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Record W2076312218 · doi:10.2337/diabetes.53.1.113

IL-1 Receptor Deficiency Slows Progression to Diabetes in the NOD Mouse

2004· article· en· W2076312218 on OpenAlexaff
Helen E. Thomas, Windy Irawaty, Rima Darwiche, Thomas C. Brodnicki, Pere Santamaría, Janette Allison, Thomas W. H. Kay

Bibliographic record

VenueDiabetes · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsUniversity of Calgary
FundersNational Health and Medical Research CouncilMedical Research CouncilFondation pour la Recherche Médicale
KeywordsNOD miceProinflammatory cytokineNodEndocrinologyTumor necrosis factor alphaInternal medicineDiabetes mellitusBeta cellBiologyImmunologyInterferon gammaCytokineMedicineIsletInflammation

Abstract

fetched live from OpenAlex

Proinflammatory cytokines are believed to be important in pancreatic beta-cell destruction in the development of type 1 diabetes. They act by upregulation of genes including Fas and inducible nitric oxide synthase (iNOS), which have both been shown to lead to beta-cell death in vitro. We used mice deficient in the interleukin (IL)-1 receptor (IL-1R) to assess the contribution of IL-1 to different models of diabetes. IL-1R-deficient islets were protected from the damaging effects of tumor necrosis factor (TNF) and interferon (IFN)-gamma in vitro, and beta-cell expression of iNOS was reduced, suggesting that IL-1 mediates the induction of iNOS by TNF and IFN-gamma. IL-1 action was not required for induction of class I major histocompatibility complex or Fas by TNF and IFN-gamma. IL-1R-deficient nonobese diabetic (NOD) mice developed diabetes significantly slower than wild-type mice. IL-1R deficiency did not affect diabetes in 8.3 TCR transgenic NOD mice but prolonged the time to diabetes in BDC2.5 TCR transgenic NOD mice. We conclude that IL-1R deficiency slows progression to diabetes in NOD mice but on its own does not prevent diabetes.

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 score0.100
Threshold uncertainty score0.551

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 head0.005
GPT teacher head0.231
Teacher spread0.225 · 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

Citations131
Published2004
Admission routes1
Has abstractyes

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