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Record W2129691912 · doi:10.1136/jcp.2003.015255

LKB1, the multitasking tumour suppressor kinase

2004· review· en· W2129691912 on OpenAlexaff
Paola A. Marignani

Bibliographic record

VenueJournal of Clinical Pathology · 2004
Typereview
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCancer researchBiologyWnt signaling pathwaySomatic cellKinaseCell biologySTK11SuppressorGermline mutationTumor suppressor geneGeneticsSignal transductionMutationCancerGeneCarcinogenesisKRAS

Abstract

fetched live from OpenAlex

Mutations in the lkb1 gene are found in Peutz-Jeghers syndrome (PJS), with loss of heterozygosity or somatic mutations at the lkb1 locus, suggesting the gene product, the serine/threonine kinase LKB1, may function as a tumour suppressor. Patients with PJS are at a greater risk of developing cancers of epithelial tissue origin. It is widely accepted that the presence of hamartomatous polyps in PJS does not in itself lead to the development of malignancy. The signalling mechanisms that lead to these PJS related malignancies are not well understood. However, it is evident from the recent literature that LKB1 is a multitasking kinase, with unlimited potential in orchestrating cell activity. Thus far, LKB1 has been found to play a role in chromatin remodelling, cell cycle arrest, Wnt signalling, cell polarity, and energy metabolism, all of which may require the tumour suppressor function of this kinase and/or its catalytic activity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.192
GPT teacher head0.502
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations66
Published2004
Admission routes1
Has abstractyes

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