Phagocyte-specific S100A8/A9 Protein Levels During Disease Exacerbations and Infections in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: S100A8 and S100A9 are calcium binding proteins of the S100 family highly expressed in neutrophils and monocytes. S100 proteins are novel ligands of TLR4 important in modulating inflammation. High levels of S100A8/A9 found in human inflammatory diseases are a marker of disease activity in rheumatoid arthritis (RA) and juvenile rheumatoid arthritis (JRA). We determined levels of S100A8/A9 in sera of patients with systemic lupus erythematosus (SLE) and analyzed their relation to clinical variables of disease activity. METHODS: A group of 93 patients with SLE were studied over a period of 3 years, and 143 serum samples were analyzed. S100A8/A9 serum concentrations were determined by a sandwich ELISA. Sera from 10 primary Sjögren's syndrome (pSS) patients and 50 healthy volunteers were used as controls. Correlations to SLEDAI, ANA, anti-dsDNA, WBC, CH50, C4, and CRP were made. In addition, infections were recorded in all SLE patients. RESULTS: Serum levels of S100A8/A9 were significantly (p = 0.04) higher in SLE patients (1412 +/- 664 ng/ml) versus healthy controls (339 +/- 35 ng/ml) and pSS patients (400 +/- 85 ng/ml). The only significant correlation (r = 0.219; p = 0.015) was found was between S100A8/A9 and SLEDAI. Further, SLE patients with concomitant infections had higher serum levels of S100A8/A9 (39300 +/- 13375 ng/ml) than those without infections (1150 +/- 422 ng/ml). CONCLUSION: Serum levels of S100A8/A9 are significantly raised in SLE versus pSS patients and healthy controls and can be correlated to a disease activity index. S100A8/A9 is a more relevant marker of infection in SLE patients.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".