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Record W2064325817 · doi:10.3389/fncel.2013.00009

IL-1RAcPb signaling regulates adaptive mechanisms in neurons that promote their long-term survival following excitotoxic insults

2013· article· en· W2064325817 on OpenAlexafffund
David Gosselin, Marc‐André Bellavance, Serge Rivest

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversité Laval
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchMultiple Sclerosis Scientific Research Foundation
KeywordsNeuroscienceTerm (time)BiologyPhysics

Abstract

fetched live from OpenAlex

Excitotoxicity is a major component of neurodegenerative diseases and is typically accompanied by an inflammatory response. Cytokines IL-1alpha and IL-1beta are key regulators of this inflammatory response and modulate the activity of numerous cell types, including neurons. IL-1RAcPb is an isoform of IL-1RAcP expressed specifically in neurons and promotes their survival during acute inflammation. Here, we investigated in vivo whether IL-1RAcPb also promotes neuronal survival in a model of excitotoxicity. Intrastriatal injection of kainic acid (KA) in mice caused a strong induction of IL-1 cytokines mRNA in the brain. The stress response of cortical neurons at 12 h post-injection, as measured by expression of Atf3, FoxO3a, and Bdnf mRNAs, was similar in WT and AcPb-deficient mice. Importantly however, a delayed upregulation in the transcription of calpastatin was significantly higher in WT than in AcPb-deficient mice. Finally, although absence of AcPb signaling had no effect on damage to neurons in the cortex at early time points, it significantly impaired their long-term survival. These data suggest that in a context of excitotoxicity, stimulation of IL-1RAcPb signaling may promote the activity of a key neuroprotective mechanism.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.234
Teacher spread0.195 · 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.

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

Citations21
Published2013
Admission routes2
Has abstractyes

Explore more

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