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Record W1977679785 · doi:10.1159/000212683

Glucocorticoid-Induced Modulation of Insulin-Stimulated DNA Synthesis: Differential Responsiveness in Cell Cultures Derived from Donors of Different Ages

2009· article· en· W1977679785 on OpenAlexaff
Ralph J. Germinario, Angela McQuillan

Bibliographic record

VenueGerontology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsInsulinHydrocortisoneEndocrinologyInternal medicineBiologyGlucocorticoidDNA synthesisCell cultureStimulationHormoneDNAFibroblastBasal (medicine)In vitroBiochemistryMedicineGenetics

Abstract

fetched live from OpenAlex

The replicative ability of variously 'aged' cell cultures, their insulin binding and biological responsiveness under control and glucocorticoid (i.e. hydrocortisone) amplified conditions have been studied in human fibroblast cultures. Insulin stimulation of DNA synthesis in early and late passage cultures and in cultures from young and old donors showed no age-related difference in insulin responsiveness. Hydrocortisone amplification of insulin-stimulated DNA synthesis in early and late passage cells expressed no age-related differences. Hydrocortisone affected basal DNA synthesis in cultures from in vivo young and old donors differently. Additionally, hydrocortisone amplified insulin-stimulated DNA synthesis in young donor cell cultures was observed to be higher than in old donor cell cultures. Specific 125I-insulin binding was increased by hydrocortisone in both early and late passage cultures and in cultures from young and old donors but no age-related differences in 125I-insulin binding were observed in the presence or absence of hydrocortisone. The data suggest that an age-related loss of an insulin postreceptor interaction during hydrocortisone amplification of insulin-stimulated DNA synthesis is being expressed in the cultures from old donors.

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 score0.297
Threshold uncertainty score0.779

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 head0.015
GPT teacher head0.260
Teacher spread0.244 · 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

Citations12
Published2009
Admission routes1
Has abstractyes

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