As the regenerative mechanisms of biological organisms, self-healing provides useful functions for soft electronics or associated systems. However, there have been few examples of soft electronics where all components have self-healing properties while also ensuring compatibility between components to achieve multifunctional and resilient bio-integrated electronics. Here, we introduce a stretchable, biodegradable, self-healing conductor constructed by combination of two layers: (i) synthetic self-healing elastomer and (ii) self-healing conductive composite with additives. Abundant dynamic disulfide and hydrogen bonds of the elastomer and conductive composite enable rapid and complete recovery of electrical conductivity (~1000 siemens per centimeter) and stretchability (~500%) in response to repetitive damages, and chemical interactions of interpenetrated polymer chains of these components facilitate robust adhesion strength, even under extreme mechanical stress. System-level demonstration of soft, self-healing electronics with diagnostic/therapeutic functions for the urinary bladder validates the possibility for versatile, practical uses in biomedical research areas.

Several studies have investigated the use of invasive and non-invasive stimulation methods to enhance nerve regeneration, and varying degrees of effectiveness have been reported. However, due to the use of different parameters in these studies, a fair comparison between the effectiveness of invasive and non-invasive stimulation methods is not possible. The present study compared the effectiveness of invasive and non-invasive stimulation using similar parameters. Eighteen Sprague Dawley rats were classified into three groups: the iES group stimulated with fully implantable device, the tES group stimulated with transcutaneous electrical nerve stimulation (TENS), and the injury group (no stimulation). The iES and tES groups received stimulation for 6 weeks starting immediately after the injury. Motor function was evaluated using the sciatic functional index (SFI) every week. The SFI values increased over time in all groups; faster and superior functional recovery was observed in the iES group than in the tES group. Histological evaluation of the nerve sections and gastrocnemius muscle sections were performed every other week. The axon diameter and muscle fiber area in the iES group were larger, and the g-ratio in the iES group was closer to 0.6 than those in the tES group. To assess the cause of the difference in efficiency, a 3D rat anatomical model was used to simulate the induced electric fields in each group. A significantly higher concentration and intensity around the sciatic nerve was observed in the iES group than in the tES group. Vector field distribution showed that the field was orthogonal to the sciatic nerve spread in the tES group, whereas it was parallel in the iES group; this suggested that the tES group was less effective in nerve stimulation. The results indicated that even though rats in the TENS group showed better recovery than those in the injury group, it cannot replace direct stimulation yet because rats stimulated with the invasive method showed faster recovery and superior outcomes. This was likely attributable to the greater concentration and parallel distribution of electric field with respect to target nerve.

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group.

Tibial nerve stimulation (TNS) is one of the neuromodulation methods used to treat an overactive bladder (OAB). However, the treatment mechanism is not accurately understood owing to significant differences in the results obtained from animal and clinical studies. Thus, this study was aimed to confirm the response of bladder activity to the different stimulation frequencies and to observe the duration of prolonged post-stimulation inhibitory effects following TNS. This study used unanesthetized rats to provide a closer approximation of the clinical setting and evaluated the changes in bladder activity in response to 30 min of TNS at different frequencies. Moreover, we observed the long-term changes of post-stimulation inhibitory effects. Our results showed that bladder response was immediately inhibited after 30 min of 10 Hz TNS, whereas it was excited at 50 Hz TNS. We also used the implantable stimulator to observe a change in duration of the prolonged post-stimulation inhibitory effects of the TNS and found large discrepancies in the time that the inhibitory effect lasted after stimulation between individual animals. This study provides important evidence that can be used to understand the neurophysiological mechanisms underlying the bladder inhibitory response induced by TNS as well as the long-lasting prolonged post-stimulation effect.