Development of a sensitive, stable and EGFR‐specific molecular imaging agent for surface enhanced Raman spectroscopy
Bibliographic record
Abstract
We created and studied a novel nanoprobe for spectroscopic molecular imaging of the epidermal growth factor (EGF) receptor, whose over‐expression is a hallmark of a wide range of cancers. Silver nanoparticles (AgNPs) of 45 nm diameter were synthesized and coupled to EGF by α‐lipoic acid, a short ligand that exhibits excellent silver binding affinity. Time‐of‐flight mass spectroscopy demonstrates formation of the protein complex. Enzyme‐linked immunosorbent assay verifies the protein complex is 100% active for the EGF receptor, alone and, following conjugation to silver nanoparticles. Compared with its monosulfide analog, 6‐mercaptohexanoic acid, α‐lipoic acid is stabilized by binding to silver with a total energy that is lower by 1.38 eV, as found from Density Functional Theory (DFT)/natural bond analysis calculations. A Highest Occupied Molecular Orbital (HOMO)‐Lowest Unoccupied Molecular Orbital (LUMO) gap energy of 5.25 (spin‐up electrons) and 5.74 eV (spin‐down electrons) was obtained for the silver‐α‐lipoic acid complex. This is the first report of silver nanoparticles being attached to EGF, and the first theoretical and experimental report on the surface enhanced Raman spectroscopy spectral interpretation of α‐lipoic acid bound to silver. These nanoprobes exhibit surface enhanced Raman spectroscopy, when aggregated in solution, at picomolar concentrations and have the necessary properties – specificity, sensitivity and stability – to serve as molecular imaging agents. Copyright © 2015 John Wiley & Sons, Ltd.
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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".