Urine and Plasma Complement Ba Levels During Disease Flares in Patients With Antineutrophil Cytoplasmic Autoantibody–Associated Vasculitis
Bibliographic record
Abstract
Objective Although the alternative complement pathway has been implicated in the pathogenesis of ANCA-associated vasculitis (AAV), the specific nature of its involvement is unclear. This study measured levels of urine and plasma complement fragment Ba at multiple time points in a group of patients with AAV. Methods The complement fragment Ba was measured by ELISA in serial urine and plasma samples from 21 patients with AAV who developed a renal flare, 19 who developed a non-renal flare, and 20 in long-term remission. Urine Ba levels were corrected for urine creatinine (uCr) concentration. Changes in Ba levels were modeled using mixed linear-effect models. A logistic regression model was fit to predict a renal flare using Ba levels at the time of flare versus the non-renal flare and long-term remission groups. Results Data from 60 patients with AAV were used for this analysis; 53% were male, 93% were White, and 74% had antiproteinase3-ANCA. Urine Ba levels increased at renal flare (p<0.001) but remained stable during a non-renal flare or long-term remission. Plasma Ba levels were stable over time in all groups. Urine Ba levels predicted a renal flare with an area under the curve of 0.76 (p<0.001), with a cutoff of 12.53 ng/mg uCr yielding a sensitivity of 76.2% and a specificity of 68.4%. Conclusion Urine Ba levels, but not plasma Ba levels, are increased at the time of a renal flare in AAV, suggesting intra-renal complement activation, and highlighting the potential use of this biomarker for surveillance of active renal vasculitis.
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".