Elucidating the Uptake and Distribution of Nanoparticles in Solid Tumors via a Multilayered Cell Culture Model
Bibliographic record
Abstract
Multicellular layers (MCLs) have previously been used to determine the pharmacokinetics of a variety of different cancer drugs including paclitaxel, doxorubicin, methotrexate, and 5-fluorouracil across a number of cell lines. It is not known how nanoparticles (NPs) navigate through the tumor microenvironment once they leave the tumor blood vessel. In this study, we used the MCL model to study the uptake and penetration dynamics of NPs. Gold nanoparticles (GNPs) were used as a model system to map the NP distribution within tissue-like structures. Our results show that NP uptake and transport are dependent on the tumor cell type. MDA-MB-231 tissue showed deeper penetration of GNPs as compared to MCF-7 one. Intracellular and extracellular distributions of NPs were mapped using CytoViva imaging. The ability of MCLs to mimic tumor tissue characteristics makes them a useful tool in assessing the efficacy of particle distribution in solid tumors.
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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) | 0.000 | 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 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".