The Role of Mucin in the Toxicological Impact of Polystyrene Nanoparticles
Bibliographic record
Abstract
The development of novel oral drug delivery systems is an expanding area of research and both new approaches for improving their efficacy and the investigation of their potential toxicological effect are crucial and should be performed in parallel. Polystyrene nanoparticles (NPs) have been used for the production of diagnostic and therapeutic nanosystems, are widely used in food packaging, and have also served as models for investigating NPs interactions with biological systems. The mucous gel layer that covers the epithelium of the gastrointestinal system is a complex barrier-exchange system that it is mainly constituted by mucin and it constitutes the first physical barrier encountered after ingestion. In this study, we aimed to investigate the effect of polystyrene NPs on mucin and its potential role during NP⁻cell interactions. For this purpose, we evaluated the interaction of polystyrene NPs with mucin in dispersion by dynamic light scattering and with a deposited layer of mucin using a quartz crystal microbalance with dissipation technology. Next, we measured cell viability and the apoptotic state of three enterocyte-like cell lines that differ in their ability to produce mucin, after their exposure to the NPs. Positive charged NPs showed the ability to strongly interact and aggregate mucin in our model. Positive NPs affected cell viability and induced apoptosis in all cell lines independently of their ability of produce mucin.
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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".