Interfacial Transduction of Nucleic Acid Hybridization Using Immobilized Quantum Dots as Donors in Fluorescence Resonance Energy Transfer
Bibliographic record
Abstract
Fluorescence resonance energy transfer (FRET) using immobilized quantum dots (QDs) as energy donors was explored as a transduction method for the detection of nucleic acid hybridization at an interface. This research was motivated by the success of the QD-FRET-based transduction of nucleic acid hybridization in solution-phase assays. This new work represents a fundamental step toward the assembly of a biosensor, where immobilization of the selective chemistry on a surface is desired. After immobilizing QD-probe oligonucleotide conjugates on optical fibers, a demonstration of the retention of selectivity was achieved by the introduction of acceptor (Cy3)-labeled single-stranded target oligonucleotides. Hybridization generated the proximity required for FRET, and the resulting fluorescence spectra provided an analytical signal proportional to the amount of target. This research provides an important framework for the future development of nucleic acid biosensors based on QDs and FRET. The most important findings of this work are that (1) a QD-FRET solid-phase hybridization assay is viable and (2) a passivating layer of denatured bovine serum albumin alleviates nonspecific adsorption, ultimately resulting in (3) the potential for a reusable assay format and mismatch discrimination. In this, the first incarnation of a solid-phase QD-FRET hybridization assay, the limit of detection was found to be 5 nM, and the dynamic range was almost 2 orders of magnitude. Selective discrimination of the target was shown using a three-base-pairs mismatch from a fully complementary sequence. Despite a gradual loss of signal, reuse of the optical fibers over multiple cycles of hybridization and dehybridization was possible. Directions for further improvement of the analytical performance by optimizing the design of the QD-probe oligonucleotide interface are identified.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".