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Abstract Purpose Prior research had indicated a relationship between fibrinogen and stroke-associated pneumonia (SAP), yet the nature of this relationship had not been thoroughly investigated. Therefore, this study was designed to elucidate the prognostic value of fibrinogen levels in forecasting the occurrence of SAP among patients with acute ischemic stroke (AIS). Patients and methods In this retrospective cross-sectional analysis, we included 1092 patients who had experienced AIS and were admitted to our facility within 72 h of the onset of their symptoms. Based on the SAP diagnostic criteria, patients were classified into two groups: SAP and non-SAP. The correlation between serum fibrinogen concentration and SAP was examined using univariate analysis. Curve fitting and multivariable logistic regression model were utilized for statistical evaluation. Results Out of the ischemic stroke patients included in the study, SAP was identified in 112 (10.26%) patients. A direct correlation was observed between fibrinogen levels and the incidence of SAP. An increase in fibrinogen levels corresponded with a heightened incidence of SAP. Multivariable logistic regression revealed a significant positive association between fibrinogen levels and SAP incidence (OR = 1.53, 95% confidence interval [CI]: 1.18, 1.99)). Conclusion A linear relationship between serum fibrinogen levels and the incidence of SAP in ischemic stroke patients was shown. The serum fibrinogen levels were positively and linearly correlated to SAP risk.

Background Stroke-associated pneumonia (SAP) is a common complication of acute ischemic stroke (AIS) and is associated with increased mortality and prolonged hospital stays. The lymphocyte-to-monocyte ratio (LMR) is a novel inflammatory marker that has been shown to be associated with various diseases. However, the relationship between the LMR and SAP in patients with AIS remains unclear. Methods A retrospective cohort study was conducted on 1,063 patients with AIS admitted to our hospital within 72 hours of symptom onset. Patients were divided into two groups: the SAP group (n = 99) and the non-SAP group (n = 964). The LMR was measured within 24 hours of admission, and the primary outcome was the incidence of SAP. We used univariate and multivariate logistic regression analyses to assess the relationship between the LMR and SAP. Additionally, curve-fitting techniques and subgroup analyses were conducted. Result The incidence of SAP was 9.31%. We found that the LMR was significantly lower in the SAP group than in the non-SAP group (2.46 ± 1.44 vs. 3.86 ± 1.48, P

Background and objectivesSeveral recent clinical studies have indicated that vamorolone is comparable in effectiveness to glucocorticosteroids for treating Duchenne muscular dystrophy (DMD). However, there is a lack of extensive data regarding the efficacy and safety of various doses of vamorolone. We conducted a study to evaluate the efficacy of different doses of vamorolone in boys with DMD, and compare the safety of vamorolone vs. glucocorticosteroids, prednisone or deflazacort in boys with DMD.MethodsWe performed systematic searches of the PubMed, Embase, and Cochrane Library databases for vamorolone, glucocorticosteroids, prednisone or deflazacort in boys with DMD. We assessed statistical heterogeneity across trials based on the Newcastle Ottawa scale (NOS) tool test and I2 values, and mean differences were pooled using the random-effects model. We used traditional meta-analysis to evaluate efficacy and safety of vamorolone 6.0 mg/kg/d vs. vamorolone 2.0 mg/kg/d and vamorolone vs. prednisone. A network meta-analysis was applied to estimated the safety of vamorolone in comparison to glucocorticosteroids, prednisone and deflazacort. Our meta-analysis were performed using Revman 5.4 software, and our network meta-analysis were performed using Stata/MP 18.0.ResultsIn the meta-analysis, a total of 193 patients were analyzed across four clinical trials (97 patients receiving vamorolone 2 mg/kg per day; 96 patients receiving vamorolone 2 mg/kg per day). We observed that there were statistically significant differences in boys with DMD between vamorolone 6.0 mg/kg/d and vamorolone 2.0 mg/kg/d in TTSTANDV (MD = 0.03, 95%CI = 0.00–0.06, p = 0.04), TTRWV (MD = 0.13, 95%CI = 0.08–0.19, p

