Size Dependent Interactions of Nanoparticles with Lung Surfactant Model Systems and the Significant Impact on Surface Potential
Bibliographic record
Abstract
The relationship between a model pulmonary surfactant system and various sized nanoparticles was investigated in this study. Diplamitoylphosphatidylcholine (DPPC) is the main lipid constituent of lung surfactant and has the ability to reach very high surface pressures (around 70 mN/m) upon compression. Due to these properties it was used as a model to simulate the lung surfactant film in vitro. The first objective of this study was to investigate the relationship between DPPC and various sized nanoparticles within the subphase through surface pressure-area isotherms. The second objective was to measure the surface potential of the different preparations (conducted on a mini-Langmuir trough) and to determine if an optimal nanoparticle size exists possessing a greater affinity for the DPPC film compared to other sizes. The results from the pressure area isotherms indicate that the interaction between DPPC and the nanoparticles is stable and that the 235 nm particles may represent an optimal size. Furthermore, the results from the surface potential experiments confirm that an interaction of the nanoparticles with the monolayer exists as indicated by surface-pressure area isotherms. Any even moderate interaction between nanoparticles and lung surfactant film might reduce or increase the surface potential of the surfactant film, and this might impact the deposition of the nanoparticles or other ligands which may be positively or negatively charged drugs within the surfactant film. Thus changes in surface potential due to nanoparticle interactions have to be taken into account for future drug targeting studies using nano-sized drug carriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".