Developing Mixed Films of Immobilized Oligonucleotides and Quantum Dots for the Multiplexed Detection of Nucleic Acid Hybridization Using a Combination of Fluorescence Resonance Energy Transfer and Direct Excitation of Fluorescence
Bibliographic record
Abstract
Methods have been developed for the simultaneous and selective detection of three target nucleic acid sequences based on mixed films of immobilized quantum dots (QDs) and oligonucleotide probes. CdSe/ZnS QDs were immobilized on optical fibers and conjugated with mixtures of different probe oligonucleotides. Hybridization events were detected using a combination of fluorescence from direct excitation and fluorescence sensitized by resonance energy transfer (FRET). A sandwich assay format was used to associate dye labeled reporter oligonucleotides with probe-target hybrids formed at the surface of the optical fiber. One detection channel utilized direct excitation of Pacific Blue and the two other detection channels were based on FRET. In one strategy, green emitting QDs were used as donors with Cy3 and Rhodamine Red-X acceptors. In a second strategy, green and red emitting QDs were coimmobilized and used as donors with Cy3 and Alexa Fluor 647 acceptors, respectively. Selective three-plex detection was demonstrated with both strategies. Several key design criteria that were explored to optimize the relative signal magnitude between channels included: the ratio of probe associated with direct excitation versus probes associated with FRET; the relative amounts of each FRET probe and corresponding spectral overlap; and the photoluminescence ratio between immobilized green and red emitting QDs (where applicable). Careful selection of probe sequences and lengths were important for the discrimination of single nucleotide polymorphisms in one channel without suppressing binding of target in the other two channels. This work provides a basis for the development of multiplexed biosensors that are ensemble compatible and do not require discrete sensor elements, spatial registration, sorting technology, or single molecule spectroscopy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".