Interferon‐gamma <scp>ELISPOT</scp> detecting reactivity of <scp>T</scp> cells to <scp>TSH</scp> receptor peptides in <scp>G</scp>raves' disease
Bibliographic record
Abstract
OBJECTIVE: While thyrotrophin receptor (TSHR) is recognized as the main autoantigen in Graves' disease (GD), the actual antigen specificity of T cells that infiltrate the thyroid and the orbit is unknown. Identifying T cell responses to TSHR peptides has been difficult in the past due to the low frequency of autoreactive T cells and to the diversity of the putative epitopes identified by proliferation assays. METHODS: We used the interferon-gamma ELISPOT assay to identify T cell reactivity to TSHR peptides in patients with GD. Peripheral blood T cells were exposed in vitro to four pools of 10 overlapping TSHR peptides. RESULTS: T cells from 11 of 31 (35%) patients with GD and 1 of 22 (4%) healthy controls reacted to at least one peptide pool (P = 0·009). Mean time since diagnosis was 3·2 years in responder patients and 5·6 years in nonresponders (P = 0·07). In two patients, T cell reactivity was observed shortly after radioiodine treatment and not thereafter. CONCLUSIONS: Our findings demonstrate that the ELISPOT assay is effective to test T cell reactivity in patients with GD and that patients with GD have significantly more interferon-gamma responses towards TSHR peptides than controls. The data suggest that screening for T cell responses in patients with GD might be more efficient in recent-onset disease or after radioiodine treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".