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Record W2797435353 · doi:10.4049/jimmunol.1701776

The Impact of Maternal Microbes and Microbial Colonization in Early Life on Hematopoiesis

2018· review· en· W2797435353 on OpenAlexaff
Kathy D. McCoy, Carolyn A. Thomson

Bibliographic record

VenueThe Journal of Immunology · 2018
Typereview
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImmune systemBiologyColonizationMicrobiomeImmunologyMicrobiologyGenetics

Abstract

fetched live from OpenAlex

All body surfaces are colonized by microbes, which occurs through a dynamic process over the first few years of life. Initial colonizing microbes are transferred from the maternal microbiota to the newborn through vertical transmission. Postnatal maturation of the immune system is heavily influenced by these microbes, particularly during early life. Although microbial-mediated education of the immune system is better understood at mucosal sites, recent data indicate that the systemic immune system is also shaped by the microbiota. Bacterial products and metabolites produced through microbial metabolism can reach distal sites, and metabolites derived from the maternal microbiota can cross the placenta and are present in milk. Recent studies show that the microbiota can even influence immune development in primary lymphoid organs like the bone marrow. This review outlines our current knowledge of how the microbiota can impact hematopoiesis, with a focus on the effects of maternal and early-life microbiota.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.405
Teacher spread0.353 · 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 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

Citations30
Published2018
Admission routes1
Has abstractyes

Explore more

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