Intrarenal and Urinary Th9 and Th22 Cytokine Gene Expression in Lupus Nephritis
Bibliographic record
Abstract
OBJECTIVE: We studied the urinary sediment mRNA level of Th9- and Th22-related cytokines in patients with systemic lupus erythematosus (SLE). METHODS: We quantified urinary mRNA levels of interleukin (IL) 9, IL-10, IL-22, and their corresponding transcription factors in 73 patients with active lupus nephritis, 13 patients with hypertensive nephrosclerosis (HTN), and 25 healthy subjects. RESULTS: There was no detectable IL-9 mRNA in all samples. Patients with proliferative lupus nephritis had significantly lower urinary IL-22 mRNA levels than those with nonproliferative nephritis (2.2 ± 5.4 vs 8.6 ± 20.0 copies, p = 0.019), and urinary IL-22 mRNA level inversely correlated with the histological activity index (r = -0.427, p < 0.0001). In contrast, patients with lupus nephritis had significantly higher urinary IL-10 mRNA levels than patients with HTN (7.8 ± 18.5 vs 1.9 ± 4.0 copies, p = 0.012), and urinary IL-10 mRNA levels correlated with its intrarenal mRNA levels (r = 0.337, p = 0.004) and SLE disease activity index (r = 0.277, p = 0.018). Urinary IL-10 mRNA level was significantly lower among patients who achieved complete remission than those with partial remission or no response (4.1 ± 6.5 vs 14.1 ± 28.0 copies, p = 0.036). CONCLUSION: Urinary IL-22 mRNA level is decreased in patients with SLE with proliferative nephritis, while urinary IL-10 mRNA levels correlates with its intrarenal mRNA level and disease activity. Urinary IL-10 mRNA levels may also predict treatment response. These results suggest that urinary mRNA levels of IL-10 and IL-22 might be used as biomarkers for assessing disease activity and risk stratification in lupus nephritis.
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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 | 0.000 |
| 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".