Purpose To use meta-analysis techniques to evaluate the efficacy and safety of platelet-rich plasma (PRP) injections for the treatment knee of osteoarthritis (OA). Methods We performed a systematic ...literature search in PubMed, Embase, Scopus, and the Cochrane database through April 2016 to identify Level I randomized controlled trials that evaluated the clinical efficacy of PRP versus control treatments for knee OA. The primary outcomes were Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function scores. The primary outcomes were compared with their minimum clinically important differences (MCID)—defined as the smallest difference perceived as important by the average patient. Results We included 10 randomized controlled trials with a total of 1069 patients. Our analysis showed that at 6 months postinjection, PRP and hyaluronic acid (HA) had similar effects with respect to pain relief (WOMAC pain score) and functional improvement (WOMAC function score, WOMAC total score, International Knee Documentation Committee score, Lequesne score). At 12 months postinjection, however, PRP was associated with significantly better pain relief (WOMAC pain score, mean difference −2.83, 95% confidence interval CI −4.26 to −1.39, P = .0001) and functional improvement (WOMAC function score, mean difference −12.53, 95% CI −14.58 to −10.47, P < .00001; WOMAC total score, International Knee Documentation Committee score, Lequesne score, standardized mean difference 1.05, 95% CI 0.21-1.89, P = .01) than HA, and the effect sizes of WOMAC pain and function scores at 12 months exceeded the MCID (−0.79 for WOMAC pain and −2.85 for WOMAC function score). Compared with saline, PRP was more effective for pain relief (WOMAC pain score) and functional improvement (WOMAC function score) at 6 months and 12 months postinjection, and the effect sizes of WOMAC pain and function scores at 6 months and 12 months exceeded the MCID. We also found that PRP did not increase the risk of adverse events compared with HA and saline. Conclusions Current evidence indicates that, compared with HA and saline, intra-articular PRP injection may have more benefit in pain relief and functional improvement in patients with symptomatic knee OA at 1 year postinjection. Level of Evidence Level I, meta-analysis of Level I studies.
This article proposes a novel disturbance rejection model predictive control (DRMPC) framework to improve the robustness of model predictive control (MPC) for a broad class of input-affine nonlinear ...systems with constraints and state-dependent disturbances. The proposed controller includes two parts-a disturbance compensation input and an optimal MPC control input. The former one is designed to compensate for the matched disturbance actively. This is made possible via a disturbance observer that estimates the disturbance and by adopting a space decomposition method. The residual disturbance is then handled in the MPC optimization problem by appropriate tightening of the constraints and designing the terminal constraint. Under reasonable assumptions, recursive feasibility and regional input-to-state practical stability (regional ISpS) of the closed-loop system are shown. Furthermore, we extend the DRMPC framework toward the tracking problem and apply it to a nonholonomic mobile robot. The performance of the proposed approach is demonstrated by a numerical example of the nonholonomic mobile robot.
This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ...ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be separated from the background noises successfully without the annoying musical artifact commonly observed in conventional speech enhancement algorithms. A series of pilot experiments were conducted under multi-condition training with more than 100 hours of simulated speech data, resulting in a good generalization capability even in mismatched testing conditions. When compared with the logarithmic minimum mean square error approach, the proposed DNN-based algorithm tends to achieve significant improvements in terms of various objective quality measures. Furthermore, in a subjective preference evaluation with 10 listeners, 76.35% of the subjects were found to prefer DNN-based enhanced speech to that obtained with other conventional technique.
Image reconstruction is the main research problem of electrical capacitance tomography (ECT). In this article, a novel ECT image reconstruction algorithm based on an efficient sparse Bayesian ...learning (ESBL) algorithm is presented. This algorithm takes the Gaussian-scale mixture model as the prior distribution of the parameters to increase the flexibility of the model. Then, a surrogate function is used to replace the Gaussian likelihood function to reduce the computational complexity of the algorithm. Finally, the original cost function is equivalently converted into a concave-convex optimization problem through logarithm, and the block coordinate descent (BCD) method is used to solve the problem under the majorization-minimization (MM) framework. In order to verify the effectiveness of this algorithm, the Laplace distribution and the Student's T distribution are used as the prior distribution of the parameters to achieve two specific implementations of this algorithm, and simulation and experiments are carried out. Compared with the sparse Bayesian learning (SBL) algorithm, the Laplace prior-based Bayesian compress sensing (LPBCS) algorithm, the total variation (TV) algorithm, and the Landweber algorithm, the presented EBSL algorithm with the Laplace prior distribution has better image quality and fairly good real-time performance.
Laryngeal squamous cell carcinoma (LSCC) is a common malignant tumor of the head and neck. LSCC patients have seriously impaired vocal, respiratory, and swallowing functions with poor prognosis. ...Circular RNA (circRNA) has attracted great attention in cancer research. However, the expression patterns and roles of circRNAs in LSCC remain largely unknown.
RNA sequencing was performed on 57 pairs of LSCC and matched adjacent normal mucosa tissues to construct circRNA, miRNA, and mRNA expression profiles. RT-PCR, qPCR, Sanger sequencing, and FISH were undertaken to study the expression, localization, and clinical significance of circCORO1C in LSCC tissues and cells. The functions of circCORO1C in LSCC were investigated by RNAi-mediated knockdown, proliferation analysis, EdU staining, colony formation assay, Transwell assay, and apoptosis analysis. The regulatory mechanisms among circCORO1C, let-7c-5p, and PBX3 were investigated by luciferase assay, RNA immunoprecipitation, western blotting, and immunohistochemistry.
circCORO1C was highly expressed in LSCC tissues and cells, and this high expression was closely associated with the malignant progression and poor prognosis of LSCC. Knockdown of circCORO1C inhibited the proliferation, migration, invasion, and in vivo tumorigenesis of LSCC cells. Mechanistic studies revealed that circCORO1C competitively bound to let-7c-5p and prevented it from decreasing the level of PBX3, which promoted the epithelial-mesenchymal transition and finally facilitated the malignant progression of LSCC.
circCORO1C has an oncogenic role in LSCC progression and may serve as a novel target for LSCC therapy. circCORO1C expression has the potential to serve as a novel diagnostic and prognostic biomarker for LSCC detection.
Asymmetric synthesis of indole-annulated medium-sized-ring compounds is developed through an iridium-catalyzed allylic dearomatization/retro-Mannich/hydrolysis cascade reaction. The reaction features ...mild conditions and a broad substrate scope. Under the optimal conditions, various seven-, eight-, or nine-membered-ring compounds can be afforded in good to excellent yields and excellent enantioselectivity. The proposed mechanism is supported by capturing the dearomatized intermediate through in situ reduction.
This article proposes a robust self-triggered model predictive control (MPC) algorithm for a class of constrained linear systems subject to bounded additive disturbances, in which the intersampling ...time is determined by a fast convergence self-triggered mechanism. The main idea of the self-triggered mechanism is to select a sampling interval so that a rapid decrease in the predicted costs associated with optimal predicted control inputs is guaranteed. This allows for a reduction in the required computation without compromising performance. By using a constraint tightening technique and exploring the nature of the open-loop control between sampling instants, a set of minimally conservative constraints is imposed on nominal states to ensure robust constraint satisfaction. A multistep open-loop MPC optimization problem is formulated, which ensures recursive feasibility for all possible realizations of the disturbance. The closed-loop system is guaranteed to satisfy a mean-square stability condition. To further reduce the computational load, when states reach a predetermined neighborhood of the origin, the control law of the robust self-triggered MPC algorithm switches to a self-triggered local controller. A compact set in the state space is shown to be robustly asymptotically stabilized. Numerical comparisons are provided to demonstrate the effectiveness of the proposed strategies.
This paper proposes a robust self-triggered model predictive control (MPC) with an adaptive prediction horizon scheme for constrained nonlinear discrete-time systems subject to additive disturbances. ...At each triggering instant, the controller provides an optimal control sequence by solving an optimal control problem (OCP), and at the same time, determines the next triggering time and prediction horizon. By implementing the algorithm, the average sampling frequency is reduced and the prediction horizon is adaptively decreased as the system state approaches a terminal region. Meanwhile, an upper bound of performance loss is guaranteed when compared with a nominal periodic sampling MPC. Feasibility of the OCP and stability of the closed-loop system are established. Simulation results verify the effectiveness of the scheme.
This article presents a reconfigurable physically unclonable function (PUF) design fabricated using 65-nm CMOS technology. A subthreshold-inverter-based static PUF cell achieves 0.3% native bit error ...rate (BER) at 0.062-fJ per bit core energy efficiency. A flexible, native transistor-based voltage regulation scheme achieves low-overhead supply regulation with 6-mV/V line sensitivity, making the PUF resistant against voltage variations. Additionally, the PUF cell is designed to be reconfigurable with no area overhead, which enables stabilization without redundancy on chip. Thanks to the highly stable and self-regulated PUF cell and the zero-overhead stabilization scheme, a 0.00182% native BER is obtained after reconfiguration. The proposed design shows 0.12%/10 °C and 0.057%/0.1-V bit error across the military-grade temperature range from -55 °C to 125 °C and supply voltage variation from 0.7 to 1.4 V. The total energy per bit is 15.3 fJ. Furthermore, the unstable bits can be detected by sweeping the body bias instead of temperature during enrollment, thereby significantly reducing the testing costs. Last but not least, the prototype exhibits almost ideal uniqueness and randomness, with a mean inter-die Hamming distance (HD) of 0.4998 and a 1020× inter-/intra-die HD separation. It also passes both NIST 800-22 and 800-90B randomness tests.
Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes ...a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the traffic dynamics of the network regions are first transformed into MFAPC data models, and then, the derived MFAPC data models instead of mathematical traffic models serve as the prediction models in the distributed control design. The formulated control problem is finally solved with an alternating direction method of multipliers (ADMM)-based approach. The simulation results for the traffic network of Linfen, Shanxi, China, show the feasibility and effectiveness of the proposed method.
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