Differential Cytokine Expression and Regulatory Cells in Patients with Primary and Secondary <scp>S</scp>jögren's Syndrome
Bibliographic record
Abstract
Sjögren's syndrome (SS) is an autoimmune disease characterized by lymphocytic infiltration of the salivary and lacrimal glands. The aim of the study was to characterize and compare the presence of diverse cytokines and regulatory T and B cells in lip minor salivary gland (MSG) biopsies from patients with primary Sjögren's syndrome (pSS), secondary SS (sSS), and patients with connective tissue disease (CTD) without (w/o) SS. We included samples of MSG from 15 pSS, 24 sSS (six scleroderma, nine rheumatoid arthritis and nine lupus patients) and 15 patients with CTD w/o SS. Tissues were examined by an indirect immunoperoxidase technique (goat polyclonal anti-human IL-19, goat polyclonal anti-human IL-22 or mouse monoclonal anti-human IL-24). To determine the subpopulation of CD4(+)/IL-17A(+)-, CD4(+)/IL-4(+)-, CD4(+)/IFN-ɣ(+)-expressing T cells, CD25(+)/Foxp3(+) Treg cells and CD20(+)/IL-10(+)-producing B cell subset, a double-staining procedure was performed. We estimated the mean percentage of positively staining cells in two fields per sample. CD4(+)/IFN-ɣ(+), CD4(+)/IL-4(+) and IL-22(+) cell percentages were elevated in both SS varieties; however, the cells were more prevalent in pSS. Patients with pSS had a high number of CD4(+)/IL-17A(+) and IL-19(+) T cells and a lower percentage of IL-24(+) cells (P < 0.05). The Treg and IL-10-producing B cells were increased in pSS (P < 0.05). Concluding, in our patients, a pro-inflammatory and regulatory balance coexists in SS, being both responses more intense in pSS. The explanation of these differences may be related to disease activity, disease duration and 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 | 0.000 | 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".