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Record W2014117985 · doi:10.1080/15216540211473

BAG‐1, An Anti‐Apoptotic Tumour Marker

2002· review· en· W2014117985 on OpenAlexaff
Shou‐Ching Tang

Bibliographic record

VenueIUBMB Life · 2002
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsApoptosisCancer researchReceptorProgrammed cell deathCytoplasmBiologyBiomarkerNuclear proteinCellCytosolCancerCell biologyHeat shock proteinTranscription factorBiochemistryGene

Abstract

fetched live from OpenAlex

BAG-1 is a multifunctional and anti-apoptotic or anti-cell death protein that interacts with a variety of cellular proteins and affects their functions. On the cell surface, it binds to the cytosolic domain of the growth factor receptors and enhances the protection from cell death triggered by growth factor receptors. In the cytosol, it binds to Bcl-2 and heat shock protein, and modulates their functions. In the nucleus, it binds to a variety of nuclear hormone receptors and inhibits hormone-induced apoptosis. BAG-1 is widely overexpressed in a variety of tumour cell lines and cancer tissues. In addition, differential expression of BAG-1 isoforms has been observed. Preclinical studies indicate that overexpression of BAG-1, especially its nuclear and cytoplasmic isoforms, may be useful as a prognostic and/or predictive biomarker. Pilot clinical studies have demonstrated that overexpression of nuclear BAG-1 may be associated with a shorter survival in breast and laryngeal carcinomas. Conversely, overexpression of cytoplasmic BAG-1 may be associated with a better clinical outcome in early stage breast cancer and in non-small cell lung cancer. Further large-scale clinical studies are warranted to establish the role of BAG-1 as a novel prognostic and/or predictive biomarker in the clinical management of these common malignancies.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.063
GPT teacher head0.365
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations53
Published2002
Admission routes1
Has abstractyes

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