Nanoporous Carbon Immunosensor for Highly Accurate and Sensitive Clinical Detection of Glial Fibrillary Acidic Protein in Traumatic Brain Injury, Stroke, and Spinal Cord Injury
Bibliographic record
Abstract
Elevated glial fibrillary acidic protein (GFAP) in the blood serum is one of the promising bodily fluid markers for the diagnosis of central nervous system (CNS) injuries, including traumatic brain injury (TBI), stroke, and spinal cord injury (SCI). However, accurate and point-of-care (POC) quantification of GFAP in clinical blood samples has been challenging and yet to be clinically validated against gold-standard assays and outcome practices. This work engineered and characterized a novel nanoporous carbon screen-printed electrode with significantly increased surface area and conductivity, as well as preserved stability and anti-fouling properties. This nano-decorated electrode was immobilized with the target GFAP antibody to create an ultrasensitive GFAP immunosensor and quantify GFAP levels in spiked samples and the serum of CNS injury patients. The immunosensor presented a dynamic detection range of 100 fg/mL to 10 ng/mL, a limit of detection of 86.6 fg/mL, and a sensitivity of 20.3 Ω mL/pg mm 2 for detecting GFAP in the serum. Its clinical utility was demonstrated by the consistent and selective quantification of GFAP comparable to the ultrasensitive single-molecule array technology in 107 serum samples collected from TBI, stroke, and SCI patients. Comparing the diagnostic and prognostic performance of the immunosensor with the existing clinical paradigms confirms the immunosensor’s accuracy as a potential complement to the existing imaging diagnostic modalities and presents a potential for rapid, accurate, cost-effective, and near real-time POC diagnosis and prognosis of CNS injuries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".