The curious case of how mimicking physiological complexity in in vitro models of the human respiratory system influences the inflammatory responses. A preliminary study focused on gold nanoparticles
Bibliographic record
Abstract
Abstract Environmental and biomedical nanoparticles can pose potential health risks to the human respiratory system by inducing severe lung inflammation. The aim of this case study is to present a comparison of the inflammatory response in four in vitro models of the human lung epithelium, differing by composition and/or culturing substrates, when exposed to gold nanoparticles (AuNPs). Three in vitro models of lung adenocarcinoma (A549) cells and a commercially available three‐dimensional (3D) culture (MucilAir™) were tested. The models were exposed to AuNPs for 3, 6, and 24 h. AuNPs internalisation was investigated by confocal, electron microscopy, and Raman spectroscopy. Enzyme‐Linked Immuno‐Sorbent Assay (ELISA) was used for quantifying the secretion of the inflammatory mediator Interleukin‐6 (IL‐6) following exposure to AuNPs. Finally, a microfluidic approach was developed in‐house to investigate whether pro‐inflammatory mediators present in supernatants harvested from the AuNPs‐treated cell cultures could trigger monocyte activation. Our results demonstrated that AuNPs were internalised only in submerged cultures grown on glass substrates. Nevertheless, AuNPs internalisation did not trigger a significant IL‐6 secretion. Significant amounts of IL‐6 were secreted by AuNPs‐treated mono‐cultures grown on Transwell™ inserts, triggering monocyte activation in dynamic microfluidic experiments. AuNPs did not induce IL‐6 secretion in co‐cultures and MucilAir™ models, although supernatants harvested from co‐cultures triggered monocyte activation. Our case study demonstrates that in vitro complexity, as well as culturing substrates, deeply influence the detectable cellular responses to nanoparticles, and advocate for the adoption of more advanced tissue‐mimetic cultures of the human respiratory system for nanomaterials testing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".