A novel cell type negatively associated with secondary autoimmunity in alemtuzumab-treated patients is revealed through single-cell longitudinal analysis of clinical trial samples
Bibliographic record
Abstract
Abstract Alemtuzumab, a humanized anti-CD52 monoclonal antibody, is an approved treatment for relapsing forms of multiple sclerosis (RMS). While its efficacy has been demonstrated in clinical studies, its use is associated with unpredictable non-MS autoimmunity manifesting months or years after treatment. Approximately 40% of treated patients present with autoimmune thyroid events, 2% with platelet deficiency (immune thrombocytopenia; ITP)1, and 0.34% with autoimmune nephropathies2. The lack of predictive biomarkers necessitates careful monitoring in clinical practice with a Risk Management Plan or Risk Evaluation and Mitigation Strategy (RMP/REMS) in place for early detection of these autoimmune events. We carried out a longitudinal single-cell analysis of PBMCs in a small subset of alemtuzumab-treated patients from the phase 3 CARE-MS I (CAMMS323) study3 and identified a novel platelet lineage cell negatively associated with thyroid autoimmunity. This discovery raises the possibility that a shared underlying mechanism may contribute to the incidence of thyroid autoimmunity and ITP in alemtuzumab-treated patients.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".