Monocyte Chemoattractant-1 as a Urinary Biomarker for the Diagnosis of Activity of Lupus Nephritis in Brazilian Patients
Bibliographic record
Abstract
OBJECTIVE: Monocyte chemotactic protein (MCP-1), involved in the pathogenesis of lupus nephritis (LN), has recently been indicated as a new biomarker of kidney activity in systemic lupus erythematosus (SLE). Our aim was to assess urinary MCP-1 (uMCP-1) as a biomarker of renal activity in patients with SLE and to compare it to other disease activity markers, using the ELISA. METHODS: Seventy-five female Brazilian patients with SLE and a control group participated in our study. Patients with SLE were distributed among 3 groups according to kidney involvement and classified according to disease activity based on clinical and laboratory measures such as urinary sediment, proteinuria, kidney function, C3, C4, anti-dsDNA, disease activity index, and renal SLE disease activity index. The serum and uMCP-1 concentrations were measured by sandwich ELISA. RESULTS: In the A-LN group (active lupus nephritis: SLE with kidney involvement), the concentration of uMCP-1 was significantly higher than in other groups. A cutoff point was established using the results of the control group to apply this test in the detection of LN. A-LN had a higher frequency of positive results for uMCP-1 in comparison to the other groups (p < 0.001). To detect disease activity in patients with LN, a new cutoff was determined based on the results of patients with SLE with kidney involvement. Setting specificity at 90%, the sensitivity of the test was 50%. CONCLUSION: The high specificity makes uMCP-1 a useful test as a predictor of kidney activity in SLE, especially when associated to other measures used in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".