Neurofilament Proteins as Prognostic Biomarkers in Neurological Disorders
Why this work is in the frame
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Bibliographic record
Abstract
Neurofilaments: light, medium, and heavy (abbreviated as NF-L, NF-M, and NF-H, respectively), which belong to Type IV intermediate filament family (IF), are neuron-specific cytoskeletal components. Neurofilaments are axonal structural components and integral components of synapses, which are important for neuronal electric signal transmissions along the axons and post-translational modification. Abnormal assembly of neurofilaments is found in several human neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), infantile spinal muscular atrophy (SMA), and hereditary sensory-motor neuropathy (HSMN). In addition, those pathological neurofilament accumulations are known in α-synuclein in Parkinson's disease (PD), Aβ and tau in Alzheimer's disease (AD), polyglutamine in CAG trinucleotide repeat disorders, superoxide dismutase 1 (SOD1), TAR DNA-binding protein 43 (TDP43), neuronal FUS proteins, optineurin (OPTN), ubiquilin 2 (UBQLN2), and dipeptide repeat protein (DRP) in amyotrophic lateral sclerosis (ALS). When axon damage occurs in central nervous disorders, neurofilament proteins are released and delivered into cerebrospinal fluid (CSF), which are then circulated into blood. New quantitative analyses and assay techniques are well-developed for the detection of neurofilament proteins, particularly NF-L and the phosphorylated NF-H (pNF-H) in CSF and serum. This review discusses the potential of using peripheral blood NF quantities and evaluating the severity of damage in the nervous system. Intermediate filaments could be promising biomarkers for evaluating disease progression in different nervous system disorders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 it