MétaCan
Menu
Back to cohort
Record W4403437483 · doi:10.1101/2024.10.14.618271

Farm exposure is associated with human breast milk immune profile and microbiome

2024· preprint· en· W4403437483 on OpenAlexaff
Mary Hannah Swaney, Olivia Steidl, Anastasia Tackett, Samantha Fye, Kristine E. Lee, Irene M. Ong, Casper G. Bendixsen, Gretchen Spicer, James Deline, James E. Gern, Christine M. Seroogy, Lindsay Kalan, Anne Marie Singh

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsMcMaster UniversityDiscovery Centre
Fundersnot available
KeywordsMicrobiomeImmune systemBreast milkHuman breast milkBiologyImmunologyFood scienceGeneticsBiochemistry

Abstract

fetched live from OpenAlex

Prenatal and early life farm exposure, and breastfeeding, are associated with protection from allergic diseases. We hypothesize that farm exposure influences the human breast milk microbiome and immune proteins. The immune protein profiles and microbial communities of 152 human breast milk samples were compared among three maternal farm exposure groups (traditional agrarian, farm, and non-farm) in rural Wisconsin to identify signatures associated with farm status and atopic disease. We found significant differences between farm groups for 23 immune proteins (p-adj<0.05), microbiome diversity (p=2.2E-05), and microbiome richness (p=8.0e-06). Traditional agrarian human breast milk had the highest immune protein levels and microbiome diversity and richness, followed by farm and non-farm human breast milk. Furthermore, Gram-positive bacterial species correlated with IL-23 mediated signaling events (p-adj<1.0E-05). These data suggest that increased farm exposures promotes human breast milk that is more microbially-diverse and rich in immune-associated proteins, ultimately influencing immune development in the infant.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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 designObservational
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicInfant Nutrition and HealthFrench-language works237,207