single CFTR Clchannels alone and in the presence of known CFTR channel activators by using baby hamster kidney cell patches. RESULTS: Polystyrene nanoparticles caused sustained, repeatable, and concentration-dependent increases in short-circuit current. In turn, these short-circuit current responses were found to be biphasic in nature, ie, an initial peak followed by a plateau. EC(50) values for peak and plateau short-circuit current responses were 1457 and 315.5 ng/mL, respectively. Short-circuit current was inhibited by diphenylamine-2-carboxylate, a CFTR Cl(-) channel blocker. Polystyrene nanoparticles activated basolateral K(+) channels and affected Cl(-) and HCO(3) (-) secretion. The mechanism of short-circuit current activation by polystyrene nanoparticles was found to be largely dependent on calcium-dependent and cyclic nucleotide-dependent phosphorylation of CFTR Cl(-) channels. Recordings from isolated inside-out patches using baby hamster kidney cells confirmed the direct activation of CFTR Cl(-) channels by the nanoparticles. CONCLUSION: This is the first study to identify the activation of ion channels in airway cells after exposure to polystyrene-based nanomaterials. Thus, polystyrene nanoparticles cannot be considered as a simple neutral vehicle for drug delivery for the treatment of lung diseases, due to the fact that they may have the ability to affect epithelial cell function and physiological processes on their own.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".