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Background The use of blood biomarkers after mild traumatic brain injury (mTBI) has been widely studied. We have identified eight unresolved issues related to the use of five commonly investigated blood biomarkers: neurofilament light chain, ubiquitin carboxy-terminal hydrolase-L1, tau, S100B, and glial acidic fibrillary protein. We conducted a focused literature review of unresolved issues in three areas: mode of entry into and exit from the blood, kinetics of blood biomarkers in the blood, and predictive capacity of the blood biomarkers after mTBI. Findings Although a disruption of the blood brain barrier has been demonstrated in mild and severe traumatic brain injury, biomarkers can enter the blood through pathways that do not require a breach in this barrier. A definitive accounting for the pathways that biomarkers follow from the brain to the blood after mTBI has not been performed. Although preliminary investigations of blood biomarkers kinetics after TBI are available, our current knowledge is incomplete and definitive studies are needed. Optimal sampling times for biomarkers after mTBI have not been established. Kinetic models of blood biomarkers can be informative, but more precise estimates of kinetic parameters are needed. Confounding factors for blood biomarker levels have been identified, but corrections for these factors are not routinely made. Little evidence has emerged to date to suggest that blood biomarker levels correlate with clinical measures of mTBI severity. The significance of elevated biomarker levels thirty or more days following mTBI is uncertain. Blood biomarkers have shown a modest but not definitive ability to distinguish concussed from non-concussed subjects, to detect sub-concussive hits to the head, and to predict recovery from mTBI. Blood biomarkers have performed best at distinguishing CT scan positive from CT scan negative subjects after mTBI.

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as to the trajectories of blood biomarker levels over time after mild TBI. Modeling the kinetic behavior of these biomarkers could be informative. We propose a one-compartment kinetic model for S100B, UCH-L1, NF-L, GFAP, and tau biomarker levels after mild TBI based on accepted pharmacokinetic models for oral drug absorption. We approximated model parameters using previously published studies. Since parameter estimates were approximate, we did uncertainty and sensitivity analyses. Using estimated kinetic parameters for each biomarker, we applied the model to an available post-concussion biomarker dataset of UCH-L1, GFAP, tau, and NF-L biomarkers levels. We have demonstrated the feasibility of modeling blood biomarker levels after mild TBI with a one compartment kinetic model. More work is needed to better establish model parameters and to understand the implications of the model for diagnostic use of these blood biomarkers for mild TBI.

Objective: Neurologists lack a metric for measuring the distance between neurological patients. When neurological signs and symptoms are represented as neurological concepts from a hierarchical ontology and neurological patients are represented as sets of concepts, distances between patients can be represented as inter-set distances.Methods:We converted the neurological signs and symptoms from 721 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated inter-concept distances based a hierarchical ontology and we calculated inter-patient distances by semantic weighted bipartite matching. We evaluated the accuracy of a k-nearest neighbor classifier to allocate patients into 40 diagnostic classes.Results:Within a given diagnosis, mean patient distance differed by diagnosis, suggesting that across diagnoses there are differences in how similar patients are to other patients with the same diagnosis. The mean distance from one diagnosis to another diagnosis differed by diagnosis, suggesting that diagnoses differ in their proximity to other diagnoses. Utilizing a k-nearest neighbor classifier and inter-patient distances, the risk of misclassification differed by diagnosis.Conclusion:If signs and symptoms are converted to machine-readable codes and patients are represented as sets of these codes, patient distances can be computed as an inter-set distance. These patient distances given insights into how homogeneous patients are within a diagnosis (stereotypy), the distance between different diagnoses (proximity), and the risk of diagnosis misclassification (diagnostic error).

In order to develop an efficient, reproducible, and well-tolerated protocol for assessing corneal innervation, 11 normal subjects underwent corneal confocal microscopy (CCM) using a Heidelberg Retinal Tomography III microscope. Five standardized locations were sampled in the left eye and one centrally in the right. The protocol was repeated 1–4 weeks later. A blinded technician measured nerve fiber length (NFL) and tortuosity coefficient (TC). The relationship between image location and NFL and TC was assessed using one-way analysis of variance, and reproducibility determined using relative intertrial variability and intraclass correlation coefficients. NFL reproducibility was maximized by averaging four or more images from the left eye, or one central image from both eyes. TC was less reproducible. CCM is a rapid, well-tolerated, and reproducible method for assessing corneal innervation.

In order to develop an efficient, reproducible, and well-tolerated protocol for assessing corneal innervation, 11 normal subjects underwent corneal confocal microscopy (CCM) using a Heidelberg Retinal Tomography III microscope. Five standardized locations were sampled in the left eye and one centrally in the right. The protocol was repeated 1-4 weeks later. A blinded technician measured nerve fiber length (NFL) and tortuosity coefficient (TC). The relationship between image location and NFL and TC was assessed using one-way analysis of variance, and reproducibility determined using relative intertrial variability and intraclass correlation coefficients. NFL reproducibility was maximized by averaging four or more images from the left eye, or one central image from both eyes. TC was less reproducible. CCM is a rapid, well-tolerated, and reproducible method for assessing corneal innervation.