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Record W2045185273 · doi:10.4161/cam.27841

p53 in cell invasion, podosomes, and invadopodia

2014· review· en· W2045185273 on OpenAlexafffund
Alan S. Mak

Bibliographic record

VenueCell Adhesion & Migration · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsInvadopodiaPodosomeCell biologyExtracellular matrixCell migrationCancer cellBiologyCellCell growthCancerCytoskeleton

Abstract

fetched live from OpenAlex

Cell invasion of the extracellular matrix is prerequisite to cross tissue migration of tumor cells in cancer metastasis, and vascular smooth muscle cells in atherosclerosis. The tumor suppressor p53, better known for its roles in the regulation of cell cycle and apoptosis, has ignited much interest in its function as a suppressor of cell migration and invasion. How p53 and its gain-of-function mutants regulate cell invasion remains a puzzle and a challenge for future studies. In recent years, podosomes and invadopodia have also gained center stage status as veritable apparatus specialized in cell invasion. It is not clear, however, whether p53 regulates cell invasion through podosomes and invadopodia. In this review, evidence supporting a negative role of p53 in podosomes formation in vascular smooth muscle cells will be surveyed, and signaling nodes that may mediate this regulation in other cell types will be explored.

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
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.021
GPT teacher head0.263
Teacher spread0.243 · 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

Citations28
Published2014
Admission routes2
Has abstractyes

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