Plasma Protein Profiling by Proximity Extension Assay Technology Reveals Novel Biomarkers of Traumatic Brain Injury—A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is a significant public health issue affecting nearly 69 million patients worldwide per year. Reliable diagnostic biomarkers are urgently needed to aid in disease diagnosis and prognosis and to guide patient aftercare. Blood biomarkers represent an attractive modality to quickly, cheaply, and objectively evaluate clinical status. We hypothesize that deep and quantitative plasma proteomic profiling with a novel technology, proximity extension assay, may lead to the discovery of diagnostic and/or prognostic biomarkers of TBI. METHODS: We used high-throughput proximity extension assays (PEA) to quantify the relative abundance of over 1000 unique proteins in plasma. PEA is a highly sensitive multiplex immunoassay capable of detecting very low-abundance proteins (down to fg/mL) in complex biological matrices. Our patient cohort consisted of severe TBI (sTBI) patients, matched healthy controls, and another non-TBI group that was included in the analysis to validate the specificity of the candidates during the selection process. The obtained protein quantification data was then filtered to identify candidate biomarkers through statistical analysis, literature searches, and comparison to our reference control groups. RESULTS: Overall, we identified 6 novel candidate TBI biomarkers. Candidates exhibit a significant increase in plasma protein abundance in sTBI when comparing between healthy controls and sTBI patients. Candidates generally had low expression in our reference groups compared with the sTBI group. CONCLUSIONS: Our preliminary findings represent a starting point for future validation. These biomarkers, either alone or in combination, may have significant clinical utility in aiding in TBI diagnosis, prognosis, and/or management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".