Aptamer-Based Detection of Epithelial Tumor Marker Mucin 1 with Quantum Dot-Based Fluorescence Readout
Bibliographic record
Abstract
Mucin 1 (MUC1) is a glycoprotein expressed on most epithelial cell surfaces, which has been confirmed as a useful biomarker for the diagnosis of early cancers. In this paper, we report an aptamer-based, quantitative detection protocol for MUC1 using a 3-component DNA hybridization system with quantum dot (QD)-labeling: in the absence of MUC1 peptides, strong fluorescence is observed upon mixing the three specially designed DNA strands (quencher, QD-labeled reporter, and the MUC1 aptamer stem); in the presence of MUC1 peptides, a successive decrease in fluorescence intensity is detected since the MUC1 peptide binds to the aptamer strand in such a way to allow the quencher and fluorescence reporter to be brought into close proximity (which leads to the occurrence of fluorescence resonance energy transfer, FRET, between the quencher and QD). The detection limit for MUC1 with this novel approach is in the nanomolar (nM) level, and a linear response can be established for the approximate range found in blood serum. This study also provided further insight into the aptamer/analyte binding site/mode for MUC1, which augments the possibility of improving this detection methodology for the early diagnosis of different types of epithelial cancers of large populations.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".