Evaluation of the Bioaccessibility of Metals and Metalloids in Eastern Canadian Mine Tailings Using an in Vitro Gastrointestinal Model, the Simulator of the Human Intestinal Microbial Ecology (Shime)
Bibliographic record
Abstract
MS1-03 Abstract: The ingestion of arsenic, mercury, and lead-contaminated soils is a potential risk to human health. This is especially true for members of residential communities that live in close proximity to the tailings of abandoned mines due to the high metal concentrations observed in these sites. Current practice in risk assessment is to assume that the bioaccessibility of ingested compounds are close to 100% or at least equal to the material used to derive the toxicological reference value. However, the validity of this practice has been questioned because recent work has demonstrated that there are large differences in percent bioaccessibility between soils. Tailing samples containing arsenic (385–105,300 ppm), mercury (6–5024 ppb), and lead (18–462 ppm), which were collected from 3 sites near abandoned mines in Eastern Canada, were digested in an in vitro gastrointestinal model, the Simulator of the Human Intestinal Microbial Ecosystem (SHIME). The SHIME is unique among in vitro models because it features a stage with a microbial community representative of that found in the human colon. The percent bioaccessibilities were measured for arsenic, mercury, and lead in the small intestine and colon stage of the SHIME after treatment with 2 size fractions (Bulk and <38 μm) of the tailings from each location. The effects of metal concentration and particle size on metal bioaccessibility were examined for each of the 3 analytes. Additionally, the effect of the colon microbe community on metal bioaccessibility was evaluated. Preliminary results suggest that metal bioaccessibility in the colon is lower than that of the small intestine due to the activity of sulfate-reducing bacteria. A precipitate was formed in the colon stage of the SHIME, thereby causing the SHIME solution to become black in color. This precipitate was not formed when sterilized (autoclaved) SHIME was added instead. Results indicate there may be a difference in bioaccessibility between the 2 size fractions examined, which may have been caused by the large differences in metal concentration between the 2 size fractions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".