Toward A Multiplexed Solid-Phase Nucleic Acid Hybridization Assay Using Quantum Dots as Donors in Fluorescence Resonance Energy Transfer
Bibliographic record
Abstract
Solid-phase assays using immobilized quantum dots (QDs) as donors in fluorescence resonance energy transfer (FRET) have been developed for the selective detection of nucleic acids. QDs were immobilized on optical fibers and conjugated with probe oligonucleotides. Hybridization with acceptor labeled target oligonucleotides generated FRET-sensitized acceptor fluorescence that was used as the analytical signal. A sandwich assay was also introduced and avoided the need for target labeling. Green and red emitting CdSe/ZnS QDs were used as donors with Cy3 and Alexa Fluor 647 acceptors, respectively. Quantitative measurements were made via spectrofluorimetry or fluorescence microscopy. Detection limits as low as 1 nM were obtained, and the discrimination of single nucleotide polymorphisms (SNPs) with contrast ratios as high as 31:1 was possible. The assays retained their selectivity and at least 50% of their signal when tested in bovine serum and against a large background of noncomplementary genomic DNA. Mixed films of the two colors of QD and two probe oligonucleotide sequences were prepared for multiplexed solid-phase hybridization assays. It was possible to simultaneously detect two target sequences with retention of selectivity, including SNP discrimination. This research provides an important precedent and framework for the future development of QD-based bioassays and biosensors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".