Size-Dependent Gold Nanoparticle Interaction at Nano–Micro Interface Using Both Monolayer and Multilayer (Tissue-Like) Cell Models
Bibliographic record
Abstract
Gold nanoparticles (GNPs) are emerging as a novel tool to improve existing cancer therapeutics. GNPs are being used as radiation dose enhancers in radiation therapy as well as anticancer drugs carriers in chemotherapy. However, the success of GNP-based therapeutics depends on their ability to penetrate tumor tissue. GNPs of 20 and 50 nm diameters were used to elucidate the effects of size on the GNP interaction with tumor cells at monolayer and multilayer level. At monolayer cell level, smaller NPs had a lower uptake compared to larger NPs at monolayer cell level. However, the order was reversed at tissue-like multilayer level. The smaller NPs penetrated better compared to larger NPs in tissue-like materials. Based on our study using tissue-like materials, we can predict that the smaller NPs are better for future therapeutics due to their greater penetration in tumor tissue once leaving the leaky blood vessels. In this study, tissue-like multilayer cellular structures (MLCs) were grown to model the post-vascular tumor environment. The MLCs exhibited a much more extensive extracellular matrix than monolayer cell cultures. The MLC model can be used to optimize the nano-micro interface at tissue level before moving into animal models. This would accelerate the use of NPs in future cancer therapeutics.
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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".