Investigating the specific uptake of EGF-conjugated nanoparticles in lung cancer cells using fluorescence imaging
Bibliographic record
Abstract
Targeted nanoparticles have the potential to deliver a large drug payload specifically to cancer cells. Targeting requires that a ligand on the nanoparticle surface interact with a specific membrane receptor on target cells. However, the contribution of the targeting ligand to nanoparticle delivery is often influenced by non-specific nanoparticle uptake or secondary targeting mechanisms. In this study, we investigate the epidermal growth factor (EGF) receptor-targeting specificity of a nanoparticle by dual-color fluorescent labeling. The targeted nanoparticle was a fluorescently labeled, EGF-conjugated HDL-like peptide-phospholipid scaffold (HPPS) and the cell lines expressed EGF receptor linked with green fluorescent protein (EGFR-GFP). Using LDLA7 cells partially expressing EGFR-GFP, fluorescence imaging demonstrated the co-internalization of EGFR-GFP and EGF-HPPS, thus validating its targeting specificity. Furthermore, specific EGFR-mediated uptake of the EGF-HPPS nanoparticle was confirmed using human non-small cell lung cancer A549 cells. Subsequent confocal microscopy and flow cytometry studies delineated how secondary targeting mechanisms affected the EGFR targeting. Together, this study confirms the EGFR targeting of EGF-HPPS in lung cancer cells and provides insight on the potential influence of unintended targets on the desired ligand-receptor interaction.
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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".