Spectrally Matched Duplexed Nucleic Acid Bioassay Using Two-Colors from a Single Form of Upconversion Nanoparticle
Bibliographic record
Abstract
Optical sensing can provide opportunity for simultaneous determination of multiple targets as well as for implementation of ratiometric methods that can improve accuracy and precision. Herein we report a paper-based two-color oligonucleotide detection assay with tunable sensitivity that is based on use of a single type of upconversion nanoparticle (UCNP). Water-soluble UCNPs were designed to concurrently offer green and red emission. These avidin functionalized UCNPs were adsorbed onto a cellulose support, and Cy3 was used as a green channel acceptor for Survival Motor Neuron (SMN1) target, and Cy5.5 was the red channel acceptor for the glucuronidase gene (uidA) target. Selective DNA hybridization of the labeled targets with the corresponding probe provided emission from dyes, which was the basis for concurrent quantification of both targets. The limit of detection (LOD) could be tuned by changing the relative ratio of the SMN1 and uidA probes. A higher proportion of a probe provided for a lower LOD. When the SMN1/uidA probe ratio was 1:4, the LOD for SMN1 and uidA target were 54.3 and 30.5 fmol, and when the probe ratio was 4:1, the LOD for the above targets were 22.1 and 1260 fmol, respectively. Selectivity evaluation showed that one base pair mismatched DNA for SMN1 and uidA could be discriminated in most cases. The assay showed resistance to nonspecific adsorption of interfering DNA and protein and was even functional for targets in undiluted serum. This work represents a significant step in the development of paper-based multiplexed UCNP luminescence assays.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".