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Record W2343258574 · doi:10.1080/19490976.2016.1182293

A humanized microbiota mouse model of ovalbumin-induced lung inflammation

2016· article· en· W2343258574 on OpenAlexafffund
Marie‐Claire Arrieta, Manish Sadarangani, Eric Brown, Shannon Russell, Michael Nimmo, John Mark Dean, Stuart E. Turvey, Edmond S. Chan, B. Brett Finlay

Bibliographic record

VenueGut Microbes · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsChild and Family Research InstituteMichael Smith Health Research BCBC Children's HospitalVancouver Infectious Diseases CentreCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsBiologyImmunologyHumanized mouseVeillonellaGut floraImmune systemAsthmaOvalbuminInflammationHygiene hypothesisBacteriaStreptococcusGenetics

Abstract

fetched live from OpenAlex

There is increasing evidence for a role of early life gut microbiota in later development of asthma in children. In our recent study, children with reduced abundance of the bacterial genera Lachnospira, Veillonella, Faecalibacterium, and Rothia had an increased risk of development of asthma and addition of these bacteria in a humanized mouse model reduced airway inflammation. In this Addendum, we provide additional data on the use of a humanized gut microbiota mouse model to study the development of asthma in children, highlighting the differences in immune development between germ-free mice colonized with human microbes compared to those colonized with mouse gut microbiota. We also demonstrate that there is no association between the composition of the gut microbiota in older children and the diagnosis of asthma, further suggesting the importance of the gut microbiota-immune system axis in the first 3 months of life.

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)0.001
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.014
GPT teacher head0.253
Teacher spread0.238 · 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

Citations48
Published2016
Admission routes2
Has abstractyes

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