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Record W2267738257 · doi:10.4236/jct.2016.71005

Non Selective Inhibition of COX Activity Reversed Inflammation and Reactive Oxygen Radicals Mediated Prostate Cancer Risk and Decreased Disease Progression in Preclinical Model

2016· article· en· W2267738257 on OpenAlexaff
Maxwell Omabe, Kenneth Omabe, Clement Ademola Famurewa, Alberta Egwu Okorocha, Grace Maxwell Omabe

Bibliographic record

VenueJournal of Cancer Therapy · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsProstate cancerOxidative stressMedicineProstateAspirinLipopolysaccharideInflammationReactive oxygen speciesPharmacologyIn vivoCyclooxygenaseCancerInternal medicineChemistryBiochemistryEnzymeBiology

Abstract

fetched live from OpenAlex

Prostate cancer (PCa) represents the most frequent urologic diagnosis in elderly males. We have previously shown that exposure of prostate to lipopolysaccharide (LPS) promotes cancer risk. We investigated the effect of non-selective cyclooxygenase (COX) inhibition on prostate inflammation-mediated cancer risk in vivo. The prostates of male rats were inoculated with E. coli as sources of inflammatory molecules (LPS) and were treated with COX inhibitor, aspirin 2 mg/Kg orally for 14 days or PBS. Oxidative stress was induced with two 2 mls of hydrogen peroxide orally twice daily or PBS for 14 days; they were either treated with COX inhibitor or PBS for another 14 days. Blood was collected and analyzed for acid phosphatase and PSA. Data showed presences of LPS in the prostate of the rats resulted in gradual increase in PSA when compared to control (P P 2O2 had 2.5 fold increase in acid phosphatase (ACP) compared control (P P ity in rats.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.327

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 head0.012
GPT teacher head0.319
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
Published2016
Admission routes1
Has abstractyes

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