Stroke is a leading cause of death and disability in the USA. Up to 60% of patients do not fully recover despite intensive physical therapy treatment. N-Methyl-D-aspartate receptors (NMDA-R) have been shown to play a role in synaptic plasticity when activated. D-Cycloserine promotes NMDA receptor function by binding to receptors with unoccupied glycine sites. These receptors are involved in learning and memory. We hypothesized that D-cycloserine, when combined with robotic-assisted physiotherapy (RAP), would result in greater gains compared with placebo + RAP in stroke survivors. Participants (n = 14) were randomized to D-cycloserine plus RAP or placebo plus RAP. Functional, cognitive, and quality-of-life measures were used to assess recovery. There was significant improvement in grip strength of the affected hand within both groups from baseline to 3 weeks (95% confidence interval for mean change, 3.95 ± 2.96 to 4.90 ± 3.56 N for D-cycloserine and 5.72 ± 3.98 to 8.44 ± 4.90 N for control). SIS mood domain showed improvement for both groups (95% confidence interval for mean change, 72.6 ± 16.3 to 82.9 ± 10.9 for D-cycloserine and 82.9 ± 13.5 to 90.3 ± 9.9 for control). This preliminary study does not provide evidence that D-cycloserine can provide greater gains in learning compared with placebo for stroke survivors.

The Centers for Disease Control estimate that 1.6 to 3.8 million concussions occur in sports and recreational activities annually. Studies have shown that concussions increase the risk of future injuries and mild cognitive disorders. Despite extensive research on sports related concussion risk factors, the factors which are most predictive of concussion outcome and recovery time course remain unknown. In order to overcome the issue of physician bias and to identify the factors which can best predict concussion diagnosis, we propose a multi-variate logistic regression based analysis. We demonstrate our results on a dataset with 126 subjects (ages 12-31). Our results indicate that among 322 features, our model selected 27-29 features which included a history of playing sports, history of a previous concussion, drowsiness, nausea, trouble focusing as measured by a common symptom list, and oculomotor function. The features picked using our model were found to be highly predictive of concussions and gave a prediction performance accuracy greater than 90%, Matthews correlation coefficient greater than 0.8 and the area under the curve greater than 0.95.

Mild traumatic brain injury (mTBI) remains a diagnostic challenge and therefore strategies for objective assessment of neurological function are key to limiting long-term sequelae. Current assessment methods are not optimal in austere environments such as athletic fields; therefore, we developed an immersive tool, the Display Enhanced Testing for Cognitive Impairment and mTBI (DETECT) platform, for rapid objective neuropsychological (NP) testing. The objectives of this study were to assess the ability of DETECT to accurately identify neurocognitive deficits associated with concussion and evaluate the relationship between neurocognitive measures and subconcussive head impacts. DETECT was used over a single season of two high school and two college football teams. Study participants were instrumented with Riddell Head Impact Telemetry (HIT) sensors and a subset tested with DETECT immediately after confirmed impacts for different combinations of linear and rotational acceleration. A total of 123 athletes were enrolled and completed baseline testing. Twenty-one players were pulled from play for suspected concussion and tested with DETECT. DETECT was 86.7% sensitive (95% confidence interval [CI]: 59.5%, 98.3%) and 66.7% specific (95% CI: 22.3%, 95.7%) in correctly identifying athletes with concussions (15 of 21). Weak but significant correlations were found between complex choice response time (processing speed and divided attention) and both linear (Spearman rank correlation coefficient 0.262, p = 0.02) and rotational (Spearman coefficient 0.254, p = 0.03) acceleration on a subset of 76 players (113 DETECT tests) with no concussion symptoms. This study demonstrates that DETECT confers moderate to high sensitivity in identifying acute cognitive impairment and suggests that football impacts that do not result in concussion may negatively affect cognitive performance immediately following an impact. Specificity, however, was not optimal and points to the need for additional studies across multiple neurological domains. Given the need for more objective concussion screening in triage situations, DETECT may provide a solution for mTBI assessment.

Traumatic brain injury (TBI) poses a major health challenge, with tens of millions of new cases reported globally every year. Brain damage resulting from TBI can vary significantly due to factors including injury severity, injury mechanism and exposure to repeated injury events. There-fore, there is need for robust blood biomarkers. Serum from Sprague Dawley rats was collected at several timepoints within 24 h of mild single or repeat closed head impacts. Serum samples were analyzed via ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) in positive and negative ion modes. Known lipid species were identified through matching to in-house tandem MS databases. Lipid biomarkers have a unique potential to serve as objective molecular measures of injury response as they may be liberated to circulation more readily than larger protein markers. Machine learning and feature selection approaches were used to construct lipid panels capable of distinguishing serum from injured and uninjured rats. The best multivariate lipid panels had over 90% cross-validated sensitivity, selectivity, and accuracy. These mapped onto sphin-golipid signaling, autophagy, necroptosis and glycerophospholipid metabolism pathways, with Benjamini adjusted p-values less than 0.05. The novel lipid biomarker candidates identified provide insight into the metabolic pathways altered within 24 h of mild TBI.