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Record W2011618490 · doi:10.1097/wnr.0b013e3282f27e60

Expression of cytokines and cytokine receptors in human Schwann cells

2008· article· en· W2011618490 on OpenAlexaff
Akihiko Ozaki, Atsushi Nagai, Yong B. Lee, Na H. Myong, Seung Up Kim

Bibliographic record

VenueNeuroreport · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsCytokineBiologyGlycoprotein 130ReceptorSchwann cellImmunologyInterleukin 6Cell biology

Abstract

fetched live from OpenAlex

The expression of cytokines and cytokine receptors was investigated in enriched populations of human fetal Schwann cells by reverse transcribed-PCR and enzyme-linked immunosorbent assay. Human fetal Schwann cells constitutively expressed mRNA of IL-1beta, IL-6, IL-8, IL-11, IL-12, IL-15 and TGF-beta, and also cytokine receptors for IL-1, IL-4, IL-6, IL-8, IL-13, tissue necrosis factor and gp130. The expression of IL-1beta, IL-6 and IL-15 was upregulated following treatment with IL-1beta or TGF-beta. The protein levels of IL-6 were increased with IL-1beta treatment, but were decreased with IFN-gamma treatment. Human Schwann cells may respond to cytokine signals in the nerve injury sites and modify the pathological conditions by secreting cytokines. The secreted cytokines may play a role in leukocyte recruitment and exacerbation of axonal injury process.

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 score0.067
Threshold uncertainty score0.345

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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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

Citations30
Published2008
Admission routes1
Has abstractyes

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