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Record W2137596844 · doi:10.1002/wrna.101

mRNA export and cancer

2011· review· en· W2137596844 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWiley Interdisciplinary Reviews - RNA · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersU.S. Public Health Service
KeywordsEIF4EMessenger RNANuclear export signalTranslation (biology)BiologyCancerRNANucleoporinEukaryotic translationCancer researchCell biologyGeneticsGeneCell nucleusNuclear transport

Abstract

fetched live from OpenAlex

Studies in the past several years highlight important features of the messenger RNA (mRNA) export process. For instance, groups of mRNAs acting in the same biochemical processes can be retained or exported in a coordinated manner thereby impacting on specific biochemistries and ultimately on cell physiology. mRNAs can be transported by either bulk export pathways involving NXF1/TAP or more specialized pathways involving chromosome region maintenance 1 (CRM1). Studies on primary tumor specimens indicate that many common and specialized mRNA export factors are dysregulated in cancer including CRM1, eukaryotic translation initiation factor 4E (eIF4E), HuR, nucleoporin 88, REF/Aly, and THO. This positions these pathways as potential therapeutic targets. Recently, specific targeting of the eIF4E-dependent mRNA export pathway in a phase II proof-of-principle trial with ribavirin led to impaired eIF4E-dependent mRNA export correlating with clinical responses including remissions in leukemia patients. Here, we provide an overview of these mRNA export pathways and highlight their relationship to cancer.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.077
GPT teacher head0.382
Teacher spread0.305 · 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