Short-term effect of radioactive iodine therapy on CXCL-10 production in Graves’ disease
Bibliographic record
Abstract
PURPOSE: To observe the short-term dynamic change in serum CXC chemokine ligand-10 (CXCL10) levels in patients with Graves' disease (GD) before and after iodine therapy and to analyze the relationship between CXCL10 levels and clinical disease indices. METHODS: ELISA was used to determine serum levels of CXCL10 in 43 patients with GD shortly before radioiodine therapy and on days six, 14, and 60, post-therapy. RESULTS: Patients with newly diagnosed GD showed significantly higher levels of serum CXCL10 compared with the control group (P < 0.01). The serum CXCL10 level increased slightly on day six after treatment of radioactive iodine (P < 0.01). There was no significant statistical difference in serum CXCL10 levels pre-treatment and on day 14 post-treatment. A significant reduction in serum CXCL10 level was observed on day 60 (P < 0.01). GD patients with exophthalmia showed higher serum CXCL10 level than GD patients without exophthalmia. No correlation was found between levels of CXCL10 and FT3, FT4 or TSH at any time point, but significant positive correlation was shown between thyroid peroxidase antibodies (TPOAb) and CXCL10 (r=0.50, P < 0.01). CONCLUSION: CXCL10 participates in the early inflammatory response after radioactive iodine therapy in patients with Graves' disease and shows a strong association with the autoimmune process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".