Serum β-synuclein, neurofilament light chain and glial fibrillary acidic protein as prognostic biomarkers in moderate-to-severe acute ischemic stroke
Bibliographic record
Abstract
We aimed to assess the prognostic value of serum β-synuclein (β-syn), neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in patients with moderate-to-severe acute ischemic stroke. We measured β-syn, GFAP and NfL in serum samples collected one day after admission in 30 adult patients with moderate-to-severe ischemic stroke due to middle cerebral artery (MCA) occlusion. We tested the associations between biomarker levels and clinical and radiological scores (National Institute of Health Stroke Scale scores, NIHSS, and Alberta Stroke Program Early CT Score, ASPECTS), as well as measures of functional outcome (modified Rankin Scale, mRS). Serum biomarkers were significantly associated with ASPECTS values (β-syn p = 0.0011, GFAP p = 0.0002) but not with NIHSS scores at admission. Patients who received mechanical thrombectomy and intravenous thrombolysis showed lower β-syn (p = 0.029) und NfL concentrations (p = 0.0024) compared to patients who received only mechanical thrombectomy. According to median biomarker levels, patients with high β-syn, NfL or GFAP levels showed, after therapy, lower clinical improvement (i.e., lower 24-h NIHSS change), higher NIHSS scores during hospitalization and higher mRS scores at 3-month follow-up. Elevated serum concentrations of β-syn (p = 0.016), NfL (p = 0.020) or GFAP (p = 0.010) were significantly associated with 3-month mRS of 3-6 vs. 0-2 even after accounting for age, sex and renal function. In patients with moderate-to-severe acute ischemic stroke, serum β-syn, NfL and GFAP levels associated with clinical and radiological scores at different timepoints and were able to predict short- and middle-term clinical outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".