Demonstrating the effectiveness of intra-articular prolotherapy combined with peri-articular perineural injection in knee osteoarthritis: a randomized controlled trial.

BACKGROUND: This study aimed to compare the efficacy of intra-articular prolotherapy (IG) combined with peri-articular perineural injection (PG) in the management of knee osteoarthritis (KOA). METHODS: A total of 60 patients with the diagnosis of KOA were included in this double-blinded randomized controlled clinical trials. The inclusion criteria were as follow: (1) 48-80 years old; (2) the diagnose of KOA; (3) the grade 2 and 3 of the Kellgern-Lawrence grading scale; (4) the pain, crepitation, and knee joint stiffness continuing for 3 months at least. The main exclusion criteria were as follow: (1) any infection involving the knee skin; (2) history of any Influencing factors of disease. All patients were divided into three groups and received either IG, PG and I + PG under the ultrasound guidance and the 2, 4 and 8 weeks follow-up data of patients were available. (IG n = 20 or PG n = 20, I + PG n = 20). Visual Analogue Scale (VAS), The Western Ontario McMaster University Osteoarthritis Index (WOMAC) and the pressure pain threshold (PPT) were used as outcome measures at baseline, 2, 4 and 8 weeks. RESULTS: There were no statistically significant differences in terms of age, sex, BMI, duration of current condition and baseline assessments of pain intensity, WOMAC scores and PPT. After treatment, the improvement of VAS activity, WOMAC and PPT values was showed compared with pre-treatment in all groups (p < 0.05). At 4 and 8 weeks after treatment, the VAS and WOMAC scores of the I + PG were significantly lower than those of the PG or IG, and the difference was statistically significant (p < 0.05). The PPT values of PG and I + PG were significantly improved compared to IG at 2, 4, and 8 weeks after treatment. CONCLUSION: The ultrasound guided I + PG of 5% glucose seem to be more effective to alleviate pain and improve knee joint function than single therapy in short term. Clinical rehabilitators could clinically try this combination of I + PG to improve clinical symptoms in patients with KOA.

Platelet-rich plasma alleviates neuropathic pain in osteoarthritis by downregulating microglial activation.

BACKGROUND: The development of neuropathic pain (NP) is one of the reasons why the pain is difficult to treat, and microglial activation plays an important role in NP. Recently, platelet-rich plasma (PRP) has emerged as a novel therapeutic method for knee osteoarthritis (KOA). However, it's unclarified whether PRP has analgesic effects on NP induced by KOA and the underlying mechanisms unknown. PURPOSE: To observe the analgesic effects of PRP on NP induced by KOA and explore the potential mechanisms of PRP in alleviating NP. METHODS: KOA was induced in male rats with intra-articular injections of monosodium iodoacetate (MIA) on day 0. The rats received PRP or NS (normal saline) treatment at days 15, 17, and 19 after modeling. The Von Frey and Hargreaves tests were applied to assess the pain-related behaviors at different time points. After euthanizing the rats with deep anesthesia at days 28 and 42, the corresponding tissues were taken for subsequent experiments. The expression of activating transcription factor 3 (ATF3) in dorsal root ganglia (DRG) and ionized-calcium-binding adapter molecule-1(Iba-1) in the spinal dorsal horn (SDH) was detected by immunohistochemical staining. In addition, the knee histological assessment was performed by hematoxylin-eosin (HE) staining. RESULTS: The results indicated that injection of MIA induced mechanical allodynia and thermal hyperalgesia, which could be reversed by PRP treatment. PRP downregulated the expression of ATF3 within the DRG and Iba-1 within the SDH. Furthermore, an inhibitory effect on cartilage degeneration was observed in the MIA + PRP group only on day 28. CONCLUSION: These results indicate that PRP intra-articular injection therapy may be a potential therapeutic agent for relieving NP induced by KOA. This effect could be attributed to downregulation of microglial activation and reduction in nerve injury.

Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors.

Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings.

End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism.

With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of prediction models remain critical bottlenecks. Therefore, this article proposes a dual-attention (DA) based end-to-end (E2E) neural network (DAE2ENet) for trajectory prediction. In the E2E structure, long short-term memory (LSTM) units are included for the task of pursuing sequential trajectory data from the encoder layer to the decoder layer. In DA mechanisms, global attention is introduced between the encoder and decoder layers to facilitate interactions between input and output trajectory sequences, and multi-head self-attention is utilized to extract sequential features from the input trajectory. In experiments, we use a ro-ro ship with a fixed navigation route as a case study. Compared with baseline models and benchmark neural networks, DAE2ENet can obtain higher performance on trajectory prediction, and better validation of environmental factors on ship navigation.

