Differential complement pathways and components in the clinical spectrum of systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: The precise contributions of the classical (CP), lectin (LP), and alternative (AP) pathways to systemic lupus erythematosus (SLE) remain incompletely understood. This study aimed to comprehensively investigate complement pathway components in SLE. METHODS: Sixty SLE patients and 20 healthy controls (HC) were recruited. Plasma C1q, C4, C4b, C2, C3, C5, Factor B (FB), Factor P (FP), Factor D (FD), Factor H (FH), Factor I (FI), and Mannose-binding lectin were measured by Multiplex Assay. RESULTS: Compared to HC and patients with low disease activity, active SLE showed decreased C1q, C4, C4b, C3, and FP, with increased C2, while FB was reduced only relative to HC. Patients with low C3 and C4 showed higher anti-dsDNA and C2, more newly diagnosed SLE, and lower C1q, C5, FP, FB, and FH. SLE patients with isolated low C3 exhibited reduced FP, FB, and FH, with a trend toward longer disease duration. Newly diagnosed SLE showed decreased C1q and C4b, while those with renal involvement had lower C1q and C3 but higher C2. The concurrent infection subgroup presented decreased C1q and increased C2. Regression analysis identified C2 as independently associated with concurrent infection and FD as the strongest negative correlation with renal function. Principal component analysis further delineated three patient clusters with distinct complement signatures. CONCLUSION: The CP is predominantly activated at disease onset, whereas the AP contributes to both initiation and progression of SLE. C2 and FD hold potential as biomarkers for SLE complications. Complement-driven stratification informs precision therapy 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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".