Real-Time Imaging of Interactions Between Dipalmitoylphosphatidylcholine Monolayers and Gelatin Based Nanoparticles Using Brewster Angle Microscopy
Bibliographic record
Abstract
Given the current interest in the pulmonary route for targeted drug delivery, assessing the impact of drug delivery vehicles on the surfactant layer lining the surface of the lung alveoli is critical. As gelatin-based nanoparticles are one such vehicle, this study addresses their interaction with the major saturated phospholipid component of native lung surfactant, dipalmitoylphosphatidylcholine (DPPC). Nanoparticles are colloidal particles in the size range of 1 to 1000 nm that are presently investigated for site-specific drug delivery in the emerging field of nanomedicine. Monolayer studies of DPPC films were performed both in the presence and absence of nanoparticles in order to assess the interaction in terms of average molecular areas occupied at given surface pressures. In Brewster angle microscopy experiments, nanoparticles significantly changed the shape and reduced the size of DPPC domains suggesting a considerable interaction of the two systems. For safe pulmonary drug delivery, understanding this interaction is a prerequisite so nanoparticles can be a feasible alternative to more conventional therapies in the future.
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 | 0.000 |
| 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 | 0.001 |
| 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".