Attack-resilient fault detection for interconnected systems under DoS attack.

In light of the expanding cyber-space applications, the imperative consideration of cyber-attack ramifications on system security is evident. This paper presents a resilient dynamic event-triggered fault detection scheme for a class of nonlinear interconnected systems subjected to denial of service (DoS) attacks. To counteract multifaceted threats, the co-design challenge involving switched-type fault detection filters and a resilient dynamic event-triggered transmission mechanism is addressed. In the design phase of the filters, the frequency information of the signal is considered comprehensively and linear solvable conditions ensuring desired augment system performance are delineated. Through a series of comparative simulation experiments, the findings support the conclusion that the proposed attack-tolerant fault detection mechanism not only conserves network resources but also demonstrates superior detection capabilities for specific frequency fault signals.

Continuous and Periodic Event-Triggered Sliding-Mode Control for Path Following of Underactuated Surface Vehicles.

This article develops continuous and periodic event-triggered sliding-mode control (SMC) algorithms for path following of underactuated surface vehicles (USVs). Based on the SMC technology, a continuous path-following control law is designed. The upper bounds of quasi-sliding modes for path following of USVs are established for the first time. Subsequently, both continuous and periodic event-triggered mechanisms are considered and added into the proposed continuous SMC scheme. It is demonstrated that with appropriate selecting of control parameters, the use of hyperbolic tangent functions does not affect the boundary layer of quasi-sliding mode caused by event-triggered mechanisms. The proposed continuous and periodic event-triggered SMC strategies can make the sliding variables reach the quasi-sliding modes and stay in there. Moreover, energy consumption can be reduced. Stability analysis shows that the USV can follow a reference path by using the designed method. The simulation results show the effectiveness of the proposed control methods.

Fixed-time command-filtered composite adaptive neural fault-tolerant control for strict-feedback nonlinear systems.

The research investigates the fixed-time command-filtered composite adaptive neural fault-tolerant (FCCANF) control issue of strict-feedback nonlinear systems (SFNSs). There exist unknown functions and bounded disturbances in the considered systems. Radial basis function neural networks (RBFNNs) will be used in the estimate of the unknown functions. By the serial-parallel estimation models (SPEMs), the forecast biases and the track biases can change the weights of RBFNNs and the approximate characteristics of RBFNNs will be improved. Then, utilizing the novel fixed-time command filter and adaptive disturbance observers, the issue of complex explosion will be effectively solved and the external disturbance is effectively compensated. Subsequently, by utilizing the adaptive control technique, a novel FCCANF controller is developed. Additionally, we have that the system internal variables are bounded and the output variable inclines to a little interval around zero in fixed time which is not determined by the system initial variables. Eventually, numerical and practical examples are shown to prove the availability of the obtained control technique.

Transcranial direct current stimulation attenuates chronic pain in knee osteoarthritis by modulating BDNF/TrkB signaling in the descending pain modulation system.

Knee osteoarthritis (KOA) is the most common cause of chronic pain, but its pain mechanisms are complex and may be closely related to the descending pain modulation system. Transcranial direct current stimulation (tDCS) is used for relieving pain, but its analgesic mechanisms are still being explored. The purpose of this study was to investigate the role of BDNF/TrkB signaling in chronic pain in KOA and to investigate whether this signaling is related to the analgesic effect of tDCS. Rats were injected with monosodium iodoacetate (MIA) into the left knee joint to establish a chronic pain model and then received 20 min of tDCS for 8 days. Rats were respectively administered the TrkB inhibitor ANA-12 after MIA modeling and exogenous BDNF after tDCS treatment. Behaviors testing was assessed by hot plate and von Frey hairs using the up-down method. In addition, the expression levels of BDNF and TrkB on the periaqueductal gray (PAG)-the rostral ventromedial medulla (RVM)-the spinal dorsal horn (SDH) axis were detected by Western blot and Immunohistochemistry staining. Behavioral results show that tDCS treatment and ANA-12 injection reversed MIA-induced allodynia while reducing BDNF and TrkB expression levels. Furthermore, injection of exogenous BDNF reversed the therapeutic effect of tDCS on pain. These results indicate that upregulation of the BDNF/TrkB signaling in the descending pain modulation system may play an important role in KOA-induced chronic pain in rats, and tDCS may reduce KOA-induced chronic pain by inhibiting the BDNF/TrkB signaling in the descending pain modulation system.

Heading and velocity guidance based path following of autonomous surface vehicle with uncertainty attenuation and asymmetric saturated constraints.

