Quinclorac (QCR) Herbicide Induces Hepatotoxicity, Cytotoxicity, and Oxidative Stress in Zebrafish Liver (ZF-L) Cells: An Evaluation of a Seized Formulation
Bibliographic record
Abstract
The seizure of agrochemicals is a persistent issue in Brazil, mainly in states bordering other South American countries, which experience the highest levels of agrochemical confiscations. We evaluated biochemical parameters, including oxidative stress, hepatotoxicity, and cytotoxicity of the herbicide quinclorac (QCR) using a sample obtained from a seizure. The herbicide was characterized using Gas Chromatography-Mass Spectrometry and thermal analysis. Additionally, hemolytic activity, along with MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), neutral red (NR), and lactate dehydrogenase (LDH) assays in Zebrafish Liver (ZF-L) cells, were performed to assess QCR cytotoxicity. The results showed IC50 values of 19.83 µg/mL for erythrocytes and 1.78 µg/mL, 5.13 µg/mL, and 0.95 µg/mL for MTT, NR, and LDH assays, respectively. A median IC50 of 3.46 µg/mL was used for cholinesterase (COL) and aspartate aminotransferase (AST) dosages, as well as oxidative stress assays, which revealed an increase in reactive oxygen species (ROS) production, along with a decrease in total sulfhydryl (SH) content and the activities of superoxide dismutase (SOD) and catalase (CAT). Furthermore, QCR exposure resulted in elevated AST levels, indicative of liver damage, and a biphasic response in COL activity, with a significant reduction at higher concentrations. Our research indicates that QCR induces hepatotoxicity and cytotoxicity via oxidative stress in the ZF-L cell line under in vitro conditions.
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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".