Short Ligands Affect Modes of QD Uptake and Elimination in Human Cells
Bibliographic record
Abstract
In order to better understand nanoparticle uptake and elimination mechanisms, we designed a controlled set of small, highly fluorescent quantum dots (QDs) with nearly identical hydrodynamic size (8-10 nm) but with varied short ligand surface functionalization. The properties of functionalized QDs and their modes of uptake and elimination were investigated systematically by asymmetrical flow field-flow fractionation (AF4), confocal fluorescence microscopy, flow cytometry (FACS), and flame atomic absorption (FAA). Using specific inhibitors of cellular uptake and elimination machinery in human embryonic kidney cells (Hek 293) and human hepatocellular carcinoma cells (Hep G2), we showed that QDs of the same size but with different surface properties were predominantly taken up through lipid raft-mediated endocytosis, however, to significantly different extents. The latter observation infers the contribution of additional modes of QD internalization, which include X-AG cysteine transporter for cysteine-functionalized QDs (QD-CYS). We also investigated putative modes of QD elimination and established the contribution of P-glycoprotein (P-gp) transporter in QD efflux. Results from these studies show a strong dependence between the properties of QD-associated small ligands and modes of uptake/elimination in human cells.
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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".