Targeted Enlargement of Aptamer Functionalized Gold Nanoparticles for Quantitative Protein Analysis
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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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) | 0.001 | 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 | 0.001 |
| 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 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".