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Record W2792355265 · doi:10.1038/s41467-018-03186-z

EGFL7 reduces CNS inflammation in mouse

2018· article· en· W2792355265 on OpenAlexafffund
Catherine Larochelle, Timo Uphaus, Bieke Broux, Elizabeth Gowing, Magdalena Paterka, Laure Michel, Nevenka Dudvarski Stanković, Frank Bicker, Florent Lemaître, Alexandre Prat, Mirko H. H. Schmidt, Frauke Zipp

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health ResearchDeutsche Forschungsgemeinschaft
KeywordsExperimental autoimmune encephalomyelitisInflammationMultiple sclerosisEncephalomyelitisImmunologyImmune systemCell biologyBlood–brain barrierBiologyCentral nervous systemNeuroscience

Abstract

fetched live from OpenAlex

Extracellular matrix (ECM) proteins secreted by blood-brain barrier (BBB) endothelial cells (ECs) are implicated in cell trafficking. We discovered that the expression of ECM epidermal growth factor-like protein 7 (EGFL7) is increased in the CNS vasculature of patients with multiple sclerosis (MS), and in mice with experimental autoimmune encephalomyelitis (EAE). Perivascular CD4 T lymphocytes colocalize with ECM-bound EGFL7 in MS lesions. Human and mouse activated T cells upregulate EGFL7 ligand αvβ3 integrin and can adhere to EGFL7 through integrin αvβ3. EGFL7-knockout (KO) mice show earlier onset of EAE and increased brain and spinal cord parenchymal infiltration of T lymphocytes. Importantly, EC-restricted EGFL7-KO is associated with a similar EAE worsening. Finally, treatment with recombinant EGFL7 improves EAE, reduces MCAM expression, and tightens the BBB in mouse. Our data demonstrate that EGFL7 can limit CNS immune infiltration and may represent a novel therapeutic avenue in MS.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.382
Teacher spread0.344 · 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

Citations45
Published2018
Admission routes2
Has abstractyes

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