An investigation of the effect of the protein corona on the cellular uptake of nanoliposomes under flow conditions using quartz crystal microgravimetry with dissipation
Bibliographic record
Abstract
, respectively), the QCM-D results provided strong evidence of the role of the protein corona in the cellular interaction of these NLP formulations, with a variation in their adsorption kinetics depending on their initial composition. NLP's attachment to the cell surface was the lowest for PEGylated NLPs (<5%), while the positively charged NLPs showed the highest cellular attachment (≈100%), regardless of the presence of the protein corona or cell type. The effect of the protein corona was more pronounced for the negatively charged NLPs, where a significant reduction in the NLP attachment was observed. To complement the QCM-D data on the NLP attachment and to determine whether the NLP attachment leads to cellular uptake, confocal microscopy and flow cytometry were used to confirm NLP uptake by A375 and THP1 cells. Proteomic analysis revealed a differential composition of the protein corona on the various NLPs with possible implications for their sequestration and cellular uptake. Collectively, the findings suggest that QCM-D can be an important tool to study the binding of NLP formulations or other nanoparticles with cell membranes under dynamic flow, which very often differs from nanoparticle uptake under static conditions.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".