Ultrasensitive immunoassay allows measurement of serum neurofilament heavy in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Neurofilament heavy (NfH) is a promising biomarker for neuro-axonal damage in Multiple Sclerosis (MS). We compared the performance of high-sensitivity serum-NfH immunoassays, with as aim to investigate the value of serum-NfH as biomarker for MS. METHODS: We measured serum-NfH in 76 MS patients with Simoa (one commercial, one in-house) or Luminex assays. Serum-NfH measured by the immunoassay with greatest sensitivity was related to clinical and radiological outcomes with age and sex-adjusted linear regression analysis, and to biological outcomes cerebrospinal fluid (CSF)-NfH, serum neurofilament light (NfL) and CSF-NfL with Spearman's correlation analysis. RESULTS: With the commercial Simoa assay, we obtained 100% serum-NfH detectability (in-house Simoa: 70%, Luminex: 61%), with lowest coefficient of variation (CV) between duplicates of 11%CV (in-house Simoa: 22%CV, Luminex: 30%CV). Serum-NfH quantified with the commercial Simoa assay was associated with disease duration (standardized beta (sβ) = 0.28, p = 0.034), T2 lesion volume (sβ = 0.23, p = 0.041), and tended to associate with black hole count (sβ = 0.21, p = 0.084) but not with Expanded Disease Disability Score (EDSS) or normalized brain volume (all: p>0.10). Furthermore, serum-NfH showed correlations with CSF-NfH (rho = 0.27, p = 0.018) and serum-NfL (rho=0.44, p < 0.001), but not with CSF-NfL. CONCLUSIONS: Serum-NfH can be quantified with high-sensitivity technology. Cross-sectionally, we observed some weak correlations of serum-NfH with MS disease burden parameters, suggesting there might be some utility for serum-NfH as biomarker for MS disease burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".