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Record W2153242439 · doi:10.1002/pros.21198

Effects of castration on insulin levels and glucose tolerance in the mouse differ from those in man

2010· article· en· W2153242439 on OpenAlexaff
Takamitsu Inoue, Mahvash Zakikhani, Stéphanie David, Carolyn Algire, Marie‐José Blouin, Michaël Pollak

Bibliographic record

VenueThe Prostate · 2010
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsCastrationInternal medicineEndocrinologyInsulinMedicineBiologyHormone

Abstract

fetched live from OpenAlex

BACKGROUND: Plasma insulin concentration is increased in prostate cancer patients during androgen deprivation therapy (ADT) and hyperinsulinemia has been associated with aggressive prostate cancer behavior. To investigate the possible role of castration-induced hyperinsulinemia as a mechanism that may attenuate the beneficial effects of ADT in patients with prostate cancer, a murine model would be useful. We therefore investigated long-term metabolic effects of castration in several mouse models. METHODS: We studied the long-term influence of castration on energy intake, body weight, glucose tolerance, plasma-insulin, plasma insulin-like growth factor-1 (IGF-1), plasma adiponectin, and plasma leptin in C57BL/6, Swiss nu/nu, and CB17 scid mice receiving various diets. In each case, mice were randomized to have either bilateral orchiectomy or a sham operation. RESULTS: Energy intake, body weight, blood glucose levels in glucose tolerance test, plasma insulin, plasma IGF-1, and plasma leptin level in all had a trend to be decreased in castrated as compared to sham operated mice. Plasma adiponectin level was increased in the castrated mice. CONCLUSIONS: The effects of castration on glucose, insulin, and related markers in several mouse models studied does not coincide with clinical observations; further studies in this area will require clinical research and/or the use of alternate models such as the dog.

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 score0.619
Threshold uncertainty score0.174

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 head0.021
GPT teacher head0.304
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

Citations28
Published2010
Admission routes1
Has abstractyes

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