Electrochemical Sensor for the Diagnosis of Traumatic Injuries of the Central Nervous System
Bibliographic record
Abstract
Injuries of the central nervous system (CNS) such as traumatic brain injuries (TBI) and traumatic spinal cord injury (tSCI) are widespread. Approximately, 170,000 individuals in Canada, suffers these injuries every year. Majority of the patients are left permanently disabled with limited restorative treatment and the cost of these CNS injuries is approximately $15billion a year to the Canadian economy. One of the ways to accurately diagnose and manage these injuries for the effective outcome is by the detection of Biomarkers. Concentrations of biomarkers are found to be critical and provide vital information of health and healing of tissues. It is found that concentrations of several biomarkers associated with the injury of Neuron, Microglia, Astrocytes like SBDP, GFAP, S100β, etc. increases significantly post injury in blood and in CSF compared to uninjured state. Accurately measuring these biomarkers at the point of care can help in assessing the heath and healing of the CNS during injury and while treating the injury. At present there is no point of care sensing device available for measuring these biomarkers for diagnosis and management of these injuries. We report a novel screen printed graphene based electrochemical biosensor for sensing S100β for the diagnosis and management of the CNS injury. Median concentration of S100β before injury is approximately 45pg/ml while post injury it increases five folds to 240pg/ml. We detected S100β in the dynamic range of 1pg/ml to 1ng/ml using this biosensor.
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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 | 0.000 | 0.000 |
| 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".