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Record W4403969398 · doi:10.1126/sciimmunol.adq9704

Kupffer cell reverse migration into the liver sinusoids mitigates neonatal sepsis and meningitis

2024· article· en· W4403969398 on OpenAlexaff
Bruna Araújo David, Jawairia Atif, Fernanda V. S. Castanheira, Tamanna Yasmin, Adrien Guillot, Yeni Ait Ahmed, Moritz Peiseler, Josefien W. Hommes, Lilian Salm, Marie‐Anne Bründler, Bas G. J. Surewaard, Wael Elhenawy, Sonya A. MacParland, Florent Ginhoux, Kathy D. McCoy, Paul Kubes

Bibliographic record

VenueScience Immunology · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsWomen and Children’s Health Research InstituteAlberta Medical AssociationUniversity Health NetworkUniversity of TorontoQueen's UniversityUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsSepsisMeningitisKupffer cellNeonatal meningitisNeonatal sepsisImmunologyMedicineBiologyPathologyMicrobiologySurgeryGeneEscherichia coli

Abstract

fetched live from OpenAlex

In adults, liver-resident macrophages, or Kupffer cells (KCs), reside in the sinusoids and sterilize circulating blood by capturing rapidly flowing microbes. We developed quantitative intravital imaging of 1-day-old mice combined with transcriptomics, genetic manipulation, and in vivo infection assays to interrogate increased susceptibility of newborns to bloodstream infections. Whereas 1-day-old KCs were better at catching Escherichia coli in vitro, we uncovered a critical 1-week window postpartum when KCs have limited access to blood and must translocate from liver parenchyma into the sinusoids. KC migration was independent of the microbiome but depended on macrophage migration inhibitory factor, its receptor CD74, and the adhesion molecule CD44. On the basis of our findings, we propose a model of progenitor macrophage seeding of the liver sinusoids via a reverse transmigration process from liver parenchyma. These results also illustrate the importance of developing newborn mouse models to understand newborn immunity and disease.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.330
Teacher spread0.312 · 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 designBench or experimental
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

Citations23
Published2024
Admission routes1
Has abstractyes

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