Utility of Interferon-α as a Biomarker in Central Neuropsychiatric Involvement in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To assess the utility of interferon-α (IFN-α) in serum and cerebrospinal fluid (CSF) as a biomarker of disease activity in central neuropsychiatric systemic lupus erythematosus (cNPSLE). METHODS: Serum and CSF samples were drawn at hospitalization in 34 patients with cNPSLE, 16 surgical SLE, 4 primary neuropsychiatric conditions, and 25 with nonautoimmune conditions, except in 44 non-NPSLE patients in whom only serum was studied. Six months later, serum/CSF and serum samples were taken in 20 cNPSLE and 35 non-NPSLE patients, respectively. SLE activity was assessed at hospitalization, and 6 months later in cNPSLE and non-NPSLE patients. IFN-α was detected by Luminex technology. RESULTS: The mean ± SD age of patients with cNPSLE was 31.4 ± 12.2 years, which was similar across the study groups (p = 0.46). Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) scores among cNPSLE, non-NPSLE, and SLE-surgical patients were 15.3 ± 8.2, 12.4 ± 8.2, and 3.8 ± 1.5, respectively. IFN-α levels in serum were higher in cNPSLE than in nonautoimmune patients (p = 0.02), but were similar to non-NPSLE and SLE-surgical groups. In CSF samples, IFN-α levels were higher in cNPSLE than in nonautoimmune patients (p = 0.03), and were nonsignificantly higher than in SLE-surgical and primary neuropsychiatric patients. Six months later, serum levels of IFN-α did not vary from baseline values despite a significant decrease in SLEDAI-2K score in cNPSLE and non-NPSLE patients. IFN-α levels in the CSF of patients with cNPSLE also remained stable. Among specific cNPSLE syndromes, CSF IFN-α levels were significantly higher among patients with acute confusional syndrome. CONCLUSION: IFN-α does not seem to represent a useful biomarker of cNPSLE syndromes; its utility in specific cNPSLE manifestations merits further 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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".