Evaluation of Th-1 and Th-2 Immune Responses in the Skin Lesions of Patients with Blau Syndrome
Bibliographic record
Abstract
Blau syndrome is an autosomal dominant syndrome characterized by arthritis, uveitis, skin rash, granuloma, and camptodactyly. It has overlapping symptoms with sarcoidosis and rheumatoid arthritis. Our study was directed toward determining the role of cytokines in granuloma formation in Blau syndrome. Antigenic stimulation usually follows two pathways: Th-1, which activates macrophages and cytotoxic T-lymphocytes and produces interleukin (IL)-2, IL-3, interferon gamma, and tumor necrosis factor alpha, and Th-2, which activates the humoral immune system and produces IL-4, IL-5, and IL-10. The development of cytokine profiles may shed some light on our understanding of this illness. Therefore, we studied the relative roles of two opposing lymphocytes, Th-1 and Th-2, by analyzing their relative expression in the skin lesions of patients with Blau syndrome, using the in situ reverse transcription-polymerase chain reaction technique. Our data revealed a significant upregulation of IL-2, an event that appears to play an important role in the formation of granuloma and in the pathogenesis of Blau syndrome. Expression of IL-10, however, was downregulated, and this may have an inhibitory role in the development of the disease. Further studies would be necessary to confirm the presence of other cytokines and to establish the regulatory roles of Th-1 and Th-2 lymphocytes in the pathogenesis of Blau syndrome.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".