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Record W2132403240 · doi:10.1677/joe.0.1710109

Co-administration of finasteride and the pure anti-oestrogen ICI 182,780 act synergistically in modulating the IGF system in rat prostate

2001· article· en· W2132403240 on OpenAlexaff
H. Huynh, Laurence I. Alpert, MA Alaoui-Jamali, CY Ng, TW Chan

Bibliographic record

VenueJournal of Endocrinology · 2001
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsMcGill UniversityJewish General Hospital
FundersBiomedical Research CouncilMedical Research CouncilAstraZeneca
KeywordsFinasterideProstateEndocrinologyProstate cancerInternal medicineProtein kinase BMedicineCancer researchApoptosisCancerBiology

Abstract

fetched live from OpenAlex

Prostate cancer is the most diagnosed invasive malignancy in males. Androgens and oestrogens have been implicated in the pathogenesis of prostate cancer. We report herein that the pure anti-oestrogen ICI 182,780 (ICI) reduces Ki-67 labelling index and IGF-I receptor levels in rat prostate. Increase of IGF-I mRNA and IGF-binding protein 3 (IGFBP-3) accumulation occur without any effect on prostate weight. Finasteride significantly decreases prostate weight and inhibits IGF-I gene expression. IGFBP-3 mRNA, Akt and phospho-Akt are not affected by finasteride. Co-administration of ICI plus finasteride reduces prostate weight by approximately 50% and causes acinar dilation with decreased luminal epithelial cell thickness. The acinar epithelial cells became atrophic and inactive with minimal cytoplasm. We also demonstrate a synergistic effect of ICI and finasteride on induction of IGFBP-3 accumulation and inhibition of Akt phosphorylation. Because the IGF and IGFBP-3 system plays an important role in prostate epithelial cell proliferation, apoptosis and tumour progression, the inhibitory effects of finasteride and ICI on IGF system may contribute to their anti-proliferative activity. These observations support a potential use of ICI in conjunction with finasteride in the prevention and/or treatment of prostate cancer.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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 designObservational
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

Citations30
Published2001
Admission routes1
Has abstractyes

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