An investigation of the effect of gastrointestinal microbial activity on oral arsenic bioavailability
Bibliographic record
Abstract
In vitro gastrointestinal (GI) microbial activity in the colon compartment facilitates the arsenic release from soils into simulated GI fluids. Consequentially, it is possible that in vitro models that neglect to include microbial activity underestimate arsenic bioaccessibility when calculating oral exposure. However, the toxicological relevance of increased arsenic release due to microbial activity is contingent upon the subsequent absorption of arsenic solubilized in the GI lumen. The objectives of this research are to: (1) assess whether microbes in the in vitro small intestine affect arsenic solubilization from soils, (2) determine whether differences in the GI microbial community result in differences in the oral bioavailability of soil-borne arsenic. In vitro GI microbial activity in the distal small intestine increased arsenic release from soils; however, these effects were unlikely to be relevant since they were transient and demonstrated small effect sizes. In vivo arsenic absorption for juvenile swine was unaffected by antibiotic treatment. Therefore, it appears that microbial effects on arsenic release do not result in increased arsenic bioavailability. However, it remains to be seen whether the results for the limited set of soils described herein can be extrapolated to arsenic contaminated sites in general.
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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 | 0.001 | 0.001 |
| 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".