Nanoparticles in the Food Industry and Their Impact on Human Gut Microbiome and Diseases
Bibliographic record
Abstract
The use of inorganic nanoparticles (NPs) has expanded into various industries including food manufacturing, agriculture, cosmetics, and construction. This has allowed NPs access to the human gastrointestinal tract, yet little is known about how they may impact human health. As the gut microbiome continues to be increasingly implicated in various diseases of unknown etiology, researchers have begun studying the potentially toxic effects of these NPs on the gut microbiome. Unfortunately, conflicting results have limited researcher's ability to evaluate the true impact of NPs on the gut microbiome in relation to health. This review focuses on the impact of five inorganic NPs (silver, iron oxide, zinc oxide, titanium dioxide, and silicon dioxide) on the gut microbiome and gastrointestinal tract with consideration for various methodological differences within the literature. This is important as NP-induced changes to the gut could lead to various gut-related diseases. These include irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), celiac disease, and colorectal cancer. Research in this area is necessary as the use of NPs in various industries continues to grow along with the number of people suffering from chronic gastrointestinal diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".