Estimating the bioaccessibility of trace elements in sediment to fish: a review of in vitro experimental approaches
Bibliographic record
Abstract
Human activities dominate the geochemical cycles of many trace elements (TEs), with broad implications for the health of aquatic systems worldwide. In particular, industrial discharges, agricultural runoff, and urban effluents have significantly altered the natural biogeochemical distribution of TEs in freshwater and marine environments. These anthropogenic inputs contribute to the accumulation of TEs in sediments, which serve as both a sink and a potential secondary source of contamination. Aquatic organisms are exposed to TEs primarily through diet, including the ingestion of contaminated sediments. While total metal concentrations in sediments have been widely studied, their bioaccessibility and bioavailability received less attention. In vitro experimental approaches that simulate digestion provide valuable insights into the bioaccessibility of TEs and their potential assimilation by aquatic organisms. This review synthesizes knowledge from existing studies to improve our understanding of TE exposure via dietary intake in fish, providing insights into the environmental impacts of TEs, and identifying implications for ecosystem health assessments. We highlight existing in vitro methodologies used to estimate the bioaccessibility of TEs within the gastrointestinal tract of fish, focusing on the ingestion of contaminated sediments. The study identifies key physiological factors influencing TE bioaccessibility, including the composition of gastrointestinal fluids, reaction time, solid-to-fluid ratios, agitation, and temperature. The review also describes sediment characteristics, such as mineral composition, particle size and organic matter content. Blanks values are presented for most of the TEs of contemporary environmental relevance in synthetic gastric fluid (SGF), to illustrate the importance of metal free, ultraclean analytical procedures and protocols.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".