Nanobiophotonics for molecular imaging of cancer: Au- and Ag-based Epidermal Growth Factor receptor (EGFR) specific nanoprobes
Bibliographic record
Abstract
Our aim is to create and validate a novel SERS-based nanoprobe for optical imaging of the epidermal growth factor receptor (EGFR). Gold and silver nanoparticles (Au/AgNPs) of various sizes were synthesized and coupled to epidermal growth factor (EGF) via a short ligand, α-lipoic acid (206 g/mol), which binds strongly to both Au and Ag nanoparticles via its disulfide end group. We used carbodiimide chemistry to couple EGF to α-lipoic acid. These nanoprobes were tested for binding affinity using Enzyme Linked ImmunoSorbent Assay (ELISA) and, in-vitro, using EGFRoverexpressing A431 cells. The nanoprobes show excellent EGFR-specific binding. Time of Flight Mass Spectrometry demonstrate the carbodiimide based linking of the carboxylic acid end-group of α-lipoic acid to one or more of the three (terminal, or 2 lysine) amine groups on EGF. ELISA confirms that the linked EGF is active by itself, and following conjugation with gold or silver nanoparticles. Compared with bare nanoparticles, UV-Vis spectroscopy of Ag-based nanoprobes exhibit significant plasmon red-shift, while there was no discernable shift for Au-based ones. Dark field microscopy shows abundant uptake by EGFR overexpressing A431 cells, and serves to further confirm the excellent binding affinity. Nanoprobe internalization and consequent aggregation is thought to be the basis of enhanced light scattering in the dark field images, supporting the notion that these nanoprobes should provide excellent SERS signals at all nanoprobe sizes. In summary, novel EGFR-specific nanoprobes have been synthesized and validated by standard assay and in cell culture for use as SERS optical imaging probes.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".