Stable isotope labeling kinetics of neurofilament light <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Abstract Importance Neurofilament light (NfL) is elevated in CSF and blood across a range of traumatic, inflammatory and neurodegenerative diseases of the central nervous system, and has been increasingly included in clinical trials as an outcome measure of target engagement. Interpreting trajectories of NfL post-treatment has been challenging, prompting a greater need and focus on understanding its pathophysiology. Objective We measured NfL kinetics in the human central nervous system using stable isotope labeling kinetics (SILK). Design Observational study. Participants underwent SILK protocol. Infusion of 16 hours with 4mg/kg/h and follow-up lumbar punctures scheduled at 7, 14, 60 and 120 days post-labeling. Setting Referral center – specialist neurology clinic. Participants Participants with diagnosed primary tauopathies (n=10) were recruited to the Human CNS Tau Kinetics in Tauopathies (TANGLES) study. A control case was examined post-mortem to assess the technical background of the SILK method. Exposure Intravenous infusion of 13 C 6 -leucine, with rates of label incorporation representative of fractional synthesis and fractional clearance rates in vivo and in vitro . Main outcome and Measure Level of incorporation of 13 C 6 -leucine tracer into newly-translated NfL divided by the pool of NfL with previously incorporated 12 C 6 -leucine, expressed as a percentage tracer-to-tracee (TTR) ratio. Results NfL is rapidly translated in human brain within hours but takes 53 – 162 days to appear in cerebrospinal fluid (CSF). Labeled NfL remains detectable in post-mortem brain tissue 1.5 years post-labeling, indicating an extremely slow turnover in the human CNS. Together, these data suggest the greatest contribution of CSF NfL in neurodegeneration is from slow release of a large pool of previously translated NfL. However, release of newly translated NfL makes a significant contribution. Conclusion and relevance Rapid increases in CSF NfL seen within weeks of disease processes or interventions are likely to reflect release of pre-existing NfL from damaged neurons, but later increases in NfL (>3 months) may also reflect new NfL translation and release. Clinical trials using NfL as an outcome measure to track neurodegeneration would particularly benefit from substantially longer follow-up periods due to the slow turnover of the protein in the central nervous system.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".