Soluble TNF-R1, VEGF and other cytokines as markers of disease activity in systemic lupus erythematosus and lupus nephritis
Bibliographic record
Abstract
BACKGROUND: Current non-invasive methods of assessing disease activity in systemic lupus erythematosus (SLE) are of limited sensitivity and specificity. Testing includes acute phase markers, autoantibodies and complement levels. Although measurements of dsDNA antibodies and complement C3/C4 levels are routine, they remain of limited value. Improved blood and urine markers may help in early detection of flare, distinction between flare and chronic damage, and monitoring response to therapy. METHODS: A total of 87 patients with SLE were tested for the following cytokines in serum and urine: monocyte chemoattractant protein 1 (MCP-1), regulated upon activation, normal T cell expressed and secreted (RANTES), soluble tumour necrosis factor receptor 1 (sTNF-R1), interferon-inducible protein 10 (IP-10), monocyte inhibitory protein 1α (MIP-1α) and vascular endothelial growth factor (VEGF). Patients attending the Lupus Unit at St Thomas' Hospital, London, UK were divided into active lupus nephritis (LN), inactive LN and non-renal SLE groups based on their renal pathology and SLE disease activity index (SLEDAI). Cytokine testing was performed using the FIDIS multiplex bead assay. RESULTS: The mean level of serum sTNF-R1 was higher in the active LN group compared with both inactive LN and non-renal SLE groups ( p < 0.001). For urine measurements there were significant differences between active LN and non-renal SLE for VEGF ( p = 0.016), after statistical correction for multiple testing. Both urinary and serum sTNF-R1 and IP-10 levels correlated with SLEDAI scores ( p < 0.001), while serum VEGF correlated weakly with SLEDAI ( p = 0.025). The optimum combination for differentiating active from inactive LN patients was serum VEGF, sTNF-R1, MCP-1 and glomerular filtration rate plus urinary sTNF-R1 and protein-creatinine ratio. CONCLUSION: These results indicate that for active LN, sTNF-R1 could be a useful serum cytokine marker, with potential for VEGF in the urine. This study has confirmed the ability of the multiplex bead technique to detect cytokines in a good analytical range, including very low and high levels, in both serum and urine. Combining serum and urine markers provided additional sensitivity in distinguishing active from inactive LN.
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 | 0.001 | 0.000 |
| 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".