Diagnosis of Parkinson’s disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.
Day surgery is regarded as a breakthrough in promoting the construction of hierarchical diagnosis and treatment because of its advantages such as effectively shortening the length of hospital stay and making full use of medical resources. In order to comply with the national policies, medical development, patient needs, and other factors, as a comprehensive tertiary hospital, Zhengzhou Central Hospital Affiliated to Zhengzhou University taking regional synergy as a carrier, combining with 92 medical units, actively explores the rational flow of patients with day surgery in the region, through implementing clinical path management, controlling surgery expenses and drug and consumables proportions, purchasing medical health insurance for day surgery patients to ensure patient safety, opening a green channel for patients at the grassroots level, effectively using the medical insurance to co-ordinate funds, and rationally planning medical treatment procedures, ect.
Objective To explore the effect of salbutamol combined with Rho associated coiled-coil forming protein kinase (ROCK) inhibitor Y-27632 on airway smooth muscle and to find a new way for drug treatment of asthma. Methods Pig tracheal smooth muscle tissue strips were prepared, and after treatment they were divided into an electrical stimulation group (Fmax, 50%Fmax) and a blank group. The smooth muscle tissue strips were quickly frozen to determine the expression level of Rock-Ⅱ and the phosphorylation level of MLC20. The Fmax and 50%Fmax electrical stimulation groups were divided into a blank group, a salbutamol group, a Y-27632 group, and a salbutamol combined with Y-27632 group according to different intervention drugs. The relaxation of smooth muscle strips was observed. Results In the blank group, 50%Fmax group and Fmax group, the expression level of Rock-Ⅱ and the phosphorylation of MLC20 in smooth muscle tissue showed an increasing trend, with statistically significant differences (P<0.05). In the 4 subgroups of the 50%Fmax group intervention with different drugs (blank group, salbutamol group, Y-27632 group, salbutamol plus Y-27632 group), the diastolic ratio smooth muscle tissue strips showed an increasing trend. When the time reaches 10 min, the diastolic ratios were 0.7%, 2.5%, 6.0%, and 15.0%. the diastolic ratios were 1.8%, 4.5%, 7.5%, and 21.0% at the time of 20 min. the diastolic ratios were 1.9%, 7.5%, 7.9% and 22.0% at the time of 40 min. the diastolic ratios were 2.0%, 8.0%, 8.8%, and 22.5% at the time of 60 min. In the four subgroups of the Fmax electrical stimulation group, the relaxation ratio of smooth muscle tissue strips also showed an increasing trend. When the time reaches 10 min, the diastolic ratios were 1.0%, 3.0%, 7.0%, and 17.0%. the diastolic ratios were 2.6%, 5.5%, 9.0%, and 24.0% at the time of 20 min. the diastolic ratios were 2.8%, 9.0%, 9.5%, and 27.5% at the time of 40 min. diastolic ratios were 2.9%, 10.5%, 10.5%, and 28.0% at the time of 60 min. The analysis of difference between groups showed that at the same time, the diastolic ratio of smooth muscle in salbutamol combined with Y-27632 group was significantly higher than that in salbutamol alone group and Y-27632 group (P<0.05). In addition, the smooth muscle diastolic ratio of combined intervention was also better than the the mathematical sum effect of both single drug intervention (P<0.05). Conclusions The contractility and intensity of smooth muscle are positively correlated with the expression level of ROCK and the phosphorylation level of MLC20. Salbutamol combined with Y-27632 can enhance the relaxation of porcine airway smooth muscle, which may have a synergistic effect.
In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients (r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of “human in the loop” and provides a potential method for remote rehabilitation training and assessment of the lower extremity.
An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8–13 Hz), beta frequency band (14–30 Hz) and gamma frequency band (31–60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.