The Lectin Pathway of Complement Activation in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: The pathogenesis of systemic lupus erythematosus (SLE) involves complement activation. Activation of complement through the classical pathway (CP) is well established. However, complement activation through pattern recognition not only happens through the CP, but also through the lectin pathway (LP). We investigated the hypothesis that the LP is activated in SLE and involved in the pathogenesis of the disease. METHODS: Using immunoassays developed in-house, we measured concentrations of LP proteins in a cohort of 372 patients with SLE and 170 controls. We estimated complement activation measuring total C3, and investigated whether LP protein concentrations were associated with complement activation and disease activity. Protein changes and disease activity over time were assessed in a cohort of 52 patients with SLE followed with repeated samples over a 5-year period. RESULTS: Concentrations of LP proteins in SLE were altered compared with controls. The differences observed in LP proteins associated with complement activation were reflected by a decrease in total C3. The pattern recognition molecules (M-ficolin, CL-L1, and CL-K1), the serine protease (MASP-3), and the associated protein (MAp19) displayed a negative correlation with disease activity. Changes in MASP-2 concentrations over time correlated significantly with increased disease activity. Association between active proteinuria and serum concentration was observed for MASP-3 and MAp19. CONCLUSION: In patients with SLE, we measured specific changes in LP proteins that are associated with complement activation and disease activity, indicating that the LP is activated in patients with SLE. These novel findings substantiate the involvement of the LP in SLE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".