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Die kondensierte Fassung: Thioliertes Glycolchitosan bildet durch Ladungs‐Ladungs‐Wechselwirkungen und Netzbildung mit polymerisierten siRNAs stabile Nanopartikel (siehe Schema). Die Poly‐siRNA/Glycolchitosan‐Nanopartikel (psi‐TGC) sind in vivo genügend stabil für den systemischen Transport von siRNAs. Das Abschalten von Tumorproteinen durch psi‐TGC resultierte in reduzierten Tumorgrößen und verminderter Tumorvaskularisation.

This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.

Recently, implantable devices have become widely used in neural prostheses because they eliminate endemic drawbacks of conventional percutaneous neural interface systems. However, there are still several issues to be considered: low-efficiency wireless power transmission; wireless data communication over restricted operating distance with high power consumption; and limited functionality, working either as a neural signal recorder or as a stimulator. To overcome these issues, we suggest a novel implantable wireless neural interface system for simultaneous neural signal recording and stimulation using a single cuff electrode. By using widely available commercial off-the-shelf (COTS) components, an easily reconfigurable implantable wireless neural interface system was implemented into one compact module. The implantable device includes a wireless power consortium (WPC)-compliant power transmission circuit, a medical implant communication service (MICS)-band-based radio link and a cuff-electrode path controller for simultaneous neural signal recording and stimulation. During in vivo experiments with rabbit models, the implantable device successfully recorded and stimulated the tibial and peroneal nerves while communicating with the external device. The proposed system can be modified for various implantable medical devices, especially such as closed-loop control based implantable neural prostheses requiring neural signal recording and stimulation at the same time.

Cuff electrode recording has been proposed as a solution to obtain robust feedback signals for closed-loop controlled functional neuromuscular stimulation (FNS) systems. However, single-channel cuff electrode recording requires several electrodes to obtain the feedback signal related to each muscle. In this study, we propose an ankle-angle estimation method in which recording is conducted from the proximal nerve trunk with a multichannel cuff electrode to minimize cuff electrode usage. In experiments, muscle afferent signals were recorded from a rabbit's proximal sciatic nerve trunk using a multichannel cuff electrode, and blind source separation and ankle-angle estimation were performed using fast independent component analysis (PP/FastICA) combined with dynamically driven recurrent neural network (DDRNN). The experimental results indicate that the proposed method has high ankle-angle estimation accuracy for both situations when the ankle motion is generated by position servo system or neuromuscular stimulation. Furthermore, the results confirm that the proposed method is applicable to closed-loop FNS systems to control limb motion.