Metallic oxide nanoparticle translocation across the human bronchial epithelial barrier
Bibliographic record
Abstract
Inhalation is the most frequent route of unintentional exposure to nanoparticles (NPs). Our aim was to quantify the translocation of different metallic NPs across human bronchial epithelial cells and to determine the factors influencing this translocation. Calu-3 cells forming a tight epithelial barrier when grown on a porous membrane in a two compartment chamber were exposed to fluorescently labelled NPs to quantify the NP translocation. NP translocation and uptake by cells were also studied by confocal and transmission electron microscopy. Translocation was characterized according to NP size (16, 50, or 100 nm), surface charge (negative or positive SiO2), composition (SiO2 or TiO2), presence of proteins or phospholipids and in an inflammatory context. Our results showed that NPs can translocate through the Calu-3 monolayer whatever their composition (SiO2 or TiO2), but this translocation was increased for the smallest and negatively charged NPs. Translocation was not associated with an alteration of the integrity of the epithelial monolayer, suggesting a transcytosis of the internalized NPs. By modifying the NP corona, the ability of NPs to cross the epithelial barrier differed depending on their intrinsic properties, making positively charged NPs more prone to translocate. NP translocation can be amplified by using agents known to open tight junctions and to allow paracellular passage. NP translocation was also modulated when mimicking an inflammatory context frequently found in the lungs, altering the epithelial integrity and inducing transient tight junction opening. This in vitro evaluation of NP translocation could be extended to other inhaled NPs to predict their biodistribution.
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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".