Multi-labeling strategy to enhance direct aptamer sensor sensitivity for detecting MUC1 tumor marker
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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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".