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Record W4400585857 · doi:10.1038/s41598-024-64313-z

Alcohol consumption during pregnancy differentially affects the fecal microbiota of dams and offspring

2024· article· en· W4400585857 on OpenAlexafffund
Tamara S. Bodnar, Garrett Ainsworth-Cruickshank, Vincent Billy, Laura Wegener Parfrey, Joanne Weinberg, Charlis Raineki

Bibliographic record

VenueScientific Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsBrock UniversityUniversity of CalgaryUniversity of British Columbia
FundersNational Institute on Alcohol Abuse and AlcoholismNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthUniversities Space Research Association
KeywordsOffspringPregnancyGut floraBiologyAlcohol consumptionPhysiologyMicrobiomeAlcoholDysbiosisFecesMedicineImmunologyBioinformaticsEcologyGeneticsBiochemistry

Abstract

fetched live from OpenAlex

Microbiota imbalances are linked to inflammation and disease, as well as neurodevelopmental conditions where they may contribute to behavioral, physiological, and central nervous system dysfunction. By contrast, the role of the microbiota in Fetal Alcohol Spectrum Disorder (FASD), the group of neurodevelopmental conditions that can occur following prenatal alcohol exposure (PAE), has not received similar attention. Here we utilized a rodent model of alcohol consumption during pregnancy to characterize the impact of alcohol on the microbiota of dam-offspring dyads. Overall, bacterial diversity decreased in alcohol-consuming dams and community composition differed from that of controls in alcohol-consuming dams and their offspring. Bacterial taxa and predicted biochemical pathway composition were also altered with alcohol consumption/exposure; however, there was minimal overlap between the changes in dams and offspring. These findings illuminate the potential importance of the microbiota in the pathophysiology of FASD and support investigation into novel microbiota-based interventions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.268
Teacher spread0.253 · 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 designBench or experimental
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

Citations12
Published2024
Admission routes2
Has abstractyes

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