Purposes To achieve stable and efficient energy storage in the new elastic potential energy storage system, it is first necessary to establish an accurate mathematical model to reveal the operating characteristics of the energy storage box, and complete the calculation and configuration model of its capacity and power; Second, a strong robust control algorithm is needed to achieve the fast torque response while effectively suppressing disturbances. Methods In this paper, an accurate mathematical model of the energy storage system was established. Combined with the backstepping control algorithm, the direct torque adaptive control method of the permanent magnet synchronous motor is designed, and a new robust control strategy for the energy storage process of the elastic potential energy storage system is formed. Conclusions Through experiments, it has been proven that this control strategy has a fast torque response speed and can quickly match the rapidly increasing reverse torque of the energy storage box with minimal pulsation. At the same time, it can effectively restrain the unique moment of inertia parameter disturbance of the energy storage system, and then achieve stable and efficient energy storage.

BackgroundAnaerobic meningitis is a severe central nervous system infection associated with significant neurological sequelae and high mortality. However, the precise detection of causative pathogen(s) remains difficult because anaerobic bacteria are difficult to culture. Next-generation sequencing is a technology that was developed recently and has been applied in many fields. To the best of our knowledge, the use of next-generation sequencing for cerebrospinal fluid analysis in the diagnosis of anaerobic meningitis has been rarely reported.Case presentationHere, we report a case of polymicrobial anaerobic meningitis diagnosed using next-generation sequencing of cerebrospinal fluid in a 16-year-old girl. Five species of anaerobic bacteria (Porphyromonas gingivalis, Prevotella enoeca, Campylobacter rectus, Fusobacterium uncleatum, and Actinomyces israelii) were detected by next-generation sequencing and treated with antibacterial agents (ceftriaxone, vancomycin, and metronidazole). The patient responded well to antibacterial treatment. Further inspection revealed bone destruction at the base of the skull, which further confirmed that these bacteria had originated from the oral cavity. One month later, the patient's condition improved significantly. At the same time, we performed a literature review on anaerobic meningitis using studies published in the last 20 years.ConclusionsThis case emphasizes the importance of applying metagenomic next-generation sequencing to clinch the clinical diagnosis for patients with central nervous system infection. Metagenomic next-generation sequencing has been reported to be an important diagnostic modality for identifying uncommon pathogens.

The capsule robot (CR) is a promising endoscopic method in gastrointestinal diagnosis because of its low discomfort to users. Most CRs are used to acquire image information only and lack the ability to collect samples. Although some biopsy capsule robots (BCRs) have been developed, it remains challenging to acquire the intestinal tissue while avoiding tearing and adhesion due to the flexibility of colonic tissue. In this study, we develop a BCR with a novel sampling strategy in which soft tissue is scratched with sharp blades rotating at high speed to avoid tissue tearing. In the BCR design, a spiral spring with prestored energy is used to release high energy within a short period of time, which is difficult for a motor or magnet to perform within a small capacity installation space. The energy of the tightened spiral spring is transmitted to drive sharp blades to rotate quickly via a designed gear mechanism. To guarantee reliable sampling, a Bowden cable is used to transmit the user’s manipulation to trigger the rotation of the blades, and the triggering force transmitted by the cable can be monitored in real time by a force sensor installed at the manipulating end. A prototype of the proposed BCR is designed and fabricated, and its performance is tested through in vitro experiments. The results show that the proposed BCR is effective and the size of its acquired samples satisfies clinical requirements.

In upper limb rehabilitation training by exploiting robotic devices, the qualitative or quantitative assessment of human active effort is conducive to altering the robot control parameters to offer the patients appropriate assistance, which is considered an effective rehabilitation strategy termed as assist-as-needed. Since active effort of a patient is changeable for the conscious or unconscious behavior, it is considered to be more feasible to determine the distributions of the passive resistance of the patient’s joints versus the joint angle in advance, which can be adopted to assess the active behavior of patients combined with the measurement of robotic sensors. However, the overintensive measurements can impose a burden on patients. Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. The experiments recruiting three adult male subjects were conducted, and the results revealed that the BPANN exhibits high prediction accurate for each direction shoulder passive torque. The results revealed that the BPANN can learn the nonlinear relationship between the passive torque and the position of the shoulder joint and can make an accurate prediction without the need to build a force distribution function in advance, making it possible to draw up an assist-as-needed strategy with high accuracy while reducing the measurement burden of patients and physiotherapists.

