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Record W2088309020 · doi:10.1007/s12079-012-0189-8

CCN6: a novel method of aTAKing cancer

2013· article· en· W2088309020 on OpenAlexaff
Andrew Leask

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineMatricellular proteinCancer researchCTGFCancerBreast cancerSMADProtein kinase BSignal transductionInternal medicineTransforming growth factorBiologyExtracellular matrixReceptorGrowth factor

Abstract

fetched live from OpenAlex

It is well-established that the expression of CCN family of matricellular proteins is altered in essentially all cancers and, hence, targeting these proteins may be a novel therapeutic approach to treating these diseases. For example, CCN6 (WISP3) is downregulated in aggressive breast cancers, and this phenomenon appears to result in the tumor survival by promoting Akt phosphorylation. In a recent report by Pal et al. (Cancer Res 72(18):4818-4828, 2012), CCN6 knockdown was shown to promote BMP4-mediated activation of the Smad-independent TAK1 and p38 kinases. CCN6 expression was inversely associated with BMP4 and phospho-p38 levels in 69 % of invasive breast carcinomas. TAK1 inhibition has been previously shown to decrease tumor progression in preclinical models of TAK1-dependent cancers. These data are consistent with the idea that CCN6 may represent a novel therapeutic approach, as compared to attacking TAK1 directly, to selectively target breast cancers.

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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.036
GPT teacher head0.347
Teacher spread0.311 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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