Identification of Distinct Biological Groups of Patients With Cryptogenic NORSE via Inflammatory Profiling
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aim of this study was to identify distinct inflammatory response subtypes in patients with c-NORSE by analyzing their cytokine profiles. Insights into underlying mechanisms were sought to understand the pathophysiology and guide personalized therapies to improve patient outcomes. METHODS: Sixty-two patients with c-NORSE were included. A comprehensive panel of 96 cytokines was analyzed in serum samples. Patients were clustered based on their cytokine profiles using the Louvain algorithm, an unsupervised graph-based clustering method. The identified clusters of patients were compared regarding cytokine levels and clinical features. Protein pathway analysis was used to explore the biological relevance of the inflammatory markers within each cluster. Patients with c-NORSE were compared with control patients (n = 18) and patients with other forms of refractory SE (n = 45). RESULTS: Compared with controls, patients with c-NORSE exhibited significant differences in 33 cytokines. Pathway analysis revealed dysregulations in chemotaxis and neutrophil recruitment and migration, highlighting the importance of innate immunity in patients with c-NORSE. Within the c-NORSE cohort, 3 clusters of patients emerged: cluster A, lacking specific inflammatory markers; cluster B, with a much stronger innate-immunity cytokine-driven inflammatory response compared with clusters A and C; and cluster C, defined by dysregulated autoimmune processes. Notably, patients in cluster B showed a statistically significant elevation of innate immune-related proinflammatory cytokines associated with leukocyte recruitment and degranulation. By contrast, those in cluster C showed activation of Janus kinase signal transducer and activator of transcription (JAK-STAT) pathways, suggesting autoimmune mechanisms. Patients in clusters B and C demonstrated varied responses to immunotherapies, with cluster C patients showing favorable outcomes after multiple immunotherapies. DISCUSSION: The identification of distinct inflammatory subgroups in c-NORSE suggests that variations in the underlying immune mechanisms contribute to differential treatment responses. These findings underscore the importance of personalized therapeutic strategies, potentially targeting specific inflammatory pathways, to optimize clinical outcomes in this challenging condition.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".