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Record W3137069002 · doi:10.3389/fonc.2021.656004

Interleukin-17 Receptor D in Physiology, Inflammation and Cancer

2021· review· en· W3137069002 on OpenAlexafffund
Charlotte Girondel, Sylvain Meloche

Bibliographic record

VenueFrontiers in Oncology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersCancer Research Society
KeywordsBiologySignal transductionReceptor tyrosine kinaseCarcinogenesisCancer researchCell biologyFibroblast growth factorInflammationReceptorCancerImmunologyGenetics

Abstract

fetched live from OpenAlex

Interleukin-17 receptor D (IL-17RD) is an evolutionarily conserved member of the IL-17 receptor family. Originally identified as a negative regulator of fibroblast growth factor (FGF) signaling under the name of Sef (Similar expression to FGF genes), IL-17RD was subsequently reported to regulate other receptor tyrosine kinase signaling pathways. In addition, recent studies have shown that IL-17RD also modulates IL-17 and Toll-like receptor (TLR) signaling. Combined genetic and cell biology studies have implicated IL-17RD in the control of cell proliferation and differentiation, cell survival, lineage specification, and inflammation. Accumulating evidence also suggest a role for IL-17RD in tumorigenesis. Expression of IL-17RD is down-regulated in various human cancers and recent work has shown that loss of IL-17RD promotes tumor formation in mice. However, the exact mechanisms underlying the tumor suppressor function of IL-17RD remain unclear and some studies have proposed that IL-17RD may exert pro-tumorigenic effects in certain contexts. Here, we provide an overview of the signaling functions of IL-17RD and review the evidence for its involvement in cancer.

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.027
GPT teacher head0.365
Teacher spread0.339 · 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

Citations19
Published2021
Admission routes2
Has abstractyes

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