Identification of Serum Biomarkers in Brain-Injured Adults: Potential for Predicting Elevated Intracranial Pressure
Bibliographic record
Abstract
Brain injury biomarkers may have clinical utility in stratifying injury severity level, predicting adverse secondary events or outcomes, and monitoring the effectiveness of therapeutic interventions. As a biomarker source, serum offers several advantages over cerebrospinal fluid (CSF), including ease of accessibility and reduced risk to the patient. We screened pooled serum samples obtained from 11 severely injured traumatic brain injury (TBI) patients (Glasgow Coma Scale [GCS] <or= 8) and 11 age-, sex- and race-matched volunteers. Two time points-41.5 +/- 4.9 h and 66.3 +/- 6.6 h post-injury-were chosen for the initial screening analysis. Samples were immunodepleted for 12 highly abundant serum proteins, and then labeled with mass-balanced isobaric tags (iTRAQ), and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Identification and quantification of 2455 iTRAQ-labeled peptides that mapped to 160 proteins revealed 31 candidate biomarkers whose serum abundance was altered after injury. Changes in three candidate biomarkers (serum amyloid A, [SAA], c-reactive protein [CRP], retinol binding protein 4 [RBP4]) were verified using independent TBI and healthy volunteer serum samples. Receiver operating characteristic (ROC) curve analysis of CRP and SAA indicated they were robust indicators of injury even at very acute time points. Analysis of serum RBP4 levels at 24-36 h post-injury indicates it may predict subsequent increases in intracranial pressure (ICP) with a sensitivity of 86% and specificity of 88% at 11.6 mug/mL [n = 7, ICP < 20 mm Hg; n = 8, ICP > 25 mm Hg). Our results support the use of serum as a source for discovery of TBI biomarkers, and indicate that serum biomarkers may have utility for predicting secondary pathologies (e.g., elevated ICP) associated with TBI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".