Urine and Plasma Complement Ba Levels During Flares of Nephritis in Patients with ANCA-Associated Vasculitis
Bibliographic record
Abstract
Background: The alternative complement pathway has been implicated in the pathogenesis of ANCA-associated vasculitis (AAV), however it is not clear whether activation of complement occurs systemically or in affected organs such as the kidney. This study measured levels of urinary and plasma complement fragment Ba (uBa and pBa respectively) at multiple timepoints in patients with AAV. Methods: Ba was measured by ELISA in serial samples of urine (uBa) and plasma (pBa) from 20 AAV patients who developed a renal flare, 20 who developed a non-renal flare, and 20 in long-term remission. Changes in Ba levels were modeled using linear mixed effect models. Results: Cohort characteristics are given in Figure.1. uBa levels increased at renal flare, but did not increase at non-renal flare, and remained stable in long-term remission (Figure 2a). pBa levels were stable over time in all groups (Figure 2b). uBa correlated with renal AAV activity measured as the renal component of the BVAS score (R2= 0.33, p<0.01), but did not correlate with the overall BVAS score during renal flare (R2= 0.13, p=0.12) or non-renal flare (R2= 0.10, p=0.22). Conclusions: Urine, but not plasma, Ba levels increase at the time of a flare of renal disease in AAV, suggesting intra-renal alternative complement pathway activation. uBa has the potential for use as a surveillance biomarker of renal vasculitis. Funding: Other NIH Support - Supported by the Vasculitis Clinical Research Foundation
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".