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Record W3171019332 · doi:10.1016/j.isci.2021.102709

Nutritional and ecological perspectives of the interrelationships between diet and the gut microbiome in multiple sclerosis: Insights from marmosets

2021· review· en· W3171019332 on OpenAlexaff
María Elisa Pérez-Muñoz, Scott Sugden, Hermie J. M. Harmsen, Bert A. ‘t Hart, Jon D. Laman, Jens Walter

Bibliographic record

VenueiScience · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMarmosetMicrobiomeMultiple sclerosisGut microbiomeExperimental autoimmune encephalomyelitisBiologyImmune systemDiseaseImmunologyGut floraEcologyZoologyEvolutionary biologyBioinformaticsMedicinePathology

Abstract

fetched live from OpenAlex

Studies in experimental autoimmune encephalomyelitis (EAE), the animal model of multiple sclerosis, have shown potential links between diet components, microbiome composition, and modulation of immune responses. In this review, we reanalyze and discuss findings in an outbred marmoset EAE model in which a yogurt-based dietary supplement decreased disease frequency and severity. We show that although diet has detectable effects on the fecal microbiome, microbiome changes are more strongly associated with the EAE development. Using an ecological framework, we further show that the dominant factors influencing the gut microbiota were marmoset sibling pair and experimental time point. These findings emphasize challenges in assigning cause-and-effect relationships in studies of diet-microbiome-host interactions and differentiating the diet effects from other environmental, stochastic, and host-related factors. We advocate for animal experiments to be designed to allow causal inferences of the microbiota's role in pathology while considering the complex ecological processes that shape microbial communities.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.587
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.074
GPT teacher head0.305
Teacher spread0.231 · 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 designObservational
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

Citations13
Published2021
Admission routes1
Has abstractyes

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