The path following of underactuated autonomous surface vehicle (ASV) with line-of-sight (LOS)-based heading and velocity guidance is studied thoroughly in the presence of complex uncertainties and asymmetric input saturation that actuators are likely to suffer from. On the basis of the extended-state-observer-based LOS (ELOS) principle and guided velocity design strategies, a finite-time heading and velocity guidance control (HVG) scheme is presented. Firstly, an improved ELOS (IELOS) is developed such that the unknown sideslip angle can be estimated directly, instead of requiring one more step to calculate it by the output of observers and relying on the equivalent assumption between actual heading angle and guidance angle. Secondly, a new form of velocity guidance is designed by considering magnitude and rate constraints and path's curvature, keeping in line with ASV's manoeuvrability and agility. Then asymmetric saturation is considered and studied by designing projection-based finite-time auxiliary systems to avoid parameter drift. All error signals of the closed-loop system of ASV are forced to converge to an arbitrarily small neighbourhood of the origin within a finite settling time by the HVG scheme. The expected performance of the presented strategy is demonstrated via a series of simulations and comparisons. In addition, to show the strong robustness of the presented scheme, stochastic noises modelled by Markov process, bidirectional step signals and faults both multiplication and addition types are considered in simulations.

Distributed Fault-Tolerant Containment Control Protocols for the Discrete-Time Multiagent Systems via Reinforcement Learning Method.

This article investigates the model-free fault-tolerant containment control problem for multiagent systems (MASs) with time-varying actuator faults. Depending on the relative state information of neighbors, a distributed containment control method based on reinforcement learning (RL) is adopted to achieve containment control objective without prior knowledge on the system dynamics. First, based on the information of agent itself and its neighbors, a containment error system is established. Then, the optimal containment control problem is transformed into an optimal regulation problem for the containment error system. Furthermore, the RL-based policy iteration method is employed to deal with the corresponding optimal regulation problem, and the nominal controller is proposed for the original fault-free system. Based on the nominal controller, a fault-tolerant controller is further developed to compensate for the influence of actuator faults on MAS. Meanwhile, the uniform boundedness of the containment errors can be guaranteed by using the presented control scheme. Finally, numerical simulations are given to show the effectiveness and advantages of the proposed method.

Event-Triggered Multigradient Recursive Reinforcement Learning Tracking Control for Multiagent Systems.

In this article, the tracking control problem of event-triggered multigradient recursive reinforcement learning is investigated for nonlinear multiagent systems (MASs). Attention is focused on the distributed reinforcement learning approach for MASs. The critic neural network (NN) is applied to estimate the long-term strategic utility function, and the actor NN is designed to approximate the uncertain dynamics in MASs. The multigradient recursive (MGR) strategy is tailored to learn the weight vector in NN, which eliminates the local optimal problem inherent in gradient descent method and decreases the dependence of initial value. Furthermore, reinforcement learning and event-triggered mechanism can improve the energy conservation of MASs by decreasing the amplitude of the controller signal and the controller update frequency, respectively. It is proved that all signals in MASs are semiglobal uniformly ultimately bounded (SGUUB) according to the Lyapunov theory. Simulation results are given to demonstrate the effectiveness of the proposed strategy.

Gait Prediction and Variable Admittance Control for Lower Limb Exoskeleton With Measurement Delay and Extended-State-Observer.

The measurement delay of the feedback control system is a universal problem in industrial engineering, which will degrade output performance, especially causing undesirable chatter responses. In this study, a deep-Gaussian-process (DGP)-based method for operator's gait prediction is proposed to estimate the real-time motion intention and to compensate for the measurement delay of the inertial measurement unit (IMU). On the basis of these gait prediction uncertainties quantified by the DGP method, a variable admittance controller is designed to reduce real-time human-exoskeleton interaction torque. The reference trajectory is generated by the admittance controller, which is smoothed by the two-order Bessel interpolation. Meanwhile, the admittance parameters are self-regulated based on the defined uncertainty index of gait prediction. The extend-state observer (ESO) with backstepping iteration is adopted to compensate unmeasured system state, model uncertainties, and unmodeled dynamics of lower limb exoskeleton. The effectiveness of the proposed gait prediction and control scheme is verified by both the comparative simulations and experimental results of the human-exoskeleton cooperative motion.

Anti-Attack Event-Triggered Control for Nonlinear Multi-Agent Systems With Input Quantization.

