Investigating the specific uptake of EGF-conjugated nanoparticles in lung cancer cells using fluorescence imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it