Luminescence Resonance Energy Transfer-Based Nucleic Acid Hybridization Assay on Cellulose Paper with Upconverting Phosphor as Donors
Bibliographic record
Abstract
A bioassay based on DNA hybridization on cellulose paper is a promising format for gene fragment detection that may be suited for in-field and rapid diagnostic applications. We demonstrate for the first time that luminescence resonance energy transfer (LRET) associated with upconverting phosphors (UCPs) can be used to develop a paper-based DNA hybridization assay with high sensitivity, selectivity and fast response. UCPs with strong green emission were synthesized and subsequently functionalized with streptavidin (UCP-strep). UCP-strep particles were immobilized on cellulose paper, and then biotinylated single-stranded oligonucleotide probes were conjugated onto the UCPs via streptavidin-biotin linkage. The UCPs served as donors that were LRET-paired with Cy3-labeled target DNA. Selective DNA hybridization enabled the proximity required for LRET-sensitized emission from Cy3, which was used as the detection signal. Hybridization was complete within 2 min, and the limit of detection of the method was 34 fmol, which is a significant improvement in comparison to an analogous fluorescence resonance energy transfer (FRET) assay based on quantum dots. The assay exhibited excellent resistance to nonspecific adsorption of noncomplementary short/long DNA and protein. The selectivity of the assay was further evaluated by one base pair mismatched (1BPM) DNA detection, where a maximum signal ratio of 3.1:1 was achieved between fully complementary and 1BPM samples. This work represents a preliminary but significant step for the development of paper-based UCP-LRET nucleic acid hybridization assays, which offer potential for lowering the limit of detection of luminescent hybridization assays due to the negligible background signal associated with optical excitation by near-infrared (NIR) light.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".