Paper-based platform for detection by hybridization using intrinsically labeled fluorescent oligonucleotide probes on quantum dots
Bibliographic record
Abstract
A paper-based platform was investigated in which the selective detection of oligonucleotide targets by hybridization was accomplished via the enhancement of fluorescence emission from intrinsically labeled DNA probes that were immobilized on the surface of quantum dots (QDs). Multiple copies of a derivative of thiazole orange, an intercalating dye known to form non-emissive dimers, were conjugated to single-stranded oligonucleotide probes. Dimerization resulted in the formation of H-aggregates where excitonic interactions led to the suppression of fluorescence. The hybridization of the oligonucleotide probe with a complementary target resulted in the enhancement of fluorescence emission as the dimers dissociated and the dyes preferentially intercalated with the duplex. The detection of oligonucleotide targets using this configuration eliminated the need for labeling the target strands, and fluorescence intensity was proportional to the extent of hybridization. In addition, the dye molecules were excited using Foerster Resonance Energy Transfer (FRET) from QD donors, which resulted in improved selectivity and allowed for ratiometric detection. A solution-phase hybridization assay based on similar operational principles has been previously reported, and this new work investigated the advantages offered for this transduction scheme using paper-based solid-phase substrates. QD-probe conjugates were immobilized in sufficient density on the paper matrix to provide for multiple-donor-multiple-acceptor interactions that resulted in a 20-fold enhancement of acceptor emission compared to the solution-based assay, providing a limit of detection of 0.1 pmol. The paper-based assay provided for the reduction of the time needed for sample preparation and data acquisition, demonstrated that transduction was possible in a complex matrix (goat serum) without compromising on the performance observed in buffer solution, and that oligonucleotides generated from standard PCR amplification could be detected.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".