My46: a Web-based tool for self-guided management of genomic test results in research and clinical settings.

A major challenge to implementing precision medicine is the need for an efficient and cost-effective strategy for returning individual genomic test results that is easily scalable and can be incorporated into multiple models of clinical practice. My46 is a Web-based tool for managing the return of genetic results that was designed and developed to support a wide range of approaches to disclosing results, ranging from traditional face-to-face disclosure to self-guided models. My46 has five key functions: set and modify results-return preferences, return results, educate, manage the return of results, and assess the return of results. These key functions are supported by six distinct modules and a suite of features that enhance the user experience, ease site navigation, facilitate knowledge sharing, and enable results-return tracking. My46 is a potentially effective solution for returning results and supports current trends toward shared decision making between patients and providers and patient-driven health management.Genet Med 19 4, 467-475.

Initial comparison of energy measures for neural stimulation in a single conductance channel.

This paper considers the utility of several alternative energy measures to reduce the energy required by a stimulation current source to charge a neuron membrane capacitance to a prescribed value in the case of a single sodium channel. For a simple case, minimizing the energy of the nonlinear channel conductance provides improved efficiency in terms of stimulator energy as compared to minimizing a squared-integral measure of the stimulation current. This work lays the foundation for expanding this investigation to a full conductance-based Hodgkin-Huxley model.

Effects of acetylcholine and electrical stimulation on glial cell line-derived neurotrophic factor production in skeletal muscle cells.

Glial cell line-derived neurotrophic factor (GDNF) is a neurotrophic factor required for survival of neurons in the central and peripheral nervous system. Specifically, GDNF has been characterized as a survival factor for spinal motor neurons. GDNF is synthesized and secreted by neuronal target tissues, including skeletal muscle in the peripheral nervous system; however, the mechanisms by which GDNF is synthesized and released by skeletal muscle are not fully understood. Previous results suggested that cholinergic neurons regulate secretion of GDNF by skeletal muscle. In the current study, GDNF production by skeletal muscle myotubes following treatment with acetylcholine was examined. Acetylcholine receptors on myotubes were identified with labeled alpha-bungarotoxin and were blocked using unlabeled alpha-bungarotoxin. The question of whether electrical stimulation has a similar effect to that of acetylcholine was also investigated. Cells were stimulated with voltage pulses; at 1 and 5 Hz frequencies for times ranging from 30 min to 48 h. GDNF content in myotubes and GDNF in conditioned culture medium were quantified by enzyme-linked immunosorbant assay. Results suggest that acetylcholine and short-term electrical stimulation reduce GDNF secretion, while treatment with carbachol or long-term electrical stimulation enhances GDNF production by skeletal muscle.

Computation of reduced energy input current stimuli for neuron phase models.

A regularly spiking neuron can be studied using a phase model. The effect of an input stimulus current on the phase time derivative is captured by a phase response curve. This paper adapts a technique that was previously applied to conductance-based models to discover optimal input stimulus currents for phase models. First, the neuron phase response θ(t) due to an input stimulus current i(t) is computed using a phase model. The resulting θ(t) is taken to be a reference phase r(t). Second, an optimal input stimulus current i(*)(t) is computed to minimize a weighted sum of the square-integral `energy' of i(*)(t) and the tracking error between the reference phase r(t) and the phase response due to i(*)(t). The balance between the conflicting requirements of energy and tracking error minimization is controlled by a single parameter. The generated optimal current i(*)t) is then compared to the input current i(t) which was used to generate the reference phase r(t). This technique was applied to two neuron phase models; in each case, the current i(*)(t) generates a phase response similar to the reference phase r(t), and the optimal current i(*)(t) has a lower `energy' than the square-integral of i(t). For constant i(t), the optimal current i(*)(t) need not be constant in time. In fact, i(*)(t) is large (possibly even larger than i(t)) for regions where the phase response curve indicates a stronger sensitivity to the input stimulus current, and smaller in regions of reduced sensitivity.