MétaCan
Menu
Back to cohort
Record W2130441903 · doi:10.1158/1535-7163.mct-08-0891

The phytochemical piceatannol induces the loss of CBL and CBL-associated proteins

2009· article· en· W2130441903 on OpenAlexafffund
Alexander C. Klimowicz, Sabine A. Bisson, Karm Hans, Elizabeth Long, Henrik C. Hansen, Stephen M. Robbins

Bibliographic record

VenueMolecular Cancer Therapeutics · 2009
Typearticle
Languageen
FieldMedicine
TopicSirtuins and Resveratrol in Medicine
Canadian institutionsUniversity of CalgaryAlberta Cancer Foundation
FundersUniversity of Calgary
KeywordsPiceatannolPhytochemicalChemistryBiologyBiochemistryResveratrol

Abstract

fetched live from OpenAlex

Piceatannol is a naturally occurring bioactive stilbene with documented antileukemic properties. It has been extensively used as a Syk-selective protein tyrosine kinase inhibitor for the study of various signaling pathways. Herein, we show that the hydroxystilbene, piceatannol, and related catechol ring-containing compounds are able to induce the loss of the Cbl family of proteins. Normal cellular Cbl-regulatory mechanisms were not involved in this process. Screening of a small library of piceatannol-like compounds indicated that aromaticity and a catechol ring were required for the induction of Cbl loss. Further examination of these two chemical properties showed that the oxidative conversion of the catechol ring of piceatannol into a highly reactive O-benzoquinone was the cause of piceatannol-induced Cbl loss. Characterization of the Cbl selectivity of piceatannol-induced protein loss revealed that this compound was also able to induce the functional loss of specific Cbl-associated proteins involved in signaling pathways commonly associated with cancer. This work uncovers a new, piceatannol-dependent effect and shows a novel way in which this phenomenon can be exploited to inhibit disease-associated signaling pathways.

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 score0.054
Threshold uncertainty score0.307

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 head0.018
GPT teacher head0.302
Teacher spread0.283 · 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

Citations12
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueMolecular Cancer TherapeuticsSame topicSirtuins and Resveratrol in MedicineFrench-language works237,207