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Record W4293729210 · doi:10.1038/s41467-022-32569-6

On maternity and the stronger immune response in women

2022· article· en· W4293729210 on OpenAlexafffund
Evan Mitchell, Andrea L. Graham, Francisco Úbeda, Geoff Wild

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsTransmission (telecommunications)VirulenceImmune systemImmunocompetenceLimitingIncidence (geometry)BiologyHorizontal transmissionPathogenImmunologyDiseaseEvolutionary biologyMedicineGeneticsComputer scienceVirusTelecommunications

Abstract

fetched live from OpenAlex

Medical research reports that women often exhibit stronger immune responses than men, while pathogens tend to be more virulent in men. Current explanations cannot account for this pattern, creating an obstacle for our understanding of infectious-disease outcomes and the incidence of autoimmune diseases. We offer an alternative explanation that relies on a fundamental difference between the sexes: maternity and the opportunities it creates for transmission of pathogens from mother to child (vertical transmission). Our explanation relies on a mathematical model of the co-evolution of host immunocompetence and pathogen virulence. Here, we show that when there is sufficient vertical transmission co-evolution leads women to defend strongly against temperate pathogens and men to defend weakly against aggressive pathogens, in keeping with medical observations. From a more applied perspective, we argue that limiting vertical transmission of infections would alleviate the disproportionate incidence of autoimmune diseases in women over evolutionary time.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.186

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 science0.0000.001
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 head0.005
GPT teacher head0.265
Teacher spread0.260 · 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 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

Citations25
Published2022
Admission routes2
Has abstractyes

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