Measuring eosinophiluria, urinary eosinophil cationic protein and urinary interleukin-5 in patients with Lupus Nephritis
Bibliographic record
Abstract
BACKGROUND: Urine is increasingly becoming an attractive biological fluid in clinical practice due to being an easily obtained, non-invasive sampling method, containing proteins and peptides. The aim of this study was to investigate eosinophiluria, urinary eosinophil cationic protein (uECP) and urinary IL-5 (uIL-5) in patients with Lupus Nephritis. METHODS: Seventy-four patients with SLE-20 with clinical and laboratory evidence of lupus nephritis (LN group) and 54 without evidence of renal involvement (non-LN group)-were analyzed regarding eosinophiluria, uECP and uIL-5. Eosinophiluria was observed by Hansel's stain, ECP by fluoroenzymeimmunoassay and uIL-5 by quantitative sandwich enzyme immunoassay. Both uECP and urinary IL-5 (uIL-5) were corrected by urinary creatinine. Eosinophiluria and uECP were compared with glomerular erythrocyturia, protein/creatinine ratio (Pr/Cr ratio), serum creatinine, estimated glomerular filtration rate (eGFR), anti-double-stranded DNA (anti-dsDNA), serum levels of complement (C3 and C4), uIL-5/Cr ratio, and SLE disease activity index. RESULTS: Patients of the LN group had higher eosinophiluria, uECP, uECP/Cr ratio levels, and uIL-5 than patients of the non-LN group (p<0.001 for all). These variables showed a statistically significant correlation with glomerular erythrocyturia, casts, Pr/Cr ratio, serum creatinine, eGFR, anti-dsDNA, uIL-5/Cr, and SLE disease activity index (all p<0.05). CONCLUSION: These results provide evidence of increased urinary eosinophils, ECP and IL-5 in patients with SLE and LN; uECP/Cr ratio showed better correlation with markers of renal function and SLE disease activity.
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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) | 0.000 | 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".