The condensed version: Thiolated glycol chitosan can form stable nanoparticles with polymerized siRNAs through charge-charge interactions and self-cross-linking (see scheme). This poly-siRNA/glycol chitosan nanoparticles (psi-TGC) provided sufficient in vivo stability for systemic delivery of siRNAs. Knockdown of tumor proteins by psi-TGC resulted in a reduction in tumor size and vascularization.

Objective. Early treatment based on an early diagnosis of rheumatoid arthritis (RA) could halt progression of the disease, but early diagnosis is often difficult. Matrix metalloproteinase 3 (MMP-3) is thought to be particularly important in the pathogenesis of RA. The aim of this study was to investigate whether an MMP-3-specific polymeric probe could be used for early diagnosis and for visualizing the progression of arthritis, using a near-infrared fluorescence (NIRF) imaging system.Methods. The MMP-3-specific polymeric probe was developed by conjugating NIRF dye, MMP substrate peptide, and dark quencher to self-assembled chitosan nanoparticles. One hour after intravenous administration of the probe, fluorescent images of mice with collagen-induced arthritis at different stages of disease development were obtained. The correlation between the fluorescence recovered in in vivo imaging when using an MMP-3-specific polymeric probe and up-regulated MMP-3 activity in the joint tissues was evaluated by Western blotting and immunohistochemical staining. Histologic analysis and micro-computed tomography (micro-CT) were also used to assess arthritis progression.Results. A significantly higher NIRF signal was recovered from arthritic joints compared with normal joints at 14 days after the first immunization, before any erythema or swelling could be observed with the naked eye or any erosion was detected by histologic analysis or micro-CT. The results of immunohistochemical analysis and Western blotting confirmed that the fluorescence recovered in the in vivo imaging was related to upregulated MMP-3 activity in the joint tissues.Conclusion. An MMP-3-specific polymeric probe provided clear early diagnosis of arthritis and visualization of arthritis progression using an NIRF imaging system. This approach could be used for early diagnosis and for monitoring drug and surgical therapies in individual cases.

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Cuff electrodes are effective for chronic electroneurogram (ENG) recording while minimizing nerve damage. However, the ENG signals are usually contaminated by electromyogram (EMG) activity from the surrounding muscles, stimulus artifacts produced by the electrical stimulation and noise generated in the first stage of the neural signal amplifier. This paper proposed a new cuff electrode to reduce the interference from EMG signals and stimulus artifacts. As a result, when an additional middle electrode was placed at the center of the cuff electrode, a significant improvement in the signal-to-interference ratio was achieved at 11% for the EMG signals and 12% for the stimulus artifacts when compared to a conventional tripolar cuff. Furthermore, a new low-noise amplifier was proposed to improve the signal-to-noise ratio. The circuit was designed based on a noise analysis to minimize the noise, and the results show that the total noise of the amplifier was below 1 μV for a cuff impedance of 1 kΩ and a frequency bandwidth of 300 to 5000 Hz.

Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems.