Peripheral HLA-DR <sup>hi</sup> CD141 <sup>+</sup> Classical Monocytes Predict Relapse Risk and Worsening in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the CNS characterized by a heterogeneous disease trajectory, highlighting the need for biomarkers to predict disease activity. Current disease-monitoring tools primarily reflect existing disease damage rather than impending activity. Peripheral blood mononuclear cells (PBMCs) are an ideal source of potential biomarkers due to their accessibility and their known role in MS pathology. Among PBMCs, myeloid cells are key players in MS pathogenic processes, yet they have not been as extensively studied than lymphocytes. The objective of our study was to identify indicators of MS disease activity through immune profiling. METHODS: We analyzed PBMCs using high-dimensional flow cytometry with a panel focusing on myeloid cells. We performed unsupervised clustering analyses to define a comprehensive immune landscape at a single-cell resolution. Supervised machine learning methods were used to extract immune features indicative of MS activity. RESULTS: CMs provided a stronger prognostic value for impending relapse risk, suggesting different kinetics related to the underlying pathology. DISCUSSION: CMs could serve as a valuable predictor of disease activity complementary to current clinical tools to guide evidence-based treatment decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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 teacher head, 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".