Fish can swim in a variety of states. For example, they look flexible and perform low-frequency undulatory locomotion when cruising, but they seem very powerful and stiff and perform high-frequency undulatory when hunting. In the process of changing the motion state, the stiffness of the fish body affects the swimming performance of the fish. In this article, we imitated the change of stiffness by superimposing rubber sheets and used experimental methods to test its swimming performance under different swing frequencies. A series of rubber fish tails were made according to the analysis of the swimming movement of real fish, providing different stiffness values and changing the curves of the body. In the prototype experiments, the base of the fish tail was fixed to a platform via a force sensor, which can oscillate at various speeds, so that the fish tail was able to swing and the thrust could be tested at different frequencies. According to the experimental results, we found that with the change of the swing frequency, there were different optimal stiffnesses that could make the thrust reach the maximum value, and with the increase of stiffness, the envelope interval of the swing curve gradually widened, the amplitude increased, and the hysteresis of the tail fin relative to the end decreased.

Fishes have evolved different excellent swimming strategies. To study the influence of tail fin swing on the swimming performance of bionic robot fish, with one joint under the same tail swing frequency and amplitude, we designed a novel robot fish, driven by a double-cam mechanism. By designing the profile of the cam in the mechanism, the robot fish can achieve different undulatory motion trajectory of the caudal fin under the same tail swing frequency and amplitude. The mechanism simulated the undulatory motion of crucian carp. We studied the influence of undulatory motion on the swimming speed of robot fish, which was analyzed by dynamic analysis of the undulatory motion and experiments. According to the experimental results, we can find that the swimming speed of the robotic fish is different under various wave motions. When other conditions are the same, the speed that the robot fish can achieve by imitating the swing motion of the real fish is 1.5 times that of the robot fish doing the cycloid motion. The experimental results correspond to the kinetic analysis results. Furthermore, it is proven that the robot fish with a low caudal peduncle stiffness swims faster under a low swinging frequency, and the speed of a robot fish with a high caudal peduncle stiffness is higher under a high tail swinging frequency.

A nonlinear stiffness actuator (NSA) can achieve high torque/force resolution in the low stiffness range and high bandwidth in the high stiffness range. However, for the NSA, due to the imperfect performance of the elastic mechanical component such as friction, hysteresis, and unmeasurable energy consumption caused by former factors, it is more difficult to achieve accurate position control compared to the rigid actuator. Moreover, for a compliant robot with multiple degree of freedoms (DOFs) driven by NSAs, the influence of every NSA on the trajectory of the end effector is different and even coupled. Therefore, it is a challenge to implement precise trajectory control on a robot driven by such NSAs. In this paper, a control algorithm based on the Terminal Sliding Mode (TSM) approach is proposed to control the end effector trajectory of the compliant robot with multiple DOFs driven by NSAs. This control algorithm reduces the coupling of the driving torque, and mitigates the influence of parametric variation. The closed-loop system’s finite time convergence and stability are mathematically established via the Lyapunov stability theory. Moreover, under the same experimental conditions, by the comparison between the Proportion Differentiation (PD) controller and the controller using TSM method, the algorithm’s efficacy is experimentally verified on the developed compliant robot. The results show that the trajectory tracking is more accurate for the controller using the TSM method compared to the PD controller.

