Synthesis and characterization of N‐rich fluorescent bio‐dots as a reporter in the design of dual‐labeled FRET probe for TaqMan PCR: A feasibility study
Bibliographic record
Abstract
DNA-based analytical techniques have provided an advantageous sensing assay in the realm of biotechnology. Bio-inspired fluorescent nanodots are a novel type of biological staining agents with excellent optical properties widely used for cellular imaging and diagnostics. In the present research, we successfully synthesized bio-dots with excellent optical properties and high-quantum yield from DNA sodium salt through the hydrothermal method. We conjugated the bio-dots with 3' Eclipse Dark Quencher (Eclipse)-labeled single-strand oligodeoxyribonucleotide according to carbodiimide chemistry, to design a fluorescence resonance energy transfer (FRET) probe. The results confirmed the prosperous synthesis and surface functionalization of the bio-dot. Analysis of size, zeta potential, and FTIR spectroscopy verified successful bioconjugation of the bio-dots with probes. UV-visibility analysis and fluorescence intensity profile of the bio-dot and bio-dot@probes represented a concentration-dependent quenching of fluorescent signal of bio-dot by Eclipse after probe conjugation. The results demonstrated that TaqMan PCR was not feasible using the designed bio-dot@probes. Our results indicated that bio-dot can be used as an efficient fluorescent tag in the design of fluorescently labeled oligonucleotides with high biocompatibility and optical features.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".