Fractional Gluconeogenesis: A Biomarker of Dietary Energy Adequacy in a Rat Brain Injury Model.

Patients treated for traumatic brain injury (TBI) are in metabolic crises because of the trauma and underfeeding. We utilized fractional gluconeogenesis (fGNG) to assess nutritional adequacy in ad libitum-fed and calorically-restricted rats following TBI. Male Sprague-Dawley individually housed rats 49 days of age were randomly assigned into four groups: ad libitum (AL) fed control (AL-Con, sham), AL plus TBI (AL+TBI), caloric restriction (CR) control (CR-Con, sham), and CR plus TBI (CR+TBI). From days 1-7 animals were given AL access to food and water containing 6% deuterium oxide (D2O). On day 8, a pre-intervention blood sample was drawn from each animal, and TBI, sham injury, and CR protocols were initiated. On day 22, the animals were euthanized, and blood was collected to measure fGNG. Pre-intervention, there was no significant difference in fGNG among groups (p ≥ 0.05). There was a significant increase in fGNG due to caloric restriction, independent of TBI (p ≤ 0.05). In addition, fGNG may provide a real-time, personalized biomarker for assessing patient dietary caloric needs.

First in vivo traumatic brain injury imaging via magnetic particle imaging.

Emergency room visits due to traumatic brain injury (TBI) is common, but classifying the severity of the injury remains an open challenge. Some subjective methods such as the Glasgow Coma Scale attempt to classify traumatic brain injuries, as well as some imaging based modalities such as computed tomography and magnetic resonance imaging. However, to date it is still difficult to detect and monitor mild to moderate injuries. In this report, we demonstrate that the magnetic particle imaging (MPI) modality can be applied to imaging TBI events with excellent contrast. MPI can monitor injected iron nanoparticles over long time scales without signal loss, allowing researchers and clinicians to monitor the change in blood pools as the wound heals.

Model based dynamics analysis in live cell microtubule images.

BACKGROUND: The dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data. RESULTS: In this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior. CONCLUSION: Computational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior.