Evaluation of thiazole intercalating dyes as acceptors for quantum dot donors in Förster resonance energy transfer
Bibliographic record
Abstract
Fluorescent probes suitable for the selective detection of DNA sequences are important in genomic research, disease diagnostics, and pathogen detection, among many other applications. The unique optical properties of semiconductor quantum dots (QDs) have proven to be highly valuable for development of fluorescent probes and biosensors. We describe preliminary work toward combining QDs with monomeric thiazole dyes for the detection of nucleic acid hybridization. BO, TO, BO3, and TO3 dyes, which span the visible spectrum, were synthesized with undecanoic acid linkers to permit bioconjugation and their fluorescent enhancements in response to DNA oligonucleotides was evaluated. Contrast ratios between single-stranded probe oligonucleotide and double-stranded probe/target hybrids were between 2.5 and 7.5. BO3 and TO3 were used to label a polyhistidine-appended peptide that self-assembled to QDs and were found to be suitable acceptor dyes for Förster resonance energy transfer (FRET) with QD donors that had their peak emission at 540 nm and 625 nm, respectively. We further conjugated a probe oligonucleotide to a polyhistidineappended peptide at an internal site, and this probe also self-assembled to QDs. Mixing these conjugates with BO3 and either complementary DNA target or non-complementary DNA could induce quenching of the QD emission via FRET, but no FRET-sensitized BO3 emission was observed. Experiments suggested that binding of BO3 to the interface of the QDs was in competition with binding to DNA. Our results provide insight into important criteria (e.g., QD surface chemistry) for designing and optimizing a QD-FRET probe for DNA detection that utilizes the fluorescent properties of monomeric thiazole intercalating dyes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".