The robot arm with flexible joint has good environmental adaptability and human robot interaction ability. However, the controller for such robot mostly relies on data acquisition of multiple sensors, which is greatly disturbed by external factors, resulting in a decrease in control precision. Aiming at the control problem of the robot arm with flexible joint under the condition of incomplete state feedback, this paper proposes a control method based on closed-loop PD (Proportional-Derivative) controller and EKF (Extended Kalman Filter) state observer. Firstly, the state equation of the control system is established according to the non-linear dynamic model of the robot system. Then, a state prediction observer based on EKF is designed. The state of the motor is used to estimate the output state, and this method reduces the number of sensors and external interference. The Lyapunov method is used to analyze the stability of the system. Finally, the proposed control algorithm is applied to the trajectory control of the flexible robot according to the stability conditions, and compared with the PD control algorithm based on sensor data acquisition under the same experimental conditions, and the PD controller based on sensor data acquisition under the same test conditions. The experimental data of comparison experiments show that the proposed control algorithm is effective and has excellent trajectory tracking performance.

Training based on muscle-oriented repetitive movements has been shown to be beneficial for the improvement of movement abilities in human limbs in relation to fitness, athletic training, and rehabilitation training. In this paper, a muscle-specific rehabilitation training method based on the optimal load orientation concept (OLOC) was proposed for patients whose motor neurons are injured, but whose muscles and tendons are intact, to implement high-efficiency resistance training for the shoulder muscles, which is one of the most complex joints in the human body. A three-dimensional musculoskeletal model of the human shoulder was used to predict muscle forces experienced during shoulder movements, in which muscles that contributed to shoulder motion were divided into 31 muscle bundles, and the Hill model was used to characterize the force-length properties of the muscle. According to the musculoskeletal model, muscle activation was calculated to represent the muscle force. Thus, training based on OLOC was proposed by maximizing the activation of a specific muscle under each posture of the training process. The analysis indicated that the muscle-specific rehabilitation training method based on the OLOC significantly improved the training efficiency for specific muscles. The method could also be used for trajectory planning, load magnitude planning, and evaluation of training effects.

Compliant actuators are more advantageous than stiff actuators in some circumstances, for example, unstructured environment robots and rehabilitation robots. Compliant actuators are more adaptive and safe. Constant stiffness compliant actuators have some limitations in impedance and bandwidth. Variable stiffness actuators improve their performance owing to introducing an extra motor to tune the stiffness of the actuators. However, they also have some limitations such as the bulky structure and heavy weight. It was also found that there are some waste functions existing in the current variable stiffness actuators and that the fully decoupled position control and stiffness tune are not necessary, because there exist some regular phenomena during most circumstances of human interaction with the robots which are “low load, low stiffness and high load, high stiffness”. In this paper, a design method for nonlinear stiffness compliant actuator was proposed which performed the predefined deflection-torque trajectory of the regular phenomenon. A roller and a cantilever which has special curve profile constitute the basic mechanical structure of the nonlinear stiffness compliant actuators. An error compensation method was also proposed to analyze the stiffness of elastic structure. The simulation results proved that the proposed method was effective in designing a predefined nonlinear stiffness compliant actuator.

A variety of microrobots have commonly been used in the fields of biomedical engineering and underwater operations during the last few years. Thanks to their compact structure, low driving power, and simple control systems, microrobots can complete a variety of underwater tasks, even in limited spaces. To accomplish our objectives, we previously designed several bio-inspired underwater microrobots with compact structure, flexibility, and multi-functionality, using ionic polymer metal composite (IPMC) actuators. To implement high-position precision for IPMC legs, in the present research, we proposed an electromechanical model of an IPMC actuator and analysed the deformation and actuating force of an equivalent IPMC cantilever beam, which could be used to design biomimetic legs, fingers, or fins for an underwater microrobot. We then evaluated the tip displacement of an IPMC actuator experimentally. The experimental deflections fit the theoretical values very well when the driving frequency was larger than 1 Hz. To realise the necessary multi-functionality for adapting to complex underwater environments, we introduced a walking biomimetic microrobot with two kinds of motion attitudes: a lying state and a standing state. The microrobot uses eleven IPMC actuators to move and two shape memory alloy (SMA) actuators to change its motion attitude. In the lying state, the microrobot implements stick-insect-inspired walking/rotating motion, fish-like swimming motion, horizontal grasping motion, and floating motion. In the standing state, it implements inchworm-inspired crawling motion in two horizontal directions and grasping motion in the vertical direction. We constructed a prototype of this biomimetic microrobot and evaluated its walking, rotating, and floating speeds experimentally. The experimental results indicated that the robot could attain a maximum walking speed of 3.6 mm/s, a maximum rotational speed of 9°/s, and a maximum floating speed of 7.14 mm/s. Obstacle-avoidance and swimming experiments were also carried out to demonstrate its multi-functionality.

