Targeted Enlargement of Aptamer Functionalized Gold Nanoparticles for Quantitative Protein Analysis
Why this work is in the frame
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Bibliographic record
Abstract
The ability to selectively amplify the detection signals for targets over interferences is crucial when analyzing proteins in a complicated sample matrix. Here, we describe a targeted enlargement strategy that can amplify the light-scattering signal from aptamer-functionalized gold nanoparticles (Apt-AuNP) with high specificity for quantitative protein analysis. This strategy is achieved by labeling target proteins with competitively protected Apt-AuNP probes and enlarging the probes with gold enhancement. This competitive protection strategy could effectively eliminate nonspecific protein adsorptions from a sample matrix, leading to a highly specific labeling of the target protein. As a result, the subsequent amplification of the light-scattering signal by gold enhancement only occurs in the presence of the target protein. This strategy was successfully demonstrated by analyzing human α-thrombin in human serum samples in a Western blot format.
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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 | 0.000 | 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) | 0.000 | 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