Weakly adhered model cell membranes are prone to faster and more severe disruption by nanoparticles
Bibliographic record
Abstract
Identifying the mechanism(s) of interaction of nanoparticles (NPs) with cell membranes is key to understanding their potential cytotoxicity and their biomedical applications such as nano-carriers for targeted drug delivery, cancer therapy and bioimaging.Cell membranes are a complex matrix of biomolecules; they are very difficult to isolate and manipulate for systematic studies with NPs.Given the fact that the mechanical and physicochemical properties of cell membranes are mostly determined by the abundant phospholipids of the membrane, supported phospholipid bilayers (SPBs) are commonly used as simplified models of cell membranes.However, SPBs are soft thin films, and, as such, their properties can be significantly affected by the underlying substrates.Thus, conclusions drawn using SPBs may not be reflective of the realistic interaction scenarios between NPs and cell membranes.In this thesis, a robust method for tailoring the interfacial interaction of an electrically charged SPB-substrate system based on modulations in the solution chemistry is developed and investigated using the dissipation signal of the quartz crystal microbalance with dissipation monitoring (QCM-D).In the vicinity of its isoelectric point, the surface charge of the amphoteric substrate is highly dependent on the solution pH, while the phospholipids are mostly non-sensitive to changes in medium pH.The weakened substrate-SPB interface results in the larger dissipation of the oscillation, which is within the detectable range of the commercially available QCM-D instrument.The developed method does not require any specific sample preparation and can be performed in situ.Systematic studies with cationic and anionic NPs reveal that the bilayer response to the modulations in the interfacial interaction with its underlying substrate can be used as a sensitive tool to probe the integrity of SPBs upon their exposure to NPs.As expected, anionic NPs tend to impart no significant damage to the anionic bilayers, whereas cationic NPs can be detrimental to bilayer integrity
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".