Paper-Based Solid-Phase Multiplexed Nucleic Acid Hybridization Assay with Tunable Dynamic Range Using Immobilized Quantum Dots As Donors in Fluorescence Resonance Energy Transfer
Bibliographic record
Abstract
A multiplexed solid-phase nucleic acid hybridization assay on a paper-based platform is presented using multicolor immobilized quantum dots (QDs) as donors in fluorescence resonance energy transfer (FRET). The surface of paper was modified with imidazole groups to immobilize two types of QD-probe oligonucleotide conjugates that were assembled in solution. Green-emitting QDs (gQDs) and red-emitting QDs (rQDs) served as donors with Cy3 and Alexa Fluor 647 (A647) acceptors. The gQD/Cy3 FRET pair served as an internal standard, while the rQD/A647 FRET pair served as a detection channel, combining the control and analytical test zones in one physical location. Hybridization of dye-labeled oligonucleotide targets provided the proximity for FRET sensitized emission from the acceptor dyes, which served as an analytical signal. Hybridization assays in the multicolor format provided a limit of detection of 90 fmol and an upper limit of dynamic range of 3.5 pmol. The use of an array of detection zones was designed to provide improved analytical figures of merit compared to that which could be achieved on one type of array design in terms of relative concentration of multicolor QDs. The hybridization assays showed excellent resistance to nonspecific adsorption of oligonucleotides. Selectivity of the two-plex hybridization assay was demonstrated by single nucleotide polymorphism (SNP) detection at a contrast ratio of 50:1. Additionally, it is shown that the use of preformed QD-probe oligonucleotide conjugates and consideration of the relative number density of the two types of QD-probe conjugates in the two-color assay format is advantageous to maximize assay sensitivity and the upper limit of dynamic range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".