In this article, an anti-attack event-triggered secure control scheme for a class of nonlinear multi-agent systems with input quantization is developed. With the help of neural networks approximating unknown nonlinear functions, unknown states are obtained by designing an adaptive neural state observer. Then, a relative threshold event-triggered control strategy is introduced to save communication resources including network bandwidth and computational capabilities. Furthermore, a quantizer is employed to provide sufficient accuracy under the requirement of a low transmission rate, which is represented by the so-called a hysteresis quantizer. Meanwhile, to resist attacks in the multi-agent network, a predictor is designed to record whether an edge is attacked or not. Through the Lyapunov analysis, the proposed secure control protocol can ensure that all the closed-loop signals remain bounded under attacks. Finally, the effectiveness of the designed scheme is verified by simulation results.

Adaptive composite dynamic surface neural control for nonlinear fractional-order systems subject to delayed input.

In the article, the adaptive composite dynamic surface neural controller design problem for nonlinear fractional-order systems (NFOSs) subject to delayed input is discussed. A fractional-order auxiliary system is first designed to solve the input-delay problem. By using the developed novel estimation models, the defined prediction errors and the states of error system can decide the weights of radial basis function neural networks (RBFNNs). During the dynamic surface controller design process, the developed fractional-order filters are designed to handle the complexity explosion problem when the classical backstepping control technique is utilized. It is shown that the designed adaptive composite neural controller ensures that all the system state variables are bounded and the tracking error of the considered system finally tends to a small neighborhood of zero. Finally, the results of the simulation explain the feasibility of the developed controller. In addition, the developed controller can also be applied to single input and single output(SISO) nonlinear systems subject to a unitary input function.

Event-Triggered Distributed Secondary Control With Model-Free Predictive Compensation in AC/DC Networked Microgrids Under DoS Attacks.

This article presents an event-triggered distributed secondary control with predictive compensation based on the model-free predictive control under Denial-of-Service (DoS) attacks in ac/dc-networked microgrids. First, models of ac/dc networked microgrids in both electric network and communication network are established. On this premise, event-triggered distributed secondary control is proposed to solve the problems of strong communication burden and low-power distribution accuracy. Besides, aiming at the impact of DoS attacks on distributed secondary control, a compensation algorithm based on model-free predictive control is designed to estimate the control variables when the DoS attack occurs, which can improve the control performance and maintain the stable operation without a specific system structure system. Then, the convergence of event-triggered distributed secondary control with the condition of whether DoS attacks happen are analyzed. Finally, the effectiveness of the proposed control is verified on the hardware-in-loop (HIL) simulation platform consisting of the RT-LAB simulator, MATLAB/Simulink simulation model, and DSP controller.

Can noninvasive Brain Stimulation Improve Pain and Depressive Symptoms in Patients With Neuropathic Pain? A Systematic Review and Meta-Analysis.

CONTEXT: Noninvasive brain stimulations (NIBS) have been increasingly applied to the patients with neuropathic pain (NP), while the effectiveness of NIBS in the management of NP is still conflicting. OBJECTIVES: To examine the effectiveness of NIBS on pain and depression symptoms of patients with NP. METHODS: A comprehensive literature retrieval was performed on MEDLINE, Embase, PsycINFO, PEDro, and CENTRAL from the establishment of the databases to June 2021. Randomized controlled trials comparing NIBS with sham stimulation were included. RESULTS: A total of thirteen trials comprising 498 participants met the inclusion criteria. The pooled analysis found a significant effect on the improvement of pain scores at post-treatment, favoring NIBS over sham stimulation (SMD = -0.60; 95% CI: -1.00 to -0.20; P = 0.004). Subgroup analysis showed that only transcranial direct current stimulation (tDCS) (SMD = -0.38; 95% CI: -0.71 to -0.04; P = 0.030) and high-frequency repetitive transcranial magnetic stimulation (H-rTMS) (SMD = -0.95; 95% CI: -1.85 to -0.04; P = 0.040) had positive effects on pain reduction among all types of NIBS. The favorable effects of NIBS remained significant at follow-up visit (SMD = -0.51; 95% CI: -0.79 to -0.23; P = 0.000), while only H-rTMS was found in subgroup analyses to significantly improve pain scales of the patients (SMD = -0.54; 95% CI: -0.85 to -0.24; P = 0.000). Additionally, overall NIBS showed no beneficial effect over sham stimulation in reducing depression symptoms of NP patients either at post-treatment (SMD = -0.19; 95% CI: -0.39 to 0.01; P = 0.061) or at follow-up visit (SMD = -0.18; 95% CI: -0.45 to 0.10; P = 0.202). CONCLUSION: This meta-analysis revealed the analgesic effect of NIBS on patients with NP, while no beneficial effect was observed on reducing concomitant depression symptoms. The findings recommended the clinical application of NIBS in patients with NP.

