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Record W6989212369

The anti-inflammatory cytokine, interleukin (IL)-10, blocks the inhibatory effect of IL-1 beta on long term potentiation - a role for JNK

2001· article· en· W6989212369 on OpenAlexaff

Bibliographic record

VenueArrow@dit (Dublin Institute of Technology) · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsTrinity College
Fundersnot available
KeywordsLong-term potentiationGlutamate receptorStimulationProtein kinase CCentral nervous systemProinflammatory cytokineSuperoxide dismutaseSynaptosomeInhibitory postsynaptic potential
DOInot available

Abstract

fetched live from OpenAlex

Several effects of the proinflammatory cytokine, interleukin-1? (IL-1?), have been described in the central nervous system, and one area of the brain where marked changes have been reported is the hippocampus. Among these changes are an IL-1?-induced inhibition of long term potentiation (LTP) in perforant path-granule cell synapses and an attenuation of glutamate release in synaptosomes prepared from the hippocampus. Evidence suggests that, at least in circulating cells, the anti-inflammatory cytokine, IL-10, antagonizes certain effects of IL-1. We investigated the effect of IL-10 on IL-1?-induced inhibition of LTP and glutamate release. The evidence presented indicates that IL-1? stimulates the stress-activated protein kinase, c-Jun-activated protein kinase (JNK), and IL-1 receptor-associated kinase, which may explain its inhibitory effect on release and LTP, and that IL-10 reversed the IL-1?-induced stimulation of JNK activity and inhibition of release and LTP. We observed that IL-10 abrogated the stimulatory effect of IL-1? on superoxide dismutase activity and reactive oxygen species production, whereas the H2O2-induced inhibition of LTP was also blocked by IL-10. We present evidence that suggests that the action of IL-10 may be mediated by its ability to induce shedding of the IL-1 type I receptor.

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 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 score0.036
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations0
Published2001
Admission routes1
Has abstractyes

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