According to neuro-rehabilitation practice, active training is effective for mild stroke patients, which means these patients are able to recovery effective when they perform the training to overcome certain resistance by themselves. Therefore, for rehabilitation devices without backdrivability, implementation of human-machine synchronization is important and a precondition to perform active training. In this paper, a method to implement this precondition is proposed and applied in a user’s performance of elbow flexions and extensions when he wore an upper limb exoskeleton rehabilitation device (ULERD), which is portable, wearable and non-backdrivable. In this method, an inertia sensor is adapted to detect the motion of the user’s forearm. In order to get a smooth value of the velocity of the user’s forearm, an adaptive weighted average filtering is applied. On the other hand, to obtain accurate tracking performance, a double close-loop control is proposed to realize real-time and stable tracking. Experiments have been conducted to prove that these methods are effective and feasible for active rehabilitation.

Regenerative braking is an effective approach for electric vehicles (EVs) to extend their driving range. A fuzzy-logic-based regenerative braking strategy (RBS) integrated with series regenerative braking is developed in this paper to advance the level of energy-savings. From the viewpoint of securing car stability in braking operations, the braking force distribution between the front and rear wheels so as to accord with the ideal distribution curve are considered to prevent vehicles from experiencing wheel lock and slip phenomena during braking. Then, a fuzzy RBS using the driver’s braking force command, vehicle speed, battery SOC, battery temperature are designed to determine the distribution between friction braking force and regenerative braking force to improve the energy recuperation efficiency. The experimental results on an “LF620” prototype EV validated the feasibility and effectiveness of regenerative braking and showed that the proposed fuzzy RBS was endowed with good control performance. The maximum driving range of LF620 EV was improved by 25.7% compared with non-RBS conditions.

Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.

BACKGROUND: While many human immunodeficiency virus (HIV) studies have been performed in Liangshan, most were focused only on HIV infection and based on a sampling survey. In order to fully understand HIV and hepatitis C virus (HCV) prevalence and related risk factors in this region, this study implemented in 2009, included a survey, physical examination, HIV and HCV test in two towns. METHODS: All residents in two towns of the Butuo county were provided a physical examination and blood tests for HIV and HCV, and then followed by an interview for questionnaire. RESULTS: In total, 10,104 residents (92.4%) were enrolled and 9,179 blood samples were collected for HIV and HCV testing, 6,072 were from individuals >14 years old. The rates of HIV, HCV, and HIV/HCV co-infection were 11.4%, 14.0%, and 7.7%, respectively for >14-year-old residents. The 25-34 yr age group had the highest prevalence of HIV, HCV, and HIV/HCV co-infections, reaching 24.4%, 26.2% and 16.0%, respectively. Overall, males had a much higher prevalence of all infections than females (HIV: 16.3% vs. 6.8%, HCV: 24.6% vs. 3.9%, HIV/HCV co-infected: 14.7% vs. 1.1%, respectively; P = 0.000). Approximately half of intravenous drug users tested positive for HIV (48.7%) and 68.4% tested positive for HCV. Logistic regression analysis showed that five factors were significantly associated with HIV and HCV infection: gender (odds ratio [OR] = 5.8), education (OR = 2.29); occupation (student as reference; farmer: OR = 5.02, migrant worker: OR = 6.12); drug abuse (OR = 18.0); and multiple sexual partners (OR = 2.92). Knowledge of HIV was not associated with infection. CONCLUSION: HIV and HCV prevalence in the Liangshan region is very serious and drug use, multiple sexual partners, and low education levels were the three main risk factors. The government should focus on improving education and personal health awareness while enhancing drug control programs.

Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range.