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Record W4288063793 · doi:10.1063/5.0094814

Impact of gut microbiome on anxiety

2022· article· en· W4288063793 on OpenAlexaff
Hongyu Xiao

Bibliographic record

VenueAIP conference proceedings · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMicrobiomeGut microbiomeAnxietyBiologyMedicineBioinformaticsPsychiatry

Abstract

fetched live from OpenAlex

The gastrointestinal microbiome plays a crucial role in maintaining an individual’s health. Although the gastrointestinal microbiome is commonly perceived as facilitating gastrointestinal functions including digestion, current studies demonstrated that it is able to influence one’s health in the neurological aspect. Emerging findings show that the gut microbiome is highly correlated with anxiety. Here, this review aims to discuss the interaction between the gut microbiome and anxiety. This review shows the significant role of the gut microbiome in gut-brain communication. To better understand how the gut microbiome influences anxiety, this paper discusses the experimental results of the gut microbiome impacting anxiety behaviourally and neurochemically. In addition, several potential pathways between the gut microbiome and the brain are reviewed to investigate the possible mechanisms behind microbiome-anxiety interaction. These studies together may be crucial for future treatments of anxiety disorders via intervening in the gut microbiome.

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: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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