Serum Neuroglobin as a Potential Prognostic Biomarker for Cognitive Impairment After Intracerebral Hemorrhage
Bibliographic record
Abstract
Objective Stroke is closely related to dementia, but there are few prospective studies on cognitive decline after stroke in patients with cerebral hemorrhage. Neuroglobin is an oxygen-binding protein mainly expressed in brain neurons. The aim of our current study was to determine whether neuroglobin could serve as a biomarker for cognitive prognosis in patients with intracerebral hemorrhage (ICH). Methods Three hundred and sixteen patients with ICH were consecutively enrolled in a prospective study. Baseline data such as age and gender of ICH patients on admission were recorded. Serum neuroglobin concentrations were determined by enzyme-linked immunosorbent assay (ELISA). All ICH patients 3 months after onset were divided into post-stroke cognitive impairment group (PSCI) and non-PSCI group according to MoCA assessment results. Results The PSCI and Non-PSCI groups had serum neuroglobin concentrations of (4.7 ± 0.9) and (7.5 ± 1.1) ng/ml, respectively, with a statistically significant difference between the two groups (p < 0.05). Age, gender, LDL, FBG, SBP, DBP, NHISS, and Hematoma volume were found to be adversely connected with MoCA (p < 0.05), while education, HDL, and serum neuroglobin were found to be positively correlated with MoCA (p < 0.05). After controlling for baseline data, regression analysis revealed that serum neuroglobin was remained an efficient biomarker for predicting cognitive performance in individuals with ICH (p < 0.05). The diagnostic accuracy of blood neuroglobin concentration for PSCI in ICH patients was 72.6%, the sensitivity was 67.4%, and the specificity was 75.5%, according to receiver operating characteristic (ROC) curve analysis. Conclusions Serum neuroglobin may serve as a potential biomarker to predict cognitive decline after ICH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".