Design and characterization of a direct ELISA for the detection and quantification of leucomalachite green
Bibliographic record
Abstract
Malachite green (MG), a member of the N-methylated triphenylmethane class of dyes, has long been used to control fungal and protozoan infections in fish. MG is easily absorbed by fish during waterborne exposure and is rapidly metabolized into leucomalachite green (LMG), which is known for its long residence time in edible fish tissue. This paper describes the development of an enzyme-linked immunosorbent assay (ELISA) for the detection and quantification of LMG in fish tissue. This development includes a simple and versatile method for the conversion of LMG to monodesmethyl-LMG, which is then conjugated to bovine serum albumin (BSA) to produce an immunogenic material. Rabbit polyclonal antibodies are generated against this immunogen, purified and used to develop a direct competitive enzyme-linked immunosorbent assay (ELISA) for the screening and quantification of LMG in fish tissue. The assay performed well, with a limit of detection (LOD) and limit of quantification (LOQ) of 0.1 and 0.3 ng g(-1) of fish tissue, respectively. The average extraction efficiency from a matrix of tilapia fillets was approximately 73% and the day-to-day reproducibility for these extractions in the assay was between 5 and 10%.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".