Camera-Based Ratiometric Fluorescence Transduction of Nucleic Acid Hybridization with Reagentless Signal Amplification on a Paper-Based Platform Using Immobilized Quantum Dots as Donors
Bibliographic record
Abstract
Paper-based diagnostic assays are gaining increasing popularity for their potential application in resource-limited settings and for point-of-care screening. Achievement of high sensitivity with precision and accuracy can be challenging when using paper substrates. Herein, we implement the red-green-blue color palette of a digital camera for quantitative ratiometric transduction of nucleic acid hybridization on a paper-based platform using immobilized quantum dots (QDs) as donors in fluorescence resonance energy transfer (FRET). A nonenzymatic and reagentless means of signal enhancement for QD-FRET assays on paper substrates is based on the use of dry paper substrates for data acquisition. This approach offered at least a 10-fold higher assay sensitivity and at least a 10-fold lower limit of detection (LOD) as compared to hydrated paper substrates. The surface of paper was modified with imidazole groups to assemble a transduction interface that consisted of immobilized QD-probe oligonucleotide conjugates. Green-emitting QDs (gQDs) served as donors with Cy3 as an acceptor. A hybridization event that brought the Cy3 acceptor dye in close proximity to the surface of immobilized gQDs was responsible for a FRET-sensitized emission from the acceptor dye, which served as an analytical signal. A hand-held UV lamp was used as an excitation source and ratiometric analysis using an iPad camera was possible by a relative intensity analysis of the red (Cy3 photoluminescence (PL)) and green (gQD PL) color channels of the digital camera. For digital imaging using an iPad camera, the LOD of the assay in a sandwich format was 450 fmol with a dynamic range spanning 2 orders of magnitude, while an epifluorescence microscope detection platform offered a LOD of 30 fmol and a dynamic range spanning 3 orders of magnitude. The selectivity of the hybridization assay was demonstrated by detection of a single nucleotide polymorphism at a contrast ratio of 60:1. This work provides an important framework for the integration of QD-FRET methods with digital imaging for a ratiometric transduction of nucleic acid hybridization on a paper-based platform.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".