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Record W1898477889 · doi:10.1196/annals.1423.025

CD44 Involvement in Autoimmune Inflammations

2007· review· en· W1898477889 on OpenAlexaff
David Naor, Shlomo Nedvetzki, Marita J. Walmsley, Avner Yayon, Eva A. Turley, Ira Golan, Dan Caspi, Lora Eshkar Sebban, Yehiel Zick, Tali Garin, Dimitrios Karussis, Nathalie Assayag-Asherie, Itamar Raz, Lola Weiss, Shimon Slavin, Itshak Golan

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsWestern University
Fundersnot available
KeywordsCD44ImmunologyExperimental autoimmune encephalomyelitisNOD miceInflammationKnockout mouseArthritisBiologyReceptorAutoimmunityMedicineCancer researchAntibodyCellGenetics

Abstract

fetched live from OpenAlex

CD44 is a multistructural and multifunctional glycoprotein, the diversity of which is generated by alternative splicing. In this communication we review some aspects related to CD44 structure and function in experimental autoimmune inflammation, focusing on research performed in our own laboratory. We have found that CD44 targeting by antibody, passively injected into DBA/1 mice with collagen-induced arthritis (CIA) and NOD mice with type I diabetes or actively generated by CD44 cDNA vaccination of SJL/j mice with autoimmune encephalomyelitis, markedly reduced the pathological manifestations of these diseases by attenuating cell migration of the inflammatory cells and/or by their apoptotic killing. However, genetic deletion of CD44 by knockout technology enhanced the development of CIA because of molecular redundancy mediated by RHAMM (a receptor of hyaluronan-mediated motility). The mechanisms that stand behind these findings are discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.200
GPT teacher head0.430
Teacher spread0.230 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations62
Published2007
Admission routes1
Has abstractyes

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