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Record W1879265019 · doi:10.1111/aji.12444

Transplacental Transfer of Interleukin‐1 Receptor Agonist and Antagonist Following Maternal Immune Activation

2015· article· en· W1879265019 on OpenAlexaff
Sylvie Girard, Guillaume Sébire

Bibliographic record

VenueAmerican Journal of Reproductive Immunology · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsMcGill University Health CentreUniversité de SherbrookeUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsAgonistTransplacentalInterleukin 1 receptor antagonistReceptor antagonistAntagonistEndocrinologyImmune systemReceptorInternal medicineMedicineChemistryPharmacologyImmunologyBiologyPregnancyFetusPlacenta

Abstract

fetched live from OpenAlex

PROBLEM: Prenatal exposure to inflammation increases the incidence of neonatal brain injury. This raise the question whether maternally produced cytokines, especially interleukin (IL)-1 elevated in pathological pregnancies and known to alter fetal development, can cross the placental barrier and affect the fetus directly. METHOD OF STUDY: We addressed if IL-1 agonist/antagonist could cross the placenta. RESULTS: Radiolabelled-IL-1 injected maternally reached the fetus in minimal amount. 3% of the amount detected within the placenta was transferred into the fetal liver and less than 1% recovered in the fetal brain 30 min after the injection Importantly, transfer of IL-1 was not affected by maternal exposure to LPS. Maternal administration of IL-1 receptor antagonist also reached the fetus in low concentration. CONCLUSIONS: This suggests that minimal amount of maternally produced IL-1 family members cross the placental barrier. Their negative effects are likely indirect, through their deleterious placental actions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.240
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

Citations34
Published2015
Admission routes1
Has abstractyes

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