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Record W2611987469 · doi:10.1515/revneuro-2017-0001

Microbiota abnormalities and the therapeutic potential of probiotics in the treatment of mood disorders

2017· review· en· W2611987469 on OpenAlexaff
Adiel C. Rios, Pawan Kumar Maurya, Mariana Pedrini, Maiara Zeni‐Graiff, Elson Asevedo, Rodrigo B. Mansur, Andréa Wieck, Rodrigo Grassi‐Oliveira, Roger S. McIntyre, Mirian A.F. Hayashi, Elisa Brietzke

Bibliographic record

VenueReviews in the Neurosciences · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMood disordersMoodGut floraIntervention (counseling)Major depressive disorderPsychological interventionDiseaseMedicineBipolar disorderPsychiatryIntensive care medicinePsychologyClinical psychologyImmunologyAnxietyInternal medicine

Abstract

fetched live from OpenAlex

Major depressive disorder (MDD) and bipolar disorder (BD) are among the leading causes of burden and disability worldwide. Despite intensified research efforts to improve the treatment options and remission rates in mood disorders, no disease modifying treatment exists for these disorders. Accumulating evidence implicates the involvement of the gut microbiota in processes relevant to etiopathology of central nervous system-based disorders. The objective of this article was to critically evaluate the evidence supporting the link between gastrointestinal microbiota and mood disorders and to discuss the potential benefits of using probiotics in the treatment of MDD and BD. The concept of psychobiotics, which is bacterial-based interventions with mental health benefit, is emerging in the field. On the other hand, while probiotics might potentially represent a significant advance, specific roles of microbiota in the pathophysiology of mood disorders still need further investigation along with intervention studies.

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)0.002
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 score0.997
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.075
GPT teacher head0.374
Teacher spread0.299 · 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

Citations45
Published2017
Admission routes1
Has abstractyes

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