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Record W4243628135 · doi:10.1172/jci200316409

Prediction of spontaneous autoimmune diabetes in NOD mice by quantification of autoreactive T cells in peripheral blood

2003· article· en· W4243628135 on OpenAlexafffund
Jacqueline D. Trudeau, Carolyn Kelly-Smith, C. Bruce Verchere, John F. Elliott, Jan Dutz, Diane T. Finegood, Pere Santamaría, Rusung Tan

Bibliographic record

VenueJournal of Clinical Investigation · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsBC Children's HospitalUniversity of CalgaryUniversity of AlbertaSimon Fraser UniversityUniversity of British Columbia
FundersCanadian Institutes of Health ResearchUniversity of British ColumbiaFondation pour la Recherche MédicaleChildren's Hospital FoundationMichael Smith Health Research BCCanadian Diabetes Association
KeywordsImmunologyPeripheral bloodNOD miceAutoimmunityType 1 diabetesMajor histocompatibility complexAutoimmune diabetesNodAvidityDiabetes mellitusAutoimmune diseaseMedicinePeripheralAntigenBiologyImmune systemEndocrinologyAntibodyInternal medicine

Abstract

fetched live from OpenAlex

Autoimmune (type 1) diabetes mellitus results from the destruction of insulin-producing pancreatic β cells by T lymphocytes.Prediction of cell-mediated autoimmune diseases by direct detection of autoreactive T cells in peripheral blood has proved elusive, in part because of their low frequency and reduced avidity for peptide MHC ligands.We demonstrate here that MHC class I tetramers complexed to a high-avidity analogue of an immunodominant β cell epitope detect diabetogenic CD8 + T cells in the peripheral blood of NOD mice ex vivo and that the quantification of this autoreactive T cell population in peripheral blood is a powerful predictor of autoimmune 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.276
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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

Citations215
Published2003
Admission routes2
Has abstractyes

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