The development of a silica nanoparticle-based label-free DNA biosensor
Bibliographic record
Abstract
A silica nanoparticle-based DNA biosensor capable of detecting Bacillus anthracis bacteria through the use of unlabelled ss-oligonucleotides has been developed. The biosensor makes use of the optical changes that accompany a nanoparticle-immobilized cationic conjugated polymer (polythiophene) interacting with single-stranded vs. hybridized oligonucleotides, where a fluorescence signal appears only when hybridized DNA is present (i.e. only when the ss-oligonucleotide interacting with the polymer has hybridized with its complement). In order to enhance the sensitivity of the biosensor, two different nanoparticle architectures were developed and used to elucidate how the presence of neighboring fluorophores on the nanoparticle surface affects Förster-resonant energy transfer (FRET) between the polythiophene/oligonucleotide complex (FRET donor) and the fluorophores (FRET acceptors). We demonstrate that the silica nanoparticle-based FRET platform lowers the limit of detection at least 10-fold in comparison to the polythiophene itself, and allows the detection of ∼2 × 10(-12) moles of ss-oligonucleotide in a 100 μL sample with a standard fluorimeter (i.e. has a limit of detection of ∼2 nM ssDNA). Such nanoparticle-based biosensor platforms are beneficial because of the robustness and stability inherent to their covalent assembly and they provide a valuable new tool that may allow for the sensitive, label-free detection (the target DNA that produces the fluorescence signal is unlabelled) without the use of polymerase chain reaction.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".