Fullerenes’ Interactions with Plasma Membranes: Insight from the MD Simulations
Bibliographic record
Abstract
Understanding the interactions between carbon nanoparticles (CNPs) and biological membranes is critically important for applications of CNPs in biomedicine and toxicology. Due to the complexity and diversity of the systems, most molecular simulation studies have focused on the interactions of CNPs and single component bilayers. In this work, we performed coarse-grained molecular dynamic (CGMD) simulations to investigate the behaviors of fullerenes in the presence of multiple lipid components in the plasma membranes with varying fullerene concentrations. Our results reveal that fullerenes can spontaneously penetrate the plasma membrane. Interestingly, fullerenes prefer to locate themselves in the region of the highly unsaturated lipids that are enriched in the inner leaflet of the plasma membrane. This causes fullerene aggregation even at low concentrations. When increasing fullerene concentrations, the fullerene clusters grow, and budding may emerge at the inner leaflet of the plasma membrane. Our findings suggest by tuning the lipid composition, fullerenes can be loaded deeply inside the plasma membrane, which can be useful for designing drug carrier liposomes. Moreover, the mechanisms of how fullerenes perturb multicomponent cell membranes and how they directly enter the cell are proposed. These insights can help to determine fullerene toxicity in living cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".