RESUMEN
Background and objectives: Ischemic heart failure (HF) has become a disease that seriously endangers people's life and health. As a herbal formula widely used in clinical practice, new optimized Sheng-Mai-San (NO-SMS) has been shown to be significantly effective in improving cardiac function, increasing exercise tolerance, and slowing the progression of myocardial fibrosis in heart failure patients in multi-center clinical studies in various regions of China. In our previous pharmacodynamic and toxicological studies, we found that a medium-dose formulation (8.1 g of raw drug/kg) was the most effective in the treatment of heart failure, but its mechanism of action is still being investigated. The present study is exploring its relationship with cardiomyocyte apoptosis. Materials and methods: We investigated and verified this through two parts of experiments, in vivo and in vitro. Firstly, we prepared male SD rats with heart failure models by ligating the left anterior descending branch of the coronary artery (EF ≤ 50%), which were treated with NO-SMS Formula (8.1 g of raw drug/kg/d), Ifenprodil (5.4 mg/kg/d) or Enalapril (0.9 mg/kg/d) prepared suspensions by gavage for 4 weeks. The cardiac and structural changes were evaluated by echocardiography, H&E, and MASSON staining. The apoptosis of cardiomyocytes in each group was detected by Western blot, qRT-PCR, and ELISA. In vitro cell experiments include H9c2 cardiomyocyte injury induced by H2O2 and NMDA respectively, and the groups were incubated with NO-SMS and Ifenprodil-containing serum for 24 h. Apoptosis was detected by Annexin V-FITC/PI double-staining method, and the rest of the assays were consistent with the in vivo experiments. Results: Compared with the model group, the NO-SMS formula group and the Ifenprodil group could significantly improve cardiac function, delay myocardial fibrosis, reduce the expression of pro-apoptotic proteins, mRNA, and the concentration levels of Ca2+ and ROS in heart failure rats and H9c2 cardiomyocytes with H2O2 and NMDA-induced injury, which could significantly reduce the apoptosis rate of damaged cardiomyocytes and effectively inhibit the apoptosis of cardiomyocytes. Conclusion: NO-SMS Formula improved cardiac function, inhibited ventricular remodeling and cardiomyocyte apoptosis in HF rats, and its mechanism may be related to the regulation of the NMDAR signaling pathway, inhibition of large intracellular Ca2+ inward flow, and ROS production in cardiomyocytes.
RESUMEN
For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to offline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand.
RESUMEN
To improve the life quality of forearm amputees, prosthetic hands with high accuracy, and robustness are necessary. The application of surface electromyography (sEMG) signals to control a prosthetic hand is challenging. In this study, we proposed a time-domain CNN model for the regression prediction of joint angles in three degrees of freedom (3-DOFs, include two wrist joint motion and one finger joint motion), and five-fold cross validation was used to evaluate the correlation coefficient (CC). The CC value results of wrist flexion/extension motion obtained from 10 participants was 0.87-0.92, pronation/supination motion was 0.72-0.95, and hand grip/open motion was 0.75-0.94. We backtracked the fully connected layer weights to create a geometry plot for analyzing the motion pattern to investigate the learning of the proposed model. In order to discuss the daily updateability of the model by transfer learning, we performed a second experiment on five of the participants in another day and conducted transfer learning based on smaller amount of dataset. The CC results improved (wrist flexion/extension was 0.90-0.97, pronation/supination was 0.84-0.96, hand grip/open was 0.85-0.92), suggesting the effectiveness of the transfer learning by incorporating the small amounts of sEMG data acquired in different days. We compared our CNN-based model with four conventional regression models, the result illustrates that proposed model significantly outperforms the four conventional models with and without transfer learning. The offline result suggests the reliability of the proposed model in real-time control in different days, it can be applied for real-time prosthetic control in the future.