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Cytokine Expression in Unstimulated PBMC of Children with Type 1 Diabetes and Subjects Positive for Diabetes‐Associated Autoantibodies

2001· article· en· W2041026724 on OpenAlexaff
Markus Halminen, Olli Simell, Mikael Knip, Jorma Ilonen

Bibliographic record

VenueScandinavian Journal of Immunology · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsJuvenile Diabetes Research Foundation
FundersTurun yliopiston tutkijakoulu
KeywordsAutoantibodyType 1 diabetesPeripheral blood mononuclear cellCytokineInternal medicineImmunologyEndocrinologyMedicineDiabetes mellitusAntibodyBiologyIn vitro

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate possible changes in the circulating levels of interferon (IFN)-gamma, interleukin (IL)-4 and transforming growth factor (TGF)-beta in association with the autoimmune process leading to type 1 diabetes. Expression levels of mRNAs specific for each cytokine were determined in peripheral blood mononuclear cells (PBMC) by a multiplex reverse transcription-polymerase chain reaction (RT-PCR) followed by hybridization reactions with lanthanide-labelled probes and detection by time-resolved fluorometry. Newly diagnosed diabetic children had lower levels of IFN-gamma, IL-4 and TGF-beta 1 signals compared to their age- and sex-matched controls (P < 0.02, P < 0.005 and P < 0.005, respectively) and also the autoantibody-positive subjects had significantly lower levels of IL-4 and TGF-beta 1 in comparison with their matched controls (P = 0.0013 and P = 0.012). No significant differences were observed when comparing matched pairs of diabetic children and autoantibody-positive subjects. Our results suggest a systemic bias towards reduced production of T-helper cell type 2 cytokines (IL-4 and TGF-beta 1) during the autoimmune process, but there was also a reduced level of IFN-gamma expression in the periphery at the onset of clinical 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.523

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 head0.004
GPT teacher head0.217
Teacher spread0.213 · 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 designObservational
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

Citations43
Published2001
Admission routes1
Has abstractyes

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