Quantitation of neurofilament light chain protein in serum and cerebrospinal fluid from patients with multiple sclerosis using the MSD R-PLEX NfL assay
Bibliographic record
Abstract
Abstract Objectives Neurofilament light (NfL) chain is a marker of neuroaxonal damage in various neurological diseases. Here we quantitated NfL levels in the cerebrospinal fluid (CSF) and serum from patients with multiple sclerosis (MS) and controls, using the R-PLEX NfL assay, which employs advanced Meso Scale Discovery ® (MSD) electrochemiluminescence (ECL)-based detection technology. Methods NfL was quantitated in samples from 116 individuals from two sites (Ottawa Hospital Research Institute and Mayo Clinic), consisting of patients with MS (n=71) and age- and sex-matched inflammatory neurological controls (n=13) and non-inflammatory controls (n=32). Correlation of NfL levels between CSF and serum was assessed in paired samples in a subset of MS patients and controls (n=61). Additionally, we assessed the correlation between NfL levels obtained with MSD’s R-PLEX ® and Quanterix’s single molecule array (Simoa ® ) assays in CSF and serum (n=32). Results Using the R-PLEX, NfL was quantitated in 99% of the samples tested, and showed a broad range in the CSF (82–500,000 ng/L) and serum (8.84–2,014 ng/L). Nf-L levels in both biofluids correlated strongly (r=0.81, p<0.0001). Lastly, Nf-L measured by MSD’s R-PLEX and Quanterix’s Simoa assays were highly correlated for both biofluids (CSF: r=0.94, p<0.0001; serum: r=0.95, p<0.0001). Conclusions We show that MSD’s R-PLEX NfL assay can reliably quantitate levels of NfL in the CSF and serum from patients with MS and controls, where levels correlate strongly with Simoa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".