Enhanced cellular uptake of size-separated lipophilic silicon nanoparticles
Bibliographic record
Abstract
Specific size, shape and surface chemistry influence the biological activity of nanoparticles. In the case of lipophilic nanoparticles, which are widely used in consumer products, there is evidence that particle size and formulation influences skin permeability and that lipophilic particles smaller than 6 nm can embed in lipid bilayers. Since most nanoparticle synthetic procedures result in mixtures of different particles, post-synthetic purification promises to provide insights into nanostructure-function relationships. Here we used size-selective precipitation to separate lipophilic allyl-benzyl-capped silicon nanoparticles into monodisperse fractions within the range of 1 nm to 5 nm. We measured liposomal encapsulation and cellular uptake of the monodisperse particles and found them to have generally low cytotoxicities in Hela cells. However, specific fractions showed reproducibly higher cytotoxicity than other fractions as well as the unseparated ensemble. Measurements indicate that the cytotoxicity mechanism involves oxidative stress and the differential cytotoxicity is due to enhanced cellular uptake by specific fractions. The results indicate that specific particles, with enhanced suitability for incorporation into lipophilic regions of liposomes and subsequent in vitro delivery to cells, are enriched in certain fractions.
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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 | 0.000 |
| 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 | 0.000 |
| 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".