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Record W2007027069 · doi:10.2298/jsc0505705v

Analysis of nuclear glucocorticoid receptor-DNA interaction in aged rat liver

2005· article· en· W2007027069 on OpenAlexaff
Miroslava Vujčić, Nataša Terzić, Aleksandra Ristic‐Fira, Dušan T. Kanazir, Sabera Ruždijić

Bibliographic record

VenueJournal of the Serbian Chemical Society · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsElectrophoretic mobility shift assayNuclear receptorNuclear proteinGlucocorticoid receptorDNAOligonucleotideMolecular biologyAgeingReceptorNuclear DNAChemistryBiologyBiochemistryTranscription factorGeneticsGene

Abstract

fetched live from OpenAlex

Abstract: In order to contribute to the understanding of mechanisms by which regulatory proteins recognize genetic information stored in DNA, analyses of their interaction with specific nucleotides are usually performed. In this study, the electrophoretic mobility shift assay (EMSA) was applied to analyze the interaction of nuclear proteins from the liver of rats of different age i.e., young (3-month-old), middle- aged (12-month-old) and aged (24-month-old), with radioactively labelled synthetic oligonucleotide analogues, corresponding to GRE. The levels of GRE binding activity were assessed by quantitative densitometric scanning of the autoradiograms. The results showed statistically significant decreasing values of up to 78% and 49% in middle aged and old animals, respectively, compared to young animals (p < 0.05). The specificity of the nuclear proteins-GRE interaction was demonstrated by competition experiments with unlabelled GRE. In a supershift assay, using the antibody BuGR2, it was shown that the GR proteins present in nuclear extracts have a high affinity for the GRE probe. The stabilities of the protein-DNA complexes were analysed and it was concluded that they changed during ageing. .

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 score0.003
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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