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Record W2395061074

HSP27 knockdown produces synergistic induction of apoptosis by HSP90 and kinase inhibitors in glioblastoma multiforme.

2014· article· en· W2395061074 on OpenAlexaff
Louiza Belkacemi, Matthew O. Hebb

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsWestern University
Fundersnot available
KeywordsStaurosporineHsp27Gene knockdownViability assayCancer researchApoptosisHeat shock proteinHsp90 inhibitorCell growthBiologyKinasePharmacologyChemistryProtein kinase CHsp90Cell biologyHsp70Biochemistry
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: The heat-shock proteins HSP27 and HSP90 perpetuate the malignant nature of glioblastoma multiforme (GBM) and offer promise as targets for novel cancer therapeutics. The present study sought to define synergistic antitumor benefits of concurrent HSP27-knockdown and the HSP90 inhibitor, 17-N-allylamino-17-demethoxygeldanamycin (17-AAG) or, comparatively, the non-selective kinase inhibitor, staurosporine, in GBM cells. MATERIALS AND METHODS: Dose-response relations were determined for 17-AAG and staurosporine in three GBM cell lines. HSP27-targeted siRNA was administered alone or in combination with subtherapeutic concentrations of each drug and cells were evaluated for viability, proliferation and apoptosis. RESULTS: Adjuvant HSP27 knockdown with 17-AAG or staurosporine produced marked and synergistic decrease in GBM cell viability and proliferation, with robust elevation of apoptotic fractions and caspase-3 activation. CONCLUSION: HSP27 knockdown confers potent chemosensitization of GBM cells. These novel data support the development of HSP-targeting strategies and, specifically, anti-HSP27 agents for the treatment of GBM.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.215
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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