Quantum‐Dot‐Encoded Microbeads for Multiplexed Genetic Detection of Non‐amplified DNA Samples
Bibliographic record
Abstract
Barcoding technologies have become the basis for a new generation of molecular diagnostic platforms for measuring biomarkers in a high-throughput, rapid, and sensitive manner. Thus far, researchers have mainly focused on preparing different types of barcodes but, in order to use them optimally in genomic- and proteomic-based applications, there is a need to understand the effect of barcode and assay parameters on their performance. Herein, quantum-dot barcodes are systematically characterized for the detection of non-amplified DNA sequences. The effect of capture probes, reporter probes, and target DNA sequence lengths are studied, as well as the effect of the amount of noncomplementary sequences on the hybridization kinetics and efficiency. From DNA denaturation to signal detection, quantum-dot-barcode assays require less than one hour to detect a target DNA sequence with a linear dynamic range of 0.02-100 fmol. Three optically distinct quantum-dot barcodes are used to demonstrate the multiplexing capability of these barcodes for genomic detection. These results suggest that quantum-dot barcodes are an excellent platform for multiplex, rapid, and sensitive genetic detection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".