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Record W2015339932 · doi:10.1089/jir.2006.26.281

Proteasome-Mediated Proteolysis of the Interleukin-10 Receptor Is Important for Signal Downregulation

2006· article· en· W2015339932 on OpenAlexaff
Sherry H.-Y. Wei, Andrew Ming‐Lum, Ying Liu, David Wallach, Christopher J. Ong, Stephen W. Chung, Kevin W. Moore, Alice Mui

Bibliographic record

VenueJournal of Interferon & Cytokine Research · 2006
Typearticle
Languageen
FieldMedicine
TopicCytokine Signaling Pathways and Interactions
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsInternalizationProteasomeCytokine receptorBiologyCell biologySignal transductionInterleukin-4 receptorCytokineProteolysisCommon gamma chainDownregulation and upregulationReceptorUbiquitinInterleukin 10ImmunologyBiochemistryGene

Abstract

fetched live from OpenAlex

The cytokine interleukin-10 (IL-10) is an important regulator of immune cell function, proliferation, and survival. The IL-10 receptor (IL-10R) consists of two subunits, IL-10R1 and IL-10R2, both belonging to the class II cytokine receptor superfamily. Like other members of the cytokine receptor superfamily, IL-10R stimulation leads to activation of Jak family kinases and Stat transcription factors. To identify additional signal transduction pathways used by the IL-10R, we purified 92-kDa and 100-kDa proteins that coprecipitated with IL-10R1 from IL-10-stimulated cells. Both proteins were found to be related to the 97-kDa subunit of the regulatory component of the 26S proteasome. Subsequent studies confirmed that the IL-10R1 undergoes ligand- dependent internalization and proteasome-mediated degradation. An IL-10R1 cytoplasmic domain mutant deficient for internalization exhibited prolonged signaling through Jak1 and Stat3, reinforcing the importance of receptor internalization for signal termination.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.002

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 head0.051
GPT teacher head0.356
Teacher spread0.305 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

Explore more

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