Identifying novel soluble biomarkers in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
Relapsing-remitting multiple sclerosis (RRMS) is a chronic immune-mediated inflammatory disease characterized by central nervous system (CNS) demyelination and axonal damage. Under current guidelines, a multiple sclerosis (MS) diagnosis most often occurs over the course of months and requires clinical assessment, magnetic resonance imaging (MRI), and a lumbar puncture. While clinically useful diagnostic, prognostic, and disease monitoring biomarkers do exist, they share commonalities with many other autoimmune and/or neurodegenerative disorders. As a result, this process leaves patients waiting for critical healthcare services. The objective of this thesis is to identify novel candidate biomarkers in blood plasma of MS patients and elucidate pathophysiological disease mechanisms in RRMS. Blood plasma represents an accessible body fluid harboring many immune-related molecules that may inform on RRMS disease status and ongoing systemic pathological mechanisms. In this thesis, interleukin-1 receptor antagonist (IL-1RA) was identified as a plasma-based biomarker for increased disability in RRMS that is released from macrophages and microglia in active areas of lesions during activation of an inflammasome. Blood plasma of RRMS cases was also used to investigate the patterns of circulating extracellular vesicles (EVs). It was determined that RRMS cases have higher levels of immune cell derived EVs in circulation compared to healthy controls, and that this was unrelated to numbers of circulating parent cell populations. Finally, cerebrospinal fluid samples were analysed for 27 cytokines, and identified few differences in RRMS compared to non-inflammatory neurological disease controls. CXCL10 levels were significantly increased but were not associated with its most welliii known function of immune cell chemotaxis. Instead, an alternative pathological mechanism whereby CXCL10 leads to downregulation of glutamate transporters on astrocytes was identified. This thesis highlights the wealth of information to be gained from studying body fluid-based biomarkers of ongoing inflammatory activity in RRMS and identifies three exploratory biomarkers for which future studies will be based on. These future works should focus on determining the sensitivity and specificity of these molecules in MS prospectively, longitudinally and across the disability and disease spectrum. Furthermore, future studies will work any functional mechanisms that are modulated by IL-1RA, immune cell derived EVs and CXCL10.
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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 | 0.000 |
| 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) | 0.001 | 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".