Traumatic brain injury, posttraumatic stress disorder, and vascular risk are independently associated with white matter aging in Vietnam-Era veterans.

Objective: Traumatic brain injury (TBI), mental health conditions (e.g., posttraumatic stress disorder [PTSD]), and vascular comorbidities (e.

Serum Glutamate dehydrogenase activity enables sensitive and specific diagnosis of hepatocellular injury in humans.

Serum activities of alanine- and aspartate- aminotransferases (ALT and AST) are considered the "gold standard" biomarkers of hepatocyte injury in clinical practice and drug development. However, due to expression of ALT and AST in myocytes, the diagnosis of hepatocellular injury in patients with underlying muscle diseases, including drug-induced muscle injury, is severely limited. Thus, we proposed glutamate dehydrogenase (GLDH) as a liver-specific alternative to serum ALT and AST.

The Effects of Early Life History of TBI on the Progression of Normal Brain Aging with Implications for Increased Dementia Risk.

There is increasing interest in the risk conferred on neurological health by a traumatic brain injury (TBI) and how that influences the lifespan trajectory of brain aging. This chapter explores the importance of this issue, population, and methodological considerations, including injury documentation and outcome assessment. We then explore some of the findings in the neuroimaging and neuropsychological research literature examining the interaction between an earlier life history of TBI and the normal aging process.

Connectome-constrained networks predict neural activity across the fly visual system.

We can now measure the connectivity of every neuron in a neural circuit, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation.