Serum proteomics as a strategy to identify novel biomarkers of neurologic recovery after cardiac arrest: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Serum biomarkers may play a role in prognostication after cardiac arrest. This study was designed to assess the feasibility of using two-dimensional gel electrophoresis (2D-GE) coupled with mass spectrometry (MS) as a proteomic strategy to identify novel biomarkers that may predict neurological recovery. METHODS: Adult comatose survivors of ventricular fibrillation or pulseless ventricular tachycardia were considered eligible. Blood was collected and serum separated within 6 h of hospital admission and then at 24 h afterwards. Neurological outcome was assessed at 3 months with the Cerebral Performance Category (CPC) score. Serum was assessed with 2D-GE with and without prior depletion of high abundance proteins. Protein differences between patients with good (CPC 1,2) vs. poor (CPC 3-5) neurological recovery were subsequently identified with MS. RESULTS: From August 2010 to June 2014, 11 patients meeting eligibility criteria were recruited, from which serum was available from 9 (5 with good neurological outcome). On non-depleted serum, only high abundance acute phase proteins such as haptoglobin, cell-free hemoglobin, albumin, and amyloid were detected in both patients with good and poor neurological recovery. Following depletion of high abundance proteins, proteins identified by MS in both patient populations were the acute phase reactants c-reactive protein and retinol binding protein-4. Proteins uniquely identified in the serum of patients with poor neurological recovery included 14-3-3 (epsilon and zeta isoforms) and muskelin. CONCLUSIONS: Two-D-GE coupled with MS is a feasible strategy to facilitate the identification of novel predictive biomarkers. The presence of muskelin and 14-3-3 in the serum of patients with poor neurological prognosis warrants further investigation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".