Mammalian gastrointestinal tract parameters modulating the integrity, surface properties, and absorption of food‐relevant nanomaterials
Bibliographic record
Abstract
Many natural chemicals in food are in the nanometer size range, and the selective uptake of nutrients with nanoscale dimensions by the gastrointestinal (GI) tract is a normal physiological process. Novel engineered nanomaterials (NMs) can bring various benefits to food, e.g., enhancing nutrition. Assessing potential risks requires an understanding of the stability of these entities in the GI lumen, and an understanding of whether or not they can be absorbed and thus become systemically available. Data are emerging on the mammalian in vivo absorption of engineered NMs composed of chemicals with a range of properties, including metal, mineral, biochemical macromolecules, and lipid-based entities. In vitro and in silico fluid incubation data has also provided some evidence of changes in particle stability, aggregation, and surface properties following interaction with luminal factors present in the GI tract. The variables include physical forces, osmotic concentration, pH, digestive enzymes, other food, and endogenous biochemicals, and commensal microbes. Further research is required to fill remaining data gaps on the effects of these parameters on NM integrity, physicochemical properties, and GI absorption. Knowledge of the most influential luminal parameters will be essential when developing models of the GI tract to quantify the percent absorption of food-relevant engineered NMs for risk assessment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".