Nanostructured neural interface coatings have significantly enhanced recording fidelity in both implantable and in vitro devices. As such, nano-porous gold (np-Au) has shown promise as a multifunctional neural interface coating due, in part, to its ability to promote nanostructure-mediated reduction in astrocytic surface coverage while not affecting neuronal coverage. The goal of this study is to provide insight into the mechanisms by which the np-Au nanostructure drives the differential response of neurons versus astrocytes in an in vitro model. Utilizing microfabricated libraries that display varying feature sizes of np-Au, it is demonstrated that np-Au influ-ences neural cell coverage through modulating focal adhesion formation in a feature size-dependent manner. The results here show that surfaces with small (≈30 nm) features control astrocyte spreading through inhibition of focal adhesion formation, while surfaces with large (≈170 nm and greater) features control astrocyte spreading through other mechanotransduction mechanisms. This cellular response combined with lower electrical impedance of np-Au electrodes significantly enhances the fidelity and stability of electrophysiological recordings from cortical neuronglia co-cultures relative to smooth gold electrodes. Finally, by leveraging the effect of nanostructure on neuronal versus glial cell attachment, the use of laser-based nanostructure modulation is demonstrated for selectively patterning neurons with micrometer spatial resolution.
In article 1604631, E. Seker and co‐workers apply nanoporous gold as a multifunctional biointerface toward improved long‐term neuron‐electrode coupling and neuronal cell patterning via nanotopographical cues. Lasercontrolled surface features allow for histological and electrophysiological tuned neuron response. Enhanced long‐term electrophysiological recording performance is achieved.
BackgroundBiological systems have complicated environmental conditions that vary both spatially and temporally. It becomes necessary to impose time-varying soluble factor concentrations to study such systems, including cellular responses to pharmaceuticals, inflammation with waxing and waning cytokine concentrations, as well as circadian rhythms and their metabolic manifestations. There is therefore a need for platforms that can achieve time-varying concentrations with arbitrary waveforms.ResultsTo address this need, we developed a microfluidic system that can deliver concentration waveforms in a fast and accurate manner by adopting concepts and tools from electrical engineering and fluid mechanics. Specifically, we employed pulse width modulation (PWM), a commonly used method for generating analog signals from digital sources. We implement this technique using three microfluidic components via laser ablation prototyping: low-pass filter (lower frequency signals permitted, high frequency signals blocked), resistor, and mixer. Each microfluidic component was individually studied and iteratively tuned to generate desired concentration waveforms with high accuracy. Using fluorescein as a small-molecule soluble factor surrogate, we demonstrated a series of concentration waveforms, including square, sawtooth, sinusoidal, and triangle waves with frequencies ranging from 100 mHz to 400 mHz.ConclusionWe reported the fabrication and characterization of microfluidic platform that can generate time-varying concentrations of fluorescein with arbitrary waveforms. We envision that this platform will enable a wide range of biological studies, where time-varying soluble factor concentrations play a critical role. In addition, the technology is expected to assist in the development of biomedical devices that allow precise dosing of pharmaceuticals for enhanced therapeutic efficacy and reduced toxicity.Electronic supplementary materialThe online version of this article (10.1186/s13036-018-0126-3) contains supplementary material, which is available to authorized users.
Abstract-In this paper, we introduce a new technique for partitioning a large-scale under-determined linear inverse problem into multiple smaller sub-problems that can be efficiently solved independently, and in parallel. When it is impossible or inefficient to solve a large-scale under-determined linear inverse problem, this technique can be used to significantly speed up the computation process without compromising the accuracy of the solution. We present numerical results that show the effectiveness of this approach when applied to network inference problems including traffic matrix estimation and network anomaly detection, both are important for managing large, complex networks and cyber-security. Our proposed framework is applicable to other emerging applications in computational intelligence that can be formulated as UDLI problems.
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