Molecular Modelling Of Nanoparticle Delivery through Normal and Cancer Cell Membranes
Bibliographic record
Abstract
Nanoparticles find diverse applications in biomedical engineering, with targeted drug delivery (TDD) being a major focus.The experimental trials for the optimisation of TDD require a lot of time and effort.In this work, we perform molecular dynamics simulations to analyse the effect of the interaction of gold and silver nanoparticles with normal and cancer cell membranes to exploit their migration potential to cross the lipid bilayer membrane.The permeation of nanoparticles has been studied through both unconstrained and constrained simulations.Our observations hint at a pronounced affinity of nanoparticles to the hydrophobic tail region of lipid molecules, resulting in the pulling of lipid molecules along with the particles.We believe this observation holds promise for enhancing the functionalisation of nanoparticles in drug delivery applications.Furthermore, gold nanoparticles exhibit better penetration potential through the bilayer compared to silver nanoparticles.The insights gained from this study can be utilised in the development of effective in-silico drug screening models for cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".