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Record W2038836037 · doi:10.1152/ajpendo.90857.2008

Breakdown of endocytosis in the oncogenic activation of receptor tyrosine kinases

2009· review· en· W2038836037 on OpenAlexaff
Jasmine V. Abella, Morag Park

Bibliographic record

VenueAmerican Journal of Physiology-Endocrinology and Metabolism · 2009
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsOccupational Cancer Research Centre
Fundersnot available
KeywordsEndocytosisReceptor tyrosine kinaseCell biologyTyrosine kinaseReceptor Protein-Tyrosine KinasesKinaseReceptor-mediated endocytosisReceptorProto-oncogene tyrosine-protein kinase SrcChemistryBiologySignal transductionBiochemistry

Abstract

fetched live from OpenAlex

There is increasing evidence to support the concept that the malignant behavior of many tumors is sustained by the deregulated activation of growth factor receptors. Activation of receptor tyrosine kinases (RTKs) by their respective ligand(s) initiates cellular signals that tightly modulate cell proliferation, survival, differentiation and migration to ensure normal tissue patterning. Therefore, uncontrolled activation of such signals can have deleterious effects, leading to oncogenesis. To date, deregulation of most RTKs has been implicated in the development of cancer, although the mechanisms that lead to their deregulation are not yet fully understood (10). RTK endocytosis, the internalization and trafficking of receptors inside the cell, has long been established as a mechanism to attenuate RTK signaling. However, RTKs have been demonstrated to continue to signal along the endocytic pathway, which contributes to the spatio-temporal regulation of signal transduction. This review will focus on recent advances linking defective endocytosis of RTKs in the development of 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.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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

Citations94
Published2009
Admission routes1
Has abstractyes

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