Ultrasensitive Lateral Flow Immunoassay of Fluoroquinolone Antibiotic Gatifloxacin Using Au@Ag Nanoparticles as a Signal-Enhancing Label
Bibliographic record
Abstract
Gatifloxacin (GAT), an antibiotic belonging to the fluoroquinolone (FQ) class, is a toxicant that may contaminate food products. In this study, a method of ultrasensitive immunochromatographic detection of GAT was developed for the first time. An indirect format of the lateral flow immunoassay (LFIA) was performed. GAT-specific monoclonal antibodies and labeled anti-species antibodies were used in the LFIA. Bimetallic core@shell Au@Ag nanoparticles (Au@Ag NPs) were synthesized as a new label. Peroxidase-mimic properties of Au@Ag NPs allowed for the catalytic enhancement of the signal on test strips, increasing the assay sensitivity. A mechanism of Au@Ag NPs-mediated catalysis was deduced. Signal amplification was achieved through the oxidative etching of Au@Ag NPs by hydrogen peroxide. This resulted in the formation of gold nanoparticles and Ag+ ions, which catalyzed the oxidation of the peroxidase substrate. Such “chemical enhancement” allowed for reaching the instrumental limit of detection (LOD, calculated by Three Sigma approach) and cutoff of 0.8 and 20 pg/mL, respectively. The enhanced assay procedure can be completed in 21 min. The enhanced LFIA was tested for GAT detection in raw meat samples, and the recoveries from meat were 78.1–114.8%. This method can be recommended as a promising instrument for the sensitive detection of various toxicants.
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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) | 0.001 | 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 | 0.000 |
| 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".