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One more role for the gut: microbiota and blood brain barrier.

2016· article· en· W1761622267 on OpenAlexaff
Laure Michel, Alexandre Prat

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGut floraGut–brain axisImmune systemBiologyMultiple sclerosisCentral nervous systemNeuroscienceSynaptogenesisBlood–brain barrierImmunologyNeurotrophic factorsPhysiology

Abstract

fetched live from OpenAlex

The gut microbiota is composed of trillions of microbes that perform several tasks which are essential to our physiology. Recent emerging evidences have suggested the important contribution of gut microbiota in several biological functions of mamals, such as the regulation of the immune system, metabolism, intestinal development or brain physiology (1-4). In fact, recent work, mainly performed in experimental model of Multiple Sclerosis (MS), have demonstrated that resident commensal microbiota can modulate central nervous system (CNS) autoimmunity (5-8). The microbiota is now known to shift the balance between protective and pathogenic immune responses, in the CNS, but also in other organs. A growing body of evidence in animal support also the concept that the gut microbiota influences emotional behavior (9,10) and that its products and metabolites may promote metabolic effects such as reduced body weight, reduced adiposity, and improved glucose control (11). As regards, CNS physiology, the gut microbiota influence synaptogenesis, regulate neurotransmitters and neurotrophic factors release and function (4).

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.004
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0280.003

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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
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

Citations56
Published2016
Admission routes1
Has abstractyes

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