Reproductive effects of chronic dietary exposure to arsenic-contaminated oligochaetes in Zebrafish (Danio rerio)
Bibliographic record
Abstract
The reproductive consequences of environmentally relevant trophic exposure to arsenic in fish are poorly understood, despite diet being the major exposure source in many contaminated systems. This study investigated the reproductive effects of chronic dietary exposure to arsenic in adult Zebrafish via arsenic-contaminated oligochaete (Lumbriculus variegatus) worms. Worms pre-exposed to arsenite (0, 2.5, and 5.0 mg/L) for 14 days resulted in arsenic body burdens of 0.25 (Control), 34.69 (Low), and 77.79 (High) μg/g dry weight. Zebrafish were fed these worms at 3.5 % of body weight twice daily for 60 days. Reproductive endpoints assessed included gonadosomatic index (GSI), hepatosomatic index (HSI), fecundity, fertilization rate, and mating behaviour. Brain, liver, gonads, and blood plasma were assessed for gene expressions related to hypothalamus-pituitary-gonadal (HPG) axis genes, oxidative damage in hepatic and gonadal tissues, and to quantify the circulating 17β-estradiol (E2), 11-keto testosterone (11KT), and vitellogenin (VTG) levels in males and females. Sperm density and motility were assessed in males, and gonadal histopathology was conducted in both sexes. Dose-dependent reductions in GSI, HSI, fecundity, fertilization rate, and mating behaviour were recorded, along with oxidative damage in gonadal and liver tissues. Arsenic also reduced sperm quality and decreased the proportion of mature gametes in both ovaries and testes. Chronic exposure significantly altered HPG axis gene expression and disrupted circulating E2, 11-KT, and VTG levels in both sexes. Overall, these findings provide novel mechanistic insights into arsenic-induced reproductive toxicity in fish, which have important implications for understanding the consequences of arsenic contamination in aquatic ecosystems.
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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".