In-vitro bioaccessibility and bioavailability of heavy metals in mineral clay complex used in natural health products
Bibliographic record
Abstract
Commercial mineral clays that claim to have healing properties are also known to contain trace amounts of heavy metals, albeit the risk of consuming many of them is not entirely known. The primary objective of this study was to evaluate the in vitro bioaccessibility and bioavailability of Arsenic (As), Cadmium (Cd) and Lead (Pb) in mineral clay samples collected from the Sierra Mountains (USA) using the Unified Bioaccessibility Research Group of Europe (UBM) method and the Caco-2 permeability assay, respectively. After UMB-gastric (UBM-G) digestion, As and Pb bioaccessibility were lower compared to Cd and decreased further in the UMB-gastrointestinal (UBM-GI) assay. Bioavailability estimates using the Caco-2 cell showed very low to non-detectable permeability for all 3 heavy metals. Thus, while initial heavy metal ranged from 3.8-17 ppm, 0.024-0.061ppm, and 5.8-20 ppm for As, Cd, and Pb, respectively, the bioavailability for these metals was reduced to very low levels that followed: non-detectable values of As, <0.007ppm of Cd, and <0.1ppm of Pb. Using UBM-digestion to mimic bioaccessibility, followed by Caco-2 cell bioavailability enabled us to conclude that in vitro assessment of heavy metal exposure associated with mineral clay-based natural health products does not pose a potential hazard to consumers.
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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".