Identification of Novel Biomarkers of Brain Damage in Patients with Hemorrhagic Stroke by Integrating Bioinformatics and Mass Spectrometry-Based Proteomics
Bibliographic record
Abstract
Hemorrhagic stroke (HS) is a significant cause of mortality that requires rapid diagnosis and prompt medical attention. A time-efficient diagnostic test to assist in the early classification of patients with stroke would be of great value. The aims here were to (a) select "brain-specific" proteins using a bioinformatics approach, (b) develop selected reaction monitoring (SRM) assays for candidate proteins, and (c) quantify these proteins in cerebrospinal fluid (CSF). "The Human Protein Atlas" and the "Peptide Atlas" were used to select proteins specifically and abundantly expressed in brain tissue, excluding high-abundance plasma proteins. Protein extracts from brain tissue were used for SRM assay development of proteins of interest. The levels of 68 "brain-specific" proteins were measured by SRM in 36 age-matched patients, including individuals with HS (n = 15), ischemic stroke (n = 11), and controls (n = 10). Additionally, S100B was measured using an electrochemoluminometric immunoassay. CSF levels of S100B and eight of the "brain-specific" proteins (NSE, GFAP, α-Inx, MBP, MT3, NFM, β-Syn, and γ-Syn) were increased in a subset of samples from HS patients, especially in those individuals with intraventricular hemorrhage and poor outcome. Seven of these proteins (S100B, NSE, GFAP, α-Inx, MBP, NFM, and β-Syn) showed significant differences between patients with and without brain hemorrhage. Novel biomarkers of brain injury (α-Inx, NFM, and β-Syn) were identified in the CSF of patients with HS. Investigating the role of these proteins in blood with more sensitive methods is warranted.
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 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 | 0.004 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".