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Record W4396889572 · doi:10.1080/19490976.2024.2353394

Fecal transplant from vaginally seeded infants decreases intraabdominal adiposity in mice

2024· article· en· W4396889572 on OpenAlexafffund
Sivaranjani Namasivayam, Curtis Tilves, Hua Ding, Shaoguang Wu, Jada C. Domingue, Camilo A. Ruiz-Bedoya, Ankit Shah, Eric Bohrnsen, Benjamin Schwarz, Mickayla Bacorn, Qing Chen, Shira Levy, Maria Gloria Domínguez-Bello, Sanjay K. Jain, Cynthia L. Sears, Noel T. Mueller, Suchitra K. Hourigan

Bibliographic record

VenueGut Microbes · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Institutes of HealthNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institute of Allergy and Infectious DiseasesJohns Hopkins UniversityCanadian Institute for Advanced Research
KeywordsBiologyMicrobiomePhysiologyMetabolomeSeedingFecesLactobacillusEndocrinologyInternal medicineMedicineMicrobiologyMetaboliteBacteriaBioinformatics

Abstract

fetched live from OpenAlex

Exposing C-section infants to the maternal vaginal microbiome, coined "vaginal seeding", partially restores microbial colonization. However, whether vaginal seeding decreases metabolic disease risk is unknown. Therefore, we assessed the effect of vaginal seeding of human infants on adiposity in a murine model. Germ-free mice were colonized with transitional stool from human infants who received vaginal seeding or control (placebo) seeding in a double-blind randomized trial. There was a reduction in intraabdominal adipose tissue (IAAT) volume in male mice that received stool from vaginally seeded infants compared to control infants. Higher levels of isoleucine and lower levels of nucleic acid metabolites were observed in controls and correlated with increased IAAT. This suggests that early changes in the gut microbiome and metabolome caused by vaginal seeding have a positive impact on metabolic health.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.832

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 studies0.0000.000
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 head0.006
GPT teacher head0.243
Teacher spread0.238 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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