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Record W3109920867 · doi:10.3389/fcimb.2020.583418

The Fungal Microbiome and Asthma

2020· review· en· W3109920867 on OpenAlexafffund
Erik van Tilburg Bernardes, Mackenzie W. Gutierrez, Marie‐Claire Arrieta

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2020
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersCumming School of Medicine, University of CalgaryW. Garfield Weston FoundationAlberta Children's Hospital Research InstituteCanadian Lung AssociationSick Kids Foundation
KeywordsMicrobiomeAsthmaImmunologyEtiologyBiologyImmune systemHuman microbiomeMedicineBioinformaticsPathology

Abstract

fetched live from OpenAlex

Asthma is a group of inflammatory conditions that compromises the airways of a continuously increasing number of people around the globe. Its complex etiology comprises both genetic and environmental aspects, with the intestinal and lung microbiomes emerging as newly implicated factors that can drive and aggravate asthma. Longitudinal infant cohort studies combined with mechanistic studies in animal models have identified microbial signatures causally associated with subsequent asthma risk. The recent inclusion of fungi in human microbiome surveys has revealed that microbiome signatures associated with asthma risk are not limited to bacteria, and that fungi are also implicated in asthma development in susceptible individuals. In this review, we examine the unique properties of human-associated and environmental fungi, which confer them the ability to influence immune development and allergic responses. The important contribution of fungi to asthma development and exacerbations prompts for their inclusion in current and future asthma studies in humans and animal models.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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 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

Citations68
Published2020
Admission routes2
Has abstractyes

Explore more

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