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REVIEW ARTICLE: Toll‐Like Receptor Signaling and Pre‐Eclampsia

2009· review· en· W1523247704 on OpenAlex
Fang Xie, Stuart E. Turvey, Michelle A. Williams, Gil Mor, Peter von Dadelszen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Journal of Reproductive Immunology · 2009
Typereview
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsInnate immune systemEclampsiaBiologyImmunologyInflammationImmune systemSignal transductionReceptorToll-like receptorPathogen-associated molecular patternPattern recognition receptorCell biologyPregnancyGenetics

Abstract

fetched live from OpenAlex

Systemic inflammation and abnormal/poor placentation represent hallmarks of pre-eclampsia. Accumulating evidence suggests that infectious agents might increase the risk of pre-eclampsia; the innate immune defense mechanisms may interact with pro-inflammatory pathways, and contribute to the development of pre-eclampsia. The evidence for this has been supported by indirect epidemiologic and clinical studies, as well as by some direct support from experimental studies. Recent data directly implicate signaling by Toll-like receptors in the pathogenesis of pre-eclampsia, and establish a crucial link between pre-eclampsia and defense against both foreign pathogens and endogenously generated inflammatory ligands. Here, we review the rapid progress in this field, which has improved our understanding of the interplay between pathogen invasion, innate immune defense mechanisms, and pre-eclampsia.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.333
Teacher spread0.306 · 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