Multi-labeling strategy to enhance direct aptamer sensor sensitivity for detecting MUC1 tumor marker
Why this work is in the frame
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Bibliographic record
Abstract
Aptamers hold great potential for point-of-care diagnostics (POC), but the complexity of sensor architectures and poor sensitivities in detecting small molecules remain challenging. In this study, we present a simple but effective approach to enhance the sensitivity of the electrochemical ap-tamer-based ( E -AB) sensors. The proposed aptamer was labeled by double redox tags through a lysine linker and incorporated with an optimized length of passivation layer, which cooperatively led to gain enhancement and thus higher sensitivity. The analytical performance of this E -AB sen-sor was measured and compared with a conventional E-AB sensor towards the detection of MUC1 in buffer and serum. Our study revealed the double-tagged aptamer with a lysine linker's superior performance, yielding a low 2.4 nM limit of detection (LOD) for MUC1 in buffer, with a wide lin-ear dynamic range (LDR) from 5.0 × 101 to 4.0 × 102 nM. In contrast, the conventional counterpart exhibited a tenfold higher LOD (25.7 nM). This innovative synthetic strategy addresses the limita-tions of the signal-to-noise ratio (S/N) and the need for higher sensitivity towards the detection of the tumor markers, which may hold promise for rapid simple-to-answer technology for P.O·C testing.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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