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Record W4392005399 · doi:10.1289/ehp12758

Evaluating the Effects of Chronic Oral Exposure to the Food Additive Silicon Dioxide on Oral Tolerance Induction and Food Sensitivities in Mice

2024· article· en· W4392005399 on OpenAlexafffund
Bruno Lamas, Natália Martins Breyner, Yann Malaisé, Mark Wulczynski, Heather J. Galipeau, Eric Gaultier, Christel Cartier, Elena F. Verdú, Éric Houdeau

Bibliographic record

VenueEnvironmental Health Perspectives · 2024
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health ResearchAgence Nationale de la RechercheMcMaster University
KeywordsFood additiveMedicineCarbon dioxideToxicologyFood scienceEnvironmental healthBiologyPhysiologyEcology

Abstract

fetched live from OpenAlex

BACKGROUND: The increasing prevalence of food sensitivities has been attributed to changes in gut microenvironment; however, ubiquitous environmental triggers such as inorganic nanoparticles (NPs) used as food additives have not been thoroughly investigated. OBJECTIVES: ) on intestinal immune response involved in oral tolerance (OT) induction and evaluated the consequences of oral chronic exposure to this food-additive using a mouse model of OT to ovalbumin (OVA) and on gluten immunopathology in mice expressing the celiac disease risk gene, HLA-DQ8. METHODS: or vehicle, were immunized with gluten and immunopathology was investigated. RESULTS: treatment. DISCUSSION: exposure and food sensitivities in humans. https://doi.org/10.1289/EHP12758.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.027
GPT teacher head0.317
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes2
Has abstractyes

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