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Record W3032944184

Phosphodiesterase inhibition as an approach to increase chemosensitivity of prostate cancer

2008· article· en· W3032944184 on OpenAlexaff
Geneviève C. Paré, Jenny Jun, Klodiana Gjoncaj, Erin Bell, Nianping Hu, Charles H. Graham, D. Robert Siemens

Bibliographic record

VenueCancer Research · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhosphodiesterase function and regulation
Canadian institutionsQueen's University
Fundersnot available
KeywordsDU145Clonogenic assayProstate cancerZaprinastHypoxia (environmental)PhosphodiesteraseCancer researchCancer cellPharmacologyCancerCell cultureBiologyChemistryMedicineInternal medicineEnzymeBiochemistryLNCaP
DOInot available

Abstract

fetched live from OpenAlex

691 Purpose: Low tumour oxygenation (hypoxia) correlates with resistance to chemotherapeutic agents. We have recently provided evidence that hypoxia-induced resistance to anti-cancer drugs can be decreased by stimulating nitric oxide (NO) signalling through a cGMP-mediated pathway. An alternative approach to increase the activity of this signalling pathway is to prevent the breakdown of cGMP by phosphodiesterases (PDEs). The aim of this study was to determine whether PDE inhibitors are capable of attenuating hypoxia-induced chemoresistance in prostate carcinoma cells.
 Materials and Methods: Western blots, immunohistochemistry and functional enzymatic assays were used to determine the expression of PDEs in human cell lines and human prostate cancer samples. Drug sensitivity assays of cell lines exposed to hypoxic or standard conditions were performed in the presence of various concentrations of the PDE-5-specific inhibitor zaprinast.
 Results: These studies revealed the presence of two of the multiple cGMP-specific PDEs (PDE5 and PDE11A) in both DU145 and PC-3 cell lines and in prostate cancer tissue. Clonogenic assays revealed that incubation of DU145 in 0.5% O2 for 24 hours resulted in a corresponding 4-fold increase (P

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 score0.007
Threshold uncertainty score0.462

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 head0.049
GPT teacher head0.356
Teacher spread0.307 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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