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Record W1977485952 · doi:10.1159/000125947

Effects of Acute and Prolonged Glucose Excess on Growth Hormone Release by Cultured Rat Anterior Pituitary Cells

2008· article· en· W1977485952 on OpenAlexaff
Geneviève Renier, Omar Serri

Bibliographic record

VenueNeuroendocrinology · 2008
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsHôpital Notre-Dame
FundersMcDonnell Center for Systems Neuroscience
KeywordsEndocrinologyInternal medicineAnterior pituitaryGrowth hormoneSomatotropic cellHormoneMedicineChemistryBiology

Abstract

fetched live from OpenAlex

The aim of this study was to verify whether prolonged exposure of cultured rat anterior pituitary cells to high glucose can alter growth hormone (GH) release and responsiveness to secretagogues. Therefore, we cultured anterior pituitary cells obtained from normal male Sprague-Dawley rats in presence of normal (6 mM) or high (22 mM) glucose concentrations. After 3 days, the acute effects of glucose, growth hormone-releasing factor (GRF), dibutyryl cyclic AMP(db-cAMP) and somatostatin were studied during 2-hour incubations. High glucose did not alter basal GH release from cells cultured in 6 mM glucose. However, basal GH release from cells cultured in 22 mM glucose was moderately higher in the 2-hour incubation (by 46%) than in cells cultured in 6 mM glucose. In contrast, GH stimulation by GRF or db-cAMP was significantly reduced in cells cultured in 22 mM as compared to cells cultured in 6 mM glucose. This inhibitory effect of high glucose on GRF-stimulated GH release was completely reversible after 24 h of exposure of the cultured cells to 6 mM glucose and testing on the 4th day of culture. Finally, GH inhibition by somatostatin was also attenuated in cells cultured with high glucose. We conclude that prolonged exposure to high glucose could act directly at the pituitary level to modulate GH release and responsiveness.

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 categoriesMeta-epidemiology (narrow)
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 score0.075
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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.

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

Citations16
Published2008
Admission routes1
Has abstractyes

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