Prognostic serum biomarkers of synaptic, neuronal and glial injury in patients with acute ischemic stroke of the anterior circulation
Bibliographic record
Abstract
BACKGROUND: We aimed to investigate the prognostic role of β-synuclein in comparison to that of neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) for predicting functional outcome after acute ischemic stroke (AIS). METHODS: We measured serum concentrations of β-synuclein, NfL and GFAP 24 h after hospital admission in 213 consecutive patients with moderate-to-severe AIS. We investigated the association between serum biomarkers and radiological/clinical characteristics, 3-months mortality and functional outcome on the modified Rankin Scale (mRS). RESULTS: In 213 patients with AIS [mean age: 76.1 (±12.5) years, 53.1% males, median NIHSS score on admission: 13 (IQR: 9-17)], higher levels of β-synuclein, NfL and GFAP were associated with higher NIHSS scores and with lower Alberta Stroke Program CT Score (ASPECTS) points on admission. Serum β-synuclein levels was significantly correlated with NfL (rho = 0.715, p < 0.001) and GFAP concentrations (rho = 0.684, p < 0.001). The inclusion of serum β-synuclein significantly improved the accuracy of prediction models without biomarkers for overall mortality (AUC: 0.836 vs. 0.752, p < 0.001) and mRS 3-6 vs. 0-2 (AUC: 0.812 vs. 0.624, p < 0.001). Combination models with NfL and/or GFAP showed a similar accuracy. CONCLUSIONS: Serum β-synuclein may be used to assess synaptic damage/dysfunction and to predict 3-months clinical outcomes in patients with AIS.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".