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Record W4206838200 · doi:10.1126/scitranslmed.abj0473

DICAM promotes T <sub>H</sub> 17 lymphocyte trafficking across the blood-brain barrier during autoimmune neuroinflammation

2022· article· en· W4206838200 on OpenAlexafffund
Marc Charabati, Camille Grasmuck, Soufiane Ghannam, Lyne Bourbonnière, Antoine Fournier, Marc‐André Lécuyer, Olivier Tastet, Hania Kébir, Rose‐Marie Rébillard, Chloé Hoornaert, Elizabeth Gowing, Sandra Larouche, Olivier Fortin, Camille L. Pittet, Ali Filali‐Mouhim, Boaz Lahav, Robert Moumdjian, Alain Bouthillier, Marc Girard, Pierre Duquette, Romain Cayrol, Evelyn Peelen, Francisco J. Quintana, Jack P. Antel, Alexander Flügel, Catherine Larochelle, Nathalie Arbour, Stéphanie Zandee, Alexandre Prat

Bibliographic record

VenueScience Translational Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMontreal Neurological Institute and HospitalUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsNatalizumabMultiple sclerosisNeuroinflammationImmunologyBlood–brain barrierMonoclonal antibodyMedicineExperimental autoimmune encephalomyelitisImmune systemAntigenT cellAntibodyInflammationCentral nervous systemInternal medicine

Abstract

fetched live from OpenAlex

17 cell trafficking across the blood-brain barrier in vitro and in vivo, and alleviated disease symptoms in four distinct murine autoimmune encephalomyelitis models, including relapsing-remitting and progressive disease models. Collectively, our data highlight DICAM as a candidate therapeutic target to impede the migration of disease-inducing leukocytes into the CNS in both RRMS and PMS and suggest that blocking DICAM with a monoclonal antibody may be a promising therapeutic approach.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.311
Teacher spread0.282 · 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 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

Citations48
Published2022
Admission routes2
Has abstractyes

Explore more

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