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Record W2145444845 · doi:10.1186/1476-9255-9-23

Role of glycogen synthase kinase-3 beta in the inflammatory response caused by bacterial pathogens

2012· article· en· W2145444845 on OpenAlexafffund
Ricarda Cortés-Vieyra, Alejandro Bravo-Patiño, Juan J. Valdez‐Alarcón, Marcos Cajero Juárez, B. Brett Finlay, Víctor M. Baizabal‐Aguirre

Bibliographic record

VenueJournal of Inflammation · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCoordinación de la Investigación CientíficaCanadian Institutes of Health ResearchUniversidad Michoacana de San Nicolás de HidalgoConsejo Nacional de Ciencia y Tecnología
KeywordsGSK-3InflammationProinflammatory cytokineGSK3BContext (archaeology)Glycogen synthaseBacteriaCell biologyMicrobiologySignal transductionInflammatory responseBiologyPathogenKinaseImmunologyPhosphorylationGenetics

Abstract

fetched live from OpenAlex

Glycogen synthase kinase 3β (GSK3β) plays a fundamental role during the inflammatory response induced by bacteria. Depending on the pathogen and its virulence factors, the type of cell and probably the context in which the interaction between host cells and bacteria takes place, GSK3β may promote or inhibit inflammation. The goal of this review is to discuss recent findings on the role of the inhibition or activation of GSK3β and its modulation of the inflammatory signaling in monocytes/macrophages and epithelial cells at the transcriptional level, mainly through the regulation of nuclear factor-kappaB (NF-κB) activity. Also included is a brief overview on the importance of GSK3 in non-inflammatory processes during bacterial infection.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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 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

Citations91
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueJournal of InflammationSame topicWnt/β-catenin signaling in development and cancerFrench-language works237,207