Simultaneous Determination of (Fluoro)quinolone Antibiotics in Kidney, Marine Products, Eggs, and Muscle by Enzyme-Linked Immunosorbent Assay (ELISA)
Bibliographic record
Abstract
A direct competitive enzyme-linked immunosorbent assay (ELISA) was developed to detect a broad range of (fluoro)quinolones in various matrices. In the optimized generic test, anti-sarafloxacin antibodies in combination with norfloxacin conjugate showed 50% binding inhibition at 0.21 ng mL(-)(1) for sarafloxacin in buffer. Screening for this class of antibiotics is accomplished using a simple, rapid extraction carried out with a 1:1 mixture of methanol and phosphate-buffered saline adjusted to pH 7.4. This common extraction was able to detect 15 (fluoro)quinolone residues such as sarafloxacin, norfloxacin, difloxacin, ciprofloxacin, pefloxacin, ofloxacin, cinoxacin, danofloxacin, enrofloxacin, marbofloxacin, lomefloxacin, enoxacin, flumequine, oxolinic acid, and nalidixic acid in pig kidney, poultry muscle, egg, fish, and shrimp. The assay's detection capabilities (CCbeta) for most of these compounds were <10 microg kg(-)(1) except for the sarafloxacin-, oxolinic acid-, flumequine-, and cinoxacin-spiked matrices, the estimated CCbeta values of which were <4, <25, <100, and <200 microg kg(-)(1), respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".