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Record W2569098507 · doi:10.71781/29820

CXCR3 biased signaling, heteromerization and decoy properties

2015· dissertation· en· W2569098507 on OpenAlexfundno aff
François Guité‐Vinet

Bibliographic record

VenueOpen MIND · 2015
Typedissertation
Languageen
FieldMedicine
TopicChemokine receptors and signaling
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsDecoyBusinessMedicine

Abstract

fetched live from OpenAlex

Le récepteur de chimiokine CXCR3 est un récepteur couplé à la protéine G (RCPG) exprimé, entre autre, sur les cellules T activées lors d’une réponse immune. CXCR3 est activé par trois ligands inductibles par l’interféron-γ (CXCL9, 10, 11) et, plus récemment, il a été découvert que CXCL4 liait CXCR3. Nous savons que CXCR3 joue un rôle dans la chimiotaxie des leucocytes, mais peu d’attention a été portée sur la signalisation biaisée induite par ces quatre ligands. Alors que l’homodimérisation entre récepteurs de chimiokine est un concept grandement observé, l’hétéromérisation entre deux récepteurs reste un domaine de recherche active. La signalisation biaisée et l’hétéromérisation ont été testées grâce à la technique de bioluminescene resonance energy transfer (BRET) dans des cellules HEK293E. Nous présentons une caractérisation pharmacologique des quatre ligands de CXCR3 et démontrons l’hétéromérisation de CXCR3 avec CXCR4 et avec CXCR7. Nos résultats suggèrent que les ligands de CXCR3 n’agissent pas de manière redondante.

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 score0.006
Threshold uncertainty score0.018

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.084
GPT teacher head0.350
Teacher spread0.266 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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