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Record W2117350148 · doi:10.1155/2012/328372

The MAPK MEK1/2-ERK1/2 Pathway and Its Implication in Hepatocyte Cell Cycle Control

2012· article· en· W2117350148 on OpenAlexaff
Jean‐Philippe Guégan, Christophe Frémin, Georges Baffet

Bibliographic record

VenueInternational Journal of Hepatology · 2012
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsUniversité de Montréal
FundersUniversité de Rennes 1Ligue Contre le CancerInstitut National de la Santé et de la Recherche Médicale
KeywordsHepatocyteMAPK/ERK pathwayLiver regenerationIn vivoCell biologyKinaseProtein kinase ACancer researchRegeneration (biology)CellIn vitroCell growthBiologyMedicineBiochemistryGenetics

Abstract

fetched live from OpenAlex

Primary cultures of hepatocytes are powerful models in studying the sequence of events that are necessary for cell progression from a G0-like state to S phase. The models mimic the physiological process of hepatic regeneration after liver injury or partial hepatectomy. Many reports suggest that the mitogen-activated protein kinase (MAPK) ERK1/2 can support hepatocyte proliferation in vitro and in vivo and the MEK/ERK cascade acts as an essential element in hepatocyte responses induced by the EGF. Moreover, its disregulation has been associated with the promotion of tumor cell growth of a variety of tumors, including hepatocellular carcinoma. Whereas the strict specificity of action of ERK1 and ERK2 is still debated, the MAPKs may have specific biological functions under certain contexts and according to the differentiation status of the cells, notably hepatocytes. In this paper, we will focus on MEK1/2-ERK1/2 activations and roles in normal rodent hepatocytes in vitro and in vivo after partial hepatectomy and in human hepatocarcinoma cells. The possible specificity of ERK1 and ERK2 in normal and transformed hepatocyte will be discussed in regard to other differentiated and undifferentiated cellular models.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.239

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 head0.010
GPT teacher head0.280
Teacher spread0.270 · 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 designObservational
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

Citations62
Published2012
Admission routes1
Has abstractyes

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