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Record W2171332001 · doi:10.1189/jlb.71.5.871

The expression pattern of the ITIM-bearing lectin CLECSF6 in neutrophils suggests a key role in the control of inflammation

2002· article· en· W2171332001 on OpenAlexafffund
Manon Richard, Patricia Veilleux, Michèle Rouleau, Robert Paquin, André Beaulieu

Bibliographic record

VenueJournal of Leukocyte Biology · 2002
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsCentre hospitalier de l'Université Laval
FundersCanadian Institutes of Health Research
KeywordsBiologyInflammationSignal transductionMessenger RNACell biologyReceptorGene expressionMolecular biologyImmunologyGeneBiochemistry

Abstract

fetched live from OpenAlex

In our study of the modulation of the expression of inflammation-related genes in neutrophils, we have found a gene called CLECSF6 (C-type lectin superfamily 6). CLECSF6 expresses two mRNA species at low levels in resting neutrophils. Here, we describe for the first time the sequence of the short mRNA version. It lacks amino acids that are likely to affect the functionality of its protein product. GM-CSF, IL-3, IL-4, and IL-13 caused an accumulation of the short CLECSF6 mRNA in neutrophils. The surface expression of the CLECSF6 protein was reduced by TNF-alpha, IL-1alpha, LPS, and Matrigel. CLECSF6 bears the immunoreceptor tyrosine-based inhibition motif (ITIM) involved in signal transduction resulting in the inhibition of leukocyte activation. We propose that some neutrophil activators modulate the expression of CLECSF6 at the mRNA (GM-CSF, IL-3, IL-4, and IL-13) or protein (TNF-alpha, IL-1alpha, LPS, and Matrigel) levels in ways that block ITIM-based transduction of anti-inflammatory signals and therefore promote inflammation.

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 score0.001
Threshold uncertainty score0.004

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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

Citations34
Published2002
Admission routes2
Has abstractyes

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