A.02 Serum biomarkers of MS disease activity in patients treated with bone marrow transplant
Bibliographic record
Abstract
Background: There is an unmet need for blood-based biomarkers that can reliably detect MS disease activity. Serum Biomarkers of interest includ Neurofilament-light-chain (NfL), Glial-fibrillary-strocyte-protein(GFAP) and Tau. Bone Marrow Transplantation (BMT) is reserved for aggressive forms of MS and has been shown to halt detectable CNS inflammatory activity for prolonged periods. Significant pre-treatment tissue damage at followed by inflammatory disease abeyance should be reflected longitudinal sera collected from these patients. Methods: Sera were collected from 23 MS patients pre-treatment, and following BMT at 3, 6, 9 and 12-months in addition from 33 non-inflammatory neurological controls. Biomarker quantification was performed with SiMoA. Results: Pre-AHSCT levels of serum NfL and GFAP but not Tau were elevated compared to controls (p=0.0001), and NfL correlated with lesion-based disease activity (6-month-relapse, MRI-T2 and Gadolinium-enhancement). 3-months post-treatment, while NfL levels remained elevated, Tau/GFAP paradoxically increased (p=0.0023/0.0017). These increases at 3m correlated with MRI ‘pseudoatrophy’ at 6-months. NfL/Tau levels dropped to that of controls by 6-months (p=0.0036/0.0159). GFAP levels dropped progressively after 6-months although even at 12-months remained higher than controls (p=0.004). Conclusions: NfL was the closest correlate of MS disease activity and treatment response. Chemotherapy-related toxicity may account for transient increases in NfL, Tau and MRI brain atrophy post-BMT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".