MétaCan
Menu
Back to cohort
Record W2958575744 · doi:10.3389/fimmu.2019.01802

Diet-Microbe-Host Interactions That Affect Gut Mucosal Integrity and Infection Resistance

2019· review· en· W2958575744 on OpenAlexaff
Andrew J. Forgie, Janelle M. Fouhse, Benjamin P. Willing

Bibliographic record

VenueFrontiers in Immunology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsColonisation resistanceDysbiosisMicrobiomeBiologyPathogenDiseaseImmune systemHost (biology)Innate immune systemImmunologyGut floraAntibiotic resistanceImmunityPopulationMicrobiologyGastrointestinal tractAntibioticsMedicineBioinformaticsEnvironmental healthEcology

Abstract

fetched live from OpenAlex

The gastrointestinal tract microbiome plays a critical role in regulating host innate and adaptive immune responses against pathogenic bacteria. Disease associated dysbiosis and environmental induced insults, such as antibiotic treatments can lead to increased susceptibility to infection, particularly in a hospital setting. Dietary intervention is the greatest tool available to modify the microbiome and support pathogen resistance. Some dietary components can maintain a healthy disease resistant microbiome, whereas others can contribute to an imbalanced microbial population, impairing intestinal barrier function and immunity. Characterizing the effects of dietary components through the host-microbe axis as it relates to gastrointestinal health is vital to provide evidence-based dietary interventions to mitigate infections. This review will cover the effect of dietary components (carbohydrates, fiber, proteins, fats, polyphenolic compounds, vitamins, and minerals) on intestinal integrity and highlight their ability to modulate host-microbe interactions as to improve pathogen resistance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.026
GPT teacher head0.317
Teacher spread0.291 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations82
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in ImmunologySame topicGut microbiota and healthFrench-language works237,207