Nanoplastics in Simulated Human Lung Fluids: Aggregation Kinetics, Theoretical Model Simulation, and Effects on Pulmonary Bacteria
Bibliographic record
Abstract
The widespread presence of nanoplastics poses serious threats to public health with inhalation emerging as a crucial exposure pathway. However, their behavior and toxicity in the pulmonary microenvironment are largely unknown. In this study, polystyrene nanoplastics (PSNPs) with different diameters and surface modifications were employed to investigate the aggregation kinetics, interaction mechanisms, and effects on pulmonary bacteria in different lung fluids. All PSNPs aggregated in inflammatory artificial lysosomal fluid (ALF), whereas bare- and carboxyl-modified PSNPs remained stable in healthy Gamble's solution (GS) and modified GS (MGS). Aggregation of PSNPs in ALF and GS followed the classical Derjaguin-Landau-Verwey-Overbeek theory, while steric hindrance and hydrogen bonding between PSNPs and the lung surfactant governed the aggregation in MGS. Furthermore, redundancy analysis indicated that the aggregation of PSNPs reduced oxidative stress and membrane damage to bacteria in lung fluids. Specifically, amino-modified PSNPs, which aggregated in GS, could induce sustained oxidative stress, damage the cell membrane of native pulmonary bacteria, and promote the release of pyoverdine from pathogenic bacteria up to 2.16 times. Moreover, the aging process significantly enhanced the toxicity of PSNPs on pulmonary bacteria. These findings enhance our understanding of the behavior and toxic effects of nanoplastics on humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".