Development and multi-center validation of a fully automated digital immunoassay for neurofilament light chain: toward a clinical blood test for neuronal injury
Bibliographic record
Abstract
OBJECTIVES: Neurofilament light chain (NfL) has emerged as a promising biomarker for detecting and monitoring axonal injury. Until recently, NfL could only be reliably measured in cerebrospinal fluid, but digital single molecule array (Simoa) technology has enabled its precise measurement in blood samples where it is typically 50-100 times less abundant. We report development and multi-center validation of a novel fully automated digital immunoassay for NfL in serum for informing axonal injury status. METHODS: A 45-min immunoassay for serum NfL was developed for use on an automated digital analyzer based on Simoa technology. The analytical performance (sensitivity, precision, reproducibility, linearity, sample type) was characterized and then cross validated across 17 laboratories in 10 countries. Analytical performance for clinical NfL measurement was examined in individual patients with relapsing remitting multiple sclerosis (RRMS) after 3 months of disease modifying treatment (DMT) with fingolimod. RESULTS: The assay exhibited a lower limit of detection (LLoD) of 0.05 ng/L, a lower limit of quantification (LLoQ) of 0.8 ng/L, and between-laboratory imprecision <10 % across 17 validation sites. All tested samples had measurable NfL concentrations well above the LLoQ. In matched pre-post treatment samples, decreases in NfL were observed in 26/29 RRMS patients three months after DMT start, with significant decreases detected in a majority of patients. CONCLUSIONS: The sensitivity characteristics and reproducible performance across laboratories combined with full automation make this assay suitable for clinical use for NfL assessment, monitoring in individual patients, and cross-comparisons of results across multiple sites.
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".