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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".