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Record W2085416352 · doi:10.1097/mog.0b013e3283572ffa

On communication between gut microbes and the brain

2012· review· en· W2085416352 on OpenAlexaff
Paul Forsythe, Wolfgang Kunze, John Bienenstock

Bibliographic record

VenueCurrent Opinion in Gastroenterology · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsGut floraGut–brain axisNeuroscienceCentral nervous systemBiologyHypothalamusVagus nerveImmunology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Interest in the microbiota-gut-brain axis is increasing apace and what was, not so long ago, a hypothetical relationship is emerging as a potentially critical factor in the regulation of intestinal and mental health. Studies are now addressing the neural circuitry and mechanisms underlying the influence of gut bacteria on the central nervous system and behavior. RECENT FINDINGS: Gut bacteria influence development of the central nervous systems (CNS) and stress responses. In adult animals, the overall composition of the microbiota or exposure to specific bacterial strains can modulate neural function, peripherally and centrally. Gut bacteria can provide protection from the central effects of infection and inflammation as well as modulate normal behavioral responses. Behavioral effects described to date are largely related to stress and anxiety and an altered hypothalamus-pituitary-adrenal axis response is a common observation in many model systems. The vagus nerve has also emerged as an important means of communicating signals from gut microbes to the CNS. SUMMARY: Studies of microbiota-gut-brain communication are providing us with a deeper understanding of the relationship between the gut bacteria and their hosts while also suggesting the potential for microbial-based therapeutic strategies that may aid in the treatment of mood disorders.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.962
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.072
GPT teacher head0.385
Teacher spread0.313 · 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.

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

Citations136
Published2012
Admission routes1
Has abstractyes

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