Investigating T-cell-derived extracellular vesicles as biomarkers of disease activity, axonal injury, and disability in multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis (MS) is a chronic immune-mediated demyelinating disease of the central nervous system, whereby clinical disease activity is primarily monitored by magnetic resonance imaging. METHODS: Given the limitations associated with implementing and acquiring novel and emerging imaging biomarkers in routine clinical practice, the discovery of biofluid biomarkers may offer a more simple and cost-effective measure that would improve accessibility, standardization, and patient care. Extracellular vesicles (EVs) are nanoparticles secreted from cells under both homeostatic and pathological states, and have been recently investigated as biomarkers in MS. The objectives of this study were to longitudinally measure levels of specific immune cell-derived EVs in MS and provide evidence that EV sub-populations may serve as biomarkers of disease activity, axonal injury, and/or clinical disability. RESULTS: Our results demonstrate that the rate of clinical disability in MS negatively correlates with changes in circulating CD3+ EVs within the plasma. Additionally, numbers of CD4+ EVs decrease in individuals with increasing pNfL levels overtime whereby the magnitude of the pNfL increase negatively correlates with changes in plasma CD4+ and CD8+ EVs. Finally, when applying NEDA-3 criteria to define active versus stable disease, individuals with active disease had significantly elevated CD4+ and CD8+ EVs compared to stable disease. CONCLUSION: In summary, the analysis of specific immune cell-derived EV subsets may provide a method to monitor disability accumulation, disease activity, and axonal injury in MS, while also providing insights into the pathophysiology and cellular/molecular mechanisms that influence progression.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".