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Record W2790059318 · doi:10.1172/jci.insight.98410

Calnexin is necessary for T cell transmigration into the central nervous system

2018· article· en· W2790059318 on OpenAlexafffund
Joanna Jung, Paul Eggleton, Alison Robinson, Jessica Wang, Nick Gutowski, Janet E. Holley, Jia Newcombe, Elżbieta Dudek, Amber M. Paul, Douglas W. Zochodne, Allison Kraus, Christopher Power, Luis B. Agellon, Marek Michalak

Bibliographic record

VenueJCI Insight · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsCalnexinExperimental autoimmune encephalomyelitisNeuroinflammationMultiple sclerosisImmune systemInflammationMyelinImmunologyBlood–brain barrierCentral nervous systemBiologyEncephalomyelitisCell biologyNeuroscienceEndoplasmic reticulumCalreticulin

Abstract

fetched live from OpenAlex

In multiple sclerosis (MS), a demyelinating inflammatory disease of the CNS, and its animal model (experimental autoimmune encephalomyelitis; EAE), circulating immune cells gain access to the CNS across the blood-brain barrier to cause inflammation, myelin destruction, and neuronal damage. Here, we discovered that calnexin, an ER chaperone, is highly abundant in human brain endothelial cells of MS patients. Conversely, mice lacking calnexin exhibited resistance to EAE induction, no evidence of immune cell infiltration into the CNS, and no induction of inflammation markers within the CNS. Furthermore, calnexin deficiency in mice did not alter the development or function of the immune system. Instead, the loss of calnexin led to a defect in brain endothelial cell function that resulted in reduced T cell trafficking across the blood-brain barrier. These findings identify calnexin in brain endothelial cells as a potentially novel target for developing strategies aimed at managing or preventing the pathogenic cascade that drives neuroinflammation and destruction of the myelin sheath 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)0.000
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 score0.389
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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

Citations22
Published2018
Admission routes2
Has abstractyes

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