Differential Interactions of Gelatin Nanoparticles with the Major Lipids of Model Lung Surfactant: Changes in the Lateral Membrane Organization
Bibliographic record
Abstract
There has been an increasing interest in the potential of nanomedicine, particularly in the use of nanoparticles between 10 nm and 1 μm in diameter as drug delivery vehicles. For pulmonary drug delivery, it is important to understand the effect of polymeric nanoparticles on the lung surfactant in order to optimize the carriers by reducing their potential toxicological effects. This work presents a biophysical study of the impact of gelatin nanoparticles on packing and lateral organization of simple and complex lipid layers containing the major components of lung surfactant. Zwitterionic phosphatidylcholines, negatively charged phosphatidylglycerols, and the sterol cholesterol were employed in the models. In addition, the impact of acyl chain length was investigated. Packing was determined by surface pressure-area isotherms, whereas direct imaging of the surfactant at the air-water interface was performed using Brewster angle microscopy. Our results indicate minor changes in the surface pressure-area isotherms but concomitantly significant effects on the lateral organization of the monolayers upon nanoparticle addition. The data also suggest differential interactions of nanoparticles with the major lipid classes. Gelatin nanoparticles interact stronger with negatively charged phosphatidyl-glycerols compared to zwitterionic phosphatidyl-cholines. Furthermore, charge distribution depending on the molar lipid ratio and acyl chain saturation is important as well. Even cholesterol, whose concentration is low compared to other components, plays an important role in nanoparticle interactions.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".