Circulating neutrophil extracellular trap remnants as a biomarker to predict outcomes in lupus nephritis
Bibliographic record
Abstract
OBJECTIVE: To determine if the serum levels of neutrophil extracellular trap (NET) remnants (Elastase-DNA and HMGB1-DNA complexes) at the time of a lupus nephritis (LN) flare predict renal outcomes in the following 24 months. METHODS: This was a retrospective study performed in prospectively followed cohorts. The study included two cohorts: an exploratory cohort to assess the association between NET remnant levels and the presence of active LN, and a separate LN cohort to determine the utility of NET remnants to predict renal outcomes over the subsequent 24 months. RESULTS: Ninety-two individuals were included in the exploratory cohort (49 active systemic lupus erythematosus (SLE), 23 inactive SLE and 20 healthy controls (HC)). NET remnants were significantly higher in patients with SLE patients compared with HC (p<0.0001 for both complexes) and those with active LN (36%) had significantly higher levels of NET remnants compared with active SLE without LN (Elastase-DNA: p=0.03; HMGB1-DNA: p=0.02). The LN cohort included 109 active LN patients. Patients with proliferative LN had significantly higher levels of NET remnants than non-proliferative LN (Elastase-DNA: p<0.0001; HMGB1-DNA: p=0.0003). Patients with higher baseline levels of NET remnants had higher odds of not achieving complete remission (Elastase-DNA: OR 2.34, p=0.007; HMGB1-DNA: OR 2.61, p=0.009) and of progressing to severe renal impairment (Elastase-DNA: OR 2.84, p=0.006; HMGB1-DNA: OR 2.04, p=0.02) at 24 months after the flare. CONCLUSIONS: Elastase-DNA and HMGB1-DNA complexes predict renal outcomes, suggesting they could be used to identify patients requiring more aggressive therapy at flare onset.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".