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Record W2041028178 · doi:10.1002/jtra.10040

Regulation of tyrosine phosphorylation cascades by phosphatases: What the actions of vanadium teach us

2003· article· en· W2041028178 on OpenAlexaff
P A Hulley, Allan J. Davison

Bibliographic record

VenueThe Journal of Trace Elements in Experimental Medicine · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDephosphorylationProtein tyrosine phosphatasePhosphorylationTyrosine phosphorylationCell biologyPhosphataseReceptorBiologyTyrosineChemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract Protein phosphorylation and dephosphorylation regulate much of the machinery of the cell. Emphasis in recent years has swung toward regulation by dephosphorylation. Much current research focuses on protein tyrosine phosphatases. Researchers of cellular regulation use vanadium as a probe because of its unparalleled ability to selectively inhibit protein tyrosine phosphatases at submicromolar concentrations. This review focuses on the biological actions of vanadium relevant to cellular regulatory cascades. Recent research has led to identification of control points and possible drug targets in 1) the glucose control mechanisms downstream from insulin receptors; 2) pathways regulating mitogenesis, tumor promotion, and other events downstream from growth factor receptors; 3) regulation of osteogenesis and possibilities for counteracting the bone damaging actions of glucocorticoids. An up‐to‐date understanding of the mechanisms by which vanadium acts and of its currently identified targets is prerequisite to the intelligent design of experiments of this kind. In this review, we will consider mechanisms at the enzymological level, in cellular regulatory cascades, and events affecting the cell or organism as a whole. J. Trace Elem. Exp. Med. 16:281–290, 2003. © 2003 Wiley‐Liss, Inc.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations20
Published2003
Admission routes1
Has abstractyes

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