Determination of the persistence of dimetridazole, metronidazole and ronidazole residues in black tiger shrimp (<i>Penaeus monodon</i>) tissue and their stability during cooking
Bibliographic record
Abstract
The depletion of three banned nitroimidazole drugs - dimetridazole (DMZ), metronidazole (MNZ) and ronidazole (RNZ) - was investigated in black tiger shrimp (Penaeus monodon) following in-water medication. The highest concentrations of residues were measured immediately after the 24-h immersion (d0). At this time, MNZ and MNZ-OH residues were measured in shrimp tissue samples at concentrations ranging from 361 to 4189 and from 0.28 to 6.6 μg kg(-1), respectively. DMZ and its metabolites HMMNI ranged in concentration between 31,509 and 37,780 and between 15.0 and 31.9 μg kg(-1), respectively. RNZ and HMMNI concentrations ranged from 14,530 to 24,206 and from 25.0 to 55 μg kg(-1), respectively. MNZ, DMZ and RNZ were the more persistent marker residues and can be detected for at least 8 days post-treatment. MNZ-OH was only detectable on d0 following treatment with MNZ. HMMNI residues were only detectable up to d1 (0.97-3.2 μg kg(-1)) or d2 (1.2-4.5 μg kg(-1)) following DMZ and RNZ treatment, respectively. The parent drugs MNZ, DMZ and RNZ were still measureable on d8 at 0.12-1.0, 40.5-55 and 8.8-18.7 μg kg(-1), respectively. The study also investigated the stability of nitroimidazole residues under various cooking procedures (frying, grilling, boiling, and boiling followed by microwaving). The experiments were carried out in shrimp muscle tissue containing both high and low concentrations of these residues. Different cooking procedures showed the impact on nitroimidazole residue concentration in shrimp tissue. Residue concentration depleted significantly, but partially, by boiling and/or microwaving, but the compounds were largely resistant to conventional grilling or frying. Cooking cannot therefore be considered as a safeguard against harmful nitroimidazole residues in shrimp.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".