Neurofilament medium polypeptide (NFM) protein concentration is increased in CSF and serum samples from patients with brain injury
Bibliographic record
Abstract
BACKGROUND: Brain injury is a medical emergency that needs to be diagnosed and treated promptly. Several proteins have been studied as biomarkers of this medical condition. The aims of this study were to: 1) evaluate the selectivity and precision of a commercial ELISA kit for neurofilament medium polypeptide (NFM) protein; and 2) evaluate the concentration in cerebrospinal fluid (CSF) and serum of healthy individuals and patients with brain damage. METHODS: An ELISA from Elabscience was used. The selectivity was evaluated using size-exclusion chromatography and mass spectrometry. Intra- and inter-batch coefficients of variation (CV) were also studied. Fifty-one CSF samples from 36 age-matched patients with hemorrhagic stroke (HS) (n=30), ischemic stroke (IS) (n=11) and healthy individuals (n=10) were assayed. In addition, serum samples from healthy volunteers (n=47), 68 serum samples from seven patients with HS, 106 serum samples from 12 patients with traumatic brain injury (TBI) and 68 serum samples from 68 patients with mild traumatic brain injury (mTBI) were also analyzed. RESULTS: NFM was identified in the chromatographic fraction with highest immunoreactivity. The intra- and inter-batch CVs were ≤10% and ≤13%, respectively. The CSF-NFM concentration in HS was significantly higher (p<0.0001) than in IS and controls. Serum NFM concentration ranged from 0.26 to 8.57 ng/mL in healthy individuals (median=2.29), from 0.97 to 42.4 ng/mL in HS (median=10.8) and from 3.48 to 45.4 ng/mL in TBI (median=14.7). Finally, 44% of patients with mTBI had increased NFM concentration, with significantly higher levels (p=0.01) in patients with polytrauma. CONCLUSIONS: To our knowledge this is the first study describing increased NFM levels in CSF and serum from patients with brain damage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".