Impacts of graphene oxide contamination on a food web: Threats to somatic and reproductive health of organisms
Bibliographic record
Abstract
Contamination of aquatic food webs with nanomaterials poses a significant ecological and human health challenge. Ingestion of nanomaterials alongside food disrupts digestion and impairs physiological processes, with potential consequences for organism fitness and survival. Complex interactions between nanomaterials and biota further exacerbate the issue, influencing life-history strategies and ecosystem dynamics. Accumulation of nanomaterials within autotrophic and detritus-based food webs raises concerns about biomagnification, especially for top-level consumers and seafood-dependent human populations. Understanding the extent and impact of nanomaterial contamination on aquatic biota is crucial for effective mitigation strategies. To address this challenge, we conducted a comprehensive study evaluating the bioaccumulation effects of graphene oxide (GO), a commonly used nanomaterial, within an aquatic food chain. Using a gnotobiotic freshwater microcosm, we investigated the effects of micro- and nano-scale GO sheets on key organisms: green algae (Chlorella vulgaris), brine shrimp (Artemia salina), and zebrafish (Danio rerio). Two feeding regimes, direct ingestion and trophic transfer, were employed to assess GO uptake and transfer within the food web. Direct exposure involved individual organisms being exposed to either nano- or micro-scale GO sheets, while trophic transfer involved a sequential exposure pathway: algae exposed to GO sheets, artemias feeding on the algae, and zebrafish consuming the artemias. Our study provides critical insights into nanomaterial contamination in aquatic ecosystems. Physicochemical properties of GO sheets, including ζ-potential and dispersion, were influenced by salt culture media, resulting in aggregation under salt conditions. Microscopic imaging confirmed the bioaccumulation of GO sheets within organisms, indicating prolonged exposure and potential long-term effects. Notably, biodistribution analysis in zebrafish demonstrated the penetration of nano-sized GO into the intestinal wall, signifying direct interaction with vital organs. Exposure to GO resulted in increased zebrafish mortality and impaired reproductive performance, particularly through trophic transfer. These findings emphasize the urgent need to address nanomaterial contamination in aquatic food webs to protect ecosystem components and human consumers. Our study highlights the importance of developing effective mitigation strategies to preserve the integrity of aquatic ecosystems, ensure resource sustainability, and safeguard human well-being. In conclusion, our study provides crucial insights into the impact of nanomaterial pollution on aquatic biota. By recognizing the challenges posed by nanomaterial contamination and implementing targeted interventions, we can mitigate the adverse effects, preserving the integrity of aquatic ecosystems and safeguarding human health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".