Phenotype Determines Nanoparticle Uptake by Human Macrophages from Liver and Blood
Bibliographic record
Abstract
A significant challenge to delivering therapeutic doses of nanoparticles to targeted disease sites is the fact that most nanoparticles become trapped in the liver. Liver-resident macrophages, or Kupffer cells, are key cells in the hepatic sequestration of nanoparticles. However, the precise role that the macrophage phenotype plays in nanoparticle uptake is unknown. Here, we show that the human macrophage phenotype modulates hard nanoparticle uptake. Using gold nanoparticles, we examined uptake by human monocyte-derived macrophages that had been driven to a "regulatory" M2 phenotype or an "inflammatory" M1 phenotype and found that M2-type macrophages preferentially take up nanoparticles, with a clear hierarchy among the subtypes (M2c > M2 > M2a > M2b > M1). We also found that stimuli such as LPS/IFN-γ rather than with more "regulatory" stimuli such as TGF-β/IL-10 reduce per cell macrophage nanoparticle uptake by an average of 40%. Primary human Kupffer cells were found to display heterogeneous expression of M1 and M2 markers, and Kupffer cells expressing higher levels of M2 markers (CD163) take up significantly more nanoparticles than Kupffer cells expressing lower levels of surface CD163. Our results demonstrate that hepatic inflammatory microenvironments should be considered when studying liver sequestration of nanoparticles, and that modifying the hepatic microenvironment might offer a tool for enhancing or decreasing this sequestration. Our findings also suggest that models examining the nanoparticle/macrophage interaction should include studies with primary tissue macrophages.
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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".