Serine Proteases in Systemic Lupus Erythematosus: The Other Half of the Story
Bibliographic record
Abstract
The contribution by Troldborg, et al 1 is a valuable addition to our understanding of disease, addressing half the question.Serine proteases do not function in isolation, but are also part of an enzyme-inhibitor interaction 2 .Noting higher enzyme concentrations in their cross-sectional study of patients with systemic lupus erythematosus 1 , direct correlation with nephritis and titers of anti-dsDNA, and inverse correlation with complement C3, the authors have demonstrated that serine protease levels appear to be markers of disease activity.It may also be worthwhile to assess whether their results reflect disease activity or alteration by the medications used in its treatment, as has been demonstrated for the major serine protease inhibitors, α-1-antitrypsin, α-2-macroglobulin, and antithrombin III 2,3,4,5,6 .Their implication of a pathophysiologic involvement is an interesting speculation, especially if a moderating component is considered.Serine protease inhibitor levels are also proportionate to disease activity 7 : We and others reported levels proportionate to α-1-antitrypsin directly, and α-2-macroglobulin and antithrombin III inversely 7,8 .Serine protease inhibitors also have a significant immune modulation effect 7 , but it is unclear if this effect is related to the native molecule or to the complex it forms with serine proteases 2,9,10 .In a relationship analysis of the levels, both components and their combination seem to be a fruitful area for future investigation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".