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Record W4225080916 · doi:10.1038/s41598-022-10869-7

Comprehensive human amniotic fluid metagenomics supports the sterile womb hypothesis

2022· article· en· W4225080916 on OpenAlexafffund
HanChen Wang, Gui Xiang Yang, Yuxiang Hu, Patricia Lam, Karan Sangha, Dawn Siciliano, Anne Swenerton, Ruth R. Miller, Peter Tilley, Peter von Dadelszen, Shirin Kalyan, Patrick Tang, Millan S. Patel

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsBC Children's HospitalBC Centre for Disease ControlAcuitas Therapeutics (Canada)McGill UniversityUniversity of British Columbia
FundersCanadian Institutes of Health ResearchBC Children's HospitalChildren's Hospital Foundation
KeywordsMetagenomicsAmniotic fluidBiologyObstetricsBioinformaticsComputational biologyPregnancyMedicineFetusGeneticsGene

Abstract

fetched live from OpenAlex

As metagenomic approaches for detecting infectious agents have improved, each tissue that was once thought to be sterile has been found to harbor a variety of microorganisms. Controversy still exists over the status of amniotic fluid, which is part of an immunologically privileged zone that is required to prevent maternal immune system rejection of the fetus. Due to this privilege, the exclusion of microbes has been proposed to be mandatory, leading to the sterile womb hypothesis. Since nucleic acid yields from amniotic fluid are very low, contaminating nucleic acid found in water, reagents and the laboratory environment frequently confound attempts to address this hypothesis. Here we present metagenomic criteria for microorganism detection and a metagenomic method able to be performed with small volumes of starting material, while controlling for exogenous contamination, to circumvent these and other pitfalls. We use this method to show that human mid-gestational amniotic fluid has no detectable virome or microbiome, supporting the sterile womb hypothesis.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.039
GPT teacher head0.268
Teacher spread0.229 · 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

Citations24
Published2022
Admission routes2
Has abstractyes

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