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Record W2043386035 · doi:10.1139/o99-070

Signal transduction pathways, and nuclear translocation of zinc and metallothionein during differentiation of myoblasts

2000· article· en· W2043386035 on OpenAlexaffvenue
Margarita D. Apostolova, Iordanka A. Ivanova, M. George Cherian

Bibliographic record

VenueBiochemistry and Cell Biology · 2000
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsWestern University
FundersMedical Research CouncilUniversity of Dodoma
KeywordsMyogenesisMetallothioneinCytoplasmMyocyteCell biologyBiologySignal transductionNuclear localization sequenceCellular differentiationProtein kinase ABiochemistryKinaseMolecular biology

Abstract

fetched live from OpenAlex

The changes in subcellular localization of metallothionein during differentiation were studied in two myoblast cell lines, L6 and H9C2. Addition of insulin like growth factor-I or lowering foetal bovine serum to 1% can induce differentiation of myoblasts to myotubes. Metallothionein and zinc were localized mainly in the cytoplasm in myoblasts but were translocated into the nucleus of newly formed myotubes during early differentiation. In fully differentiated myotubes, metallothionein content was decreased with a cytoplasmic localization. Addition of an inhibitor of mitogen-activated protein kinase, PD 98059, did not affect differentiation but blocked nuclear translocation of metallothionein. LY 294092, an inhibitor of PI3 kinase, and rapamycin, an inhibitor of p70S6 serine/threonine kinase, abolished insulin-like growth factor-I induced differentiation of myoblasts, retained metallothionein in the cytoplasm, and decreased metallothionein content. These results demonstrate that the cytoplasmic-nuclear translocation of metallothionein occurs during the early stage of differentiation of myoblasts to myotubes and can be blocked by inhibition of certain signal transduction pathways. The transient nuclear localization of metallothionein and zinc may be related to a high requirement for zinc for metabolic activities during the early stage of differentiation.

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)0.000
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 score0.035
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Citations40
Published2000
Admission routes2
Has abstractyes

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