Botulinum toxin type a combined with transcranial direct current stimulation reverses the chronic pain induced by osteoarthritis in rats.

Osteoarthritis (OA) is the most common cause to lead to chronic pain. Sensitization of pain pathways including central sensitization and peripheral sensitization has been regarded as a major cause of OA pain refractory to treatment. Addressing peripheral sensitization or central sensitization alone may not adequately treat OA pain. In our previous studies, botulinum toxin type A (BoNT/A) has been shown to reduce peripheral sensitization for analgesic effects. In addition, transcranial direct current stimulation (tDCS) has also been suggested to reduce central sensitization for analgesia. The present study was designed to investigate whether BoNT/A in combination with tDCS has better analgesic effects than isolated treatment to alleviate OA-induced chronic pain in rats. The Von Frey and hot plate tests were applied to assess the pain-related behaviors at different time points. The expression level of N-methyl-D-aspartate receptor-2B (NMDAR2B) was evaluated in midbrain periaqueductal gray (PAG) by Western blot the Immunohistochemistry staining after different treatments. The results showed that the combined treatment of BoNT/A and tDCS better improved the pain-related behaviors and significantly increased the expression level of NMDAR2B protein in PAG than each isolated treatment. These results suggested that the combined treatments for relief of chronic pain were more obvious than each isolated treatment. The combination of BoNT/A and tDCS may relieve pain by increasing N-methyl-D-aspartate (NMDA) receptors in the PAG, and then the descending inhibitory systems were activated to modulate peripheral and central sensitization.

Transcranial Direct Current Stimulation Alleviates the Chronic Pain of Osteoarthritis by Modulating NMDA Receptors in Midbrain Periaqueductal Gray in Rats.

PURPOSE: Osteoarthritis (OA) is the most common cause to lead to chronic pain. Transcranial direct current stimulation (tDCS) has been widely used to treat nerve disorders and chronic pain. The benefits of tDCS for chronic pain are apparent, but its analgesic mechanism is still unclear. This study observed the analgesic effects of tDCS on OA-induced chronic pain and the changes of NMDA receptor levels in PAG after tDCS treatment in rats to explore the analgesic mechanism of tDCS. METHODS: After establishing chronic pain by injecting monosodium iodoacetate (MIA) into the rat ankle joint, the rats received tDCS for 14 consecutive days (20 min/day). Before tDCS treatment, Ifenprodil (the selective antagonist of NMDAR2B) was given to rats in different ways: intracerebroventricular (i.c.v.) injection or intraperitoneal (i.p.) injection. The Von Frey and hot plate tests were applied to assess the pain-related behaviors at different time points. The expression level of NMDAR2B was evaluated in midbrain periaqueductal gray (PAG) by Western blot. In addition, NMDAR2B and c-Fos were observed by the Immunohistochemistry staining after tDCS treatment. RESULTS: The mechanical allodynia and thermal hyperalgesia were produced after MIA injection. However, tDCS treatment reverted the mechanical allodynia and thermal hyperalgesia. Moreover, tDCS treatment significantly increased the expression of NMDAR2B and the proportion of positive stained cells of NMDAR2B. Besides that, the tDCS treatment also decreased the proportion of positive stained cells of c-Fos in PAG. However, these changes did not occur in the rats given the Ifenprodil (i.c.v.). CONCLUSION: These results indicate that tDCS may increase the expression of NMDA receptors in PAG and strengthen the NMDA receptors-mediated antinociception to alleviate OA-induced chronic pain in rats.

IBLF-Based Adaptive Neural Control of State-Constrained Uncertain Stochastic Nonlinear Systems.

In this article, the adaptive neural backstepping control approaches are designed for uncertain stochastic nonlinear systems with full-state constraints. According to the symmetry of constraint boundary, two cases of controlled systems subject to symmetric and asymmetric constraints are studied, respectively. Then, corresponding adaptive neural controllers are developed by virtue of backstepping design procedure and the learning ability of radial basis function neural network (RBFNN). It is worth mentioning that the integral Barrier Lyapunov function (IBLF), as an effective tool, is first applied to solve the above constraint problems. As a result, the state constraints are avoided from being transformed into error constraints via the proposed schemes. In addition, based on Lyapunov stability analysis, it is demonstrated that the errors can converge to a small neighborhood of zero, the full states do not exceed the given constraint bounds, and all signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) in probability. Finally, the numerical simulation results are provided to exhibit the effectiveness of the proposed control approaches.