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Record W2162641376 · doi:10.1189/jlb.69.6.881

Agonistic and antagonistic activities of chemokines

2001· article· en· W2162641376 on OpenAlexafffund
Pius Loetscher, Ian Clark‐Lewis

Bibliographic record

VenueJournal of Leukocyte Biology · 2001
Typearticle
Languageen
FieldMedicine
TopicChemokine receptors and signaling
Canadian institutionsUniversity of British Columbia
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungArthritis Society
KeywordsBiologyHoming (biology)ChemokineReceptorCell biologyAgonistic behaviourChemokine receptorImmune systemInflammationImmunologyBiochemistryEcology

Abstract

fetched live from OpenAlex

Since the discovery of interleukin-8, about 50 chemokines have been identified and characterized. Originally, they were considered as inducible mediators of inflammation, but in recent years, several chemokines were identified that are expressed constitutively and function in physiological traffic and homing of leukocyte-lymphocytes in particular. All chemokines act via seven-transmembrane domain, G protein-coupled receptors. Eighteen such receptors have been identified so far. Studies on structure-activity relationships indicate that chemokines have two main sites of interaction with their receptors, the flexible NH2-terminal region and the conformationally rigid loop that follows the second cysteine. Chemokines are thought to dock onto receptors by means of the loop region, and this contact is believed to facilitate the binding of the NH2-terminal region that results in receptor activation. These studies have also highlighted the importance of the NH2-terminal region for agonistic and antagonistic activity. Recently, we have shown that some naturally occurring chemokines can function as receptor antagonists. These observations suggest a new mechanism for the regulation of leukocyte recruitment during inflammatory and immune reactions, which are based on the combination of agonistic and antagonistic effects.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.277

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 head0.016
GPT teacher head0.297
Teacher spread0.280 · 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

Citations91
Published2001
Admission routes2
Has abstractyes

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