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Record W2011671951 · doi:10.1080/08916930412331281027

Experimental Autoimmune Thyroiditis (EAT) Induced by the Thyroglobulin Peptide (2596–2608): Influence of H-2 and Non H-2 Genes

2004· article· en· W2011671951 on OpenAlexafffund
Panayotis Verginis, George Carayanniotis

Bibliographic record

VenueAutoimmunity · 2004
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandCanadian Institutes of Health ResearchUniversity of Connecticut
KeywordsThyroglobulinAutoimmune thyroiditisPeptidePriming (agriculture)ThyroiditisAutoimmunityGeneImmunologyIn vitroEndocrinologyInternal medicineMolecular biologyChemistryBiologyThyroidMedicineImmune systemBiochemistry

Abstract

fetched live from OpenAlex

We have previously identified five thyroglobulin (Tg) peptides with Ak-binding motifs that induce experimental autoimmune thyroiditis (EAT) in CBA/J (H-2k) mice. In this study, we have examined whether H-2 or non H-2 genes can influence the immunopathogenicity of peptide p2596 (a.a. 2596-2608), which earlier elicited considerable pathology in CBA/J hosts. The p2596 peptide induced mild EAT--(infiltration index range=1-2)-- in H-2-compatible AKR/J, B10.BR, and C3H/HeJ mice. Moreover, p2596-primed LNC from these mice exhibited peptide-specific proliferative responses and secreted significant amounts of IL-2 and IFN-gamma in recall in vitro assays. Priming and boosting of these strains with p2596 resulted in the generation of specific IgG responses five weeks after the initial challenge. In contrast, s.c. challenge of H-2-incompatible strains such as DBA/1J (H-2q), SJL (H-2s), DBA/2J (H-2d) and C57BL/6 (H-2b) with the same peptide dose did not elicit EAT pathology and peptide-specific B- or T-cell responses. These data demonstrate the thyroiditogenic potential of p2596 in H-2k strains of diverse non-H-2 backgrounds but not in mice carrying H-2b, d, q or s haplotypes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.999

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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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

Citations4
Published2004
Admission routes2
Has abstractyes

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