A combination of positive dielectrophoresis driven on-line enrichment and aptamer-fluorescent silica nanoparticle label for rapid and sensitive detection of <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Staphylococcus aureus (S. aureus) is an important human pathogen that causes several diseases ranging from superficial skin infections to life-threatening diseases. Here, a method combining positive dielectrophoresis (pDEP) driven on-line enrichment and aptamer-fluorescent silica nanoparticle label has been developed for the rapid and sensitive detection of S. aureus in microfluidic channels. An aptamer, having high affinity to S. aureus, is used as the molecular recognition tool and immobilized onto chloropropyl functionalized fluorescent silica nanoparticles through a click chemistry approach to obtain S. aureus aptamer-nanoparticle bioconjugates (Apt(S.aureus)/FNPs). The pDEP driven on-line enrichment technology was used for accumulating the Apt(S.aureus)/FNP labeled S. aureus. After incubating with S. aureus, the mixture of Apt(S.aureus)/FNP labeled S. aureus and Apt(S.aureus)/FNPs was directly introduced into the pDEP-based microfluidic system. By applying an AC voltage in a pDEP frequency region, the Apt(S.aureus)/FNP labelled S. aureus moved to the electrodes and accumulated in the electrode gap, while the free Apt(S.aureus)/FNPs flowed away. The signal that came from the Apt(S.aureus)/FNP labelled S. aureus in the focused detection areas was then detected. Profiting from the specificity of aptamer, signal amplification of FNP label and pDEP on-line enrichment, this assay can detect as low as 93 and 270 cfu mL(-1)S. aureus in deionized water and spiked water samples, respectively, with higher sensitivities than our previously reported Apt(S.aureus)/FNP based flow cytometry. Moreover, without the need for separation and washing steps usually required for FNP label involved bioassays, the total assay time including sample pretreatment was within 2 h.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".