Bone cancer pain could lead to pain sensitization. Traditional Chinese Medicine could relieve bone cancer pain (BCP). This study aimed to investigate the analgesic effect of Bushen Tongluo decoction on rats with BCP and its impact on ERK/c-fos pathway in spinal dorsal horn. Cancer cells were injected to induce bone cancer pain rats. Inflammatory factors in serum were determined using enzyme-linked immunosorbent. ERK/c-Fos in the spinal dorsal horn were detected using western blotting and RT-qPCR. Thermal hyperalgesia and mechanical allodynia were observed in BCP rats. The ERK/c-Fos pathway activation was observed in the spinal dorsal horn and the expression of inflammatory cytokines increased in the serum. Bushen Tongluo decoction alleviated inflammatory cytokines and reduced the ERK/c-Fos pathway. We provided evidence that Bushen Tongluo decoction exhibits a potential and beneficial effect on inflammatory cytokines, effectively alleviating allodynia and hyperalgesia in rats with bone cancer. This effect may be attributed to down-regulation of the ERK/c-Fos pathway in spinal dorsal horn and serum inflammatory cytokines.
Objective
To investigate the changes of cAMP response element-binding protein (CREB) and p-CREB in the anterior cingulate cortex after dry needling treatment in myofascial trigger points (MTrPs) rats.
Methods
Forty male SD rats of specific pathogen free, aged 6 weeks, were randomly assigned into 4 groups: control group (CG), model group (MG), dry needling on MTrPs group (TG) and dry needling on non-MTrPs group (NTG). The rat model of chronic MTrPs was established by striking on gastrocnemius muscles and eccentric exercise in a period of 12 weeks. Dry needling on MTrPs and non-MTrPs treatment were performed in TG and NTG, once a week, lasted four weeks respectively. Pain threshold was tested before and after treatment. The expression of CREB and p-CREB were detected by western blot analysis.
Results
Compared with MG, pain threshold was significantly increased on the 3rd and 4th week in TG, but only significantly increased on the 3rd week in NTG. Compared with CG, the expression of CREB and p-CREB were significantly increased in MG and NTG. Compared with MG and NTG, the expression of CREB and p-CREB was significantly decreased in TG.
Conclusion
Dry needling on MTrPs can increase pain threshold and decrease the expression of CREB and p-CREB level in the anterior cingulate cortex in the rat model.
Key words:
Myofascial pain syndromes; Acupuncture; cyclic AMP response element-binding protein; Pain measurement
Inverse heat conduction problems (IHCPs) are problems of estimating unknown quantities of interest (QoIs) of the heat conduction with given temperature observations. The challenge of IHCPs is that it is usually ill-posed since the observations are noisy, and the estimations of QoIs are generally not unique or unstable, especially when there are unknown spatially varying QoIs. In this study, an ensemble physics-informed neural network (E-PINN) is proposed to handle function estimation and uncertainty quantification of space-dependent IHCPs. The distinctive characteristics of E-PINN are ensemble learning and adversarial training (AT). Compared with other data-driven UQ approaches, the suggested method is more than straightforward to implement and also achieves high-quality uncertainty estimates of the QoI. Furthermore, an adaptive active sampling (AS) strategy based on the uncertainty estimates from E-PINNs is also proposed to improve the accuracy of material field inversion problems. Finally, the proposed method is validated through several numerical experiments of IHCPs.
Abstract In gradient-based time-domaintopology optimization, Design Sensitivity Analysis (DSA) of the dynamic response is essential, and requires high computational cost to directly differentiate, especially for high-order dynamic system. To address this issue, this study develops an efficient Reduced Basis Method(RBM)-based discrete adjoint sensitivity analysis method, which on the one hand significantly improves the efficiency of sensitivity analysis and on the other hand avoids the consistency errors caused by the continuum method. In this algorithm, the basis functions of the adjoint problem are constructed in the offline phase based on the greedy-POD method, and a novel model-based estimator is developed to accurately predict the true error for facilitating this process. Based on these basis functions, a fast and reasonably accurate model is then built by Galerkin projection for sensitivity analysis in each dynamic topology optimization iteration. Finally, the efficiency and accuracy of the suggest method are verified by 2D and 3D dynamic structure studies.
The Bayesian inference approach is widely used to tackle inverse problems due to its versatile and natural ability to handle ill-posedness. However, it often faces challenges when dealing with situations involving continuous fields or large-resolution discrete representations (high-dimensional). Moreover, the prior distribution of unknown parameters is commonly difficult to be determined. In this study, an Operator Learning-based Generative Adversarial Network (OL-GAN) is proposed and integrated into the Bayesian inference framework to handle these issues. Unlike most Bayesian approaches, the distinctive characteristic of the proposed method is to learn the joint distribution of parameters and responses. By leveraging the trained generative model, the posteriors of the unknown parameters can theoretically be approximated by any sampling algorithm (e.g., Markov Chain Monte Carlo, MCMC) in a low-dimensional latent space shared by the components of the joint distribution. The latent space is typically a simple and easy-to-sample distribution (e.g., Gaussian, uniform), which significantly reduces the computational cost associated with the Bayesian inference while avoiding prior selection concerns. Furthermore, incorporating operator learning enables resolution-independent in the generator. Predictions can be obtained at desired coordinates, and inversions can be performed even if the observation data are misaligned with the training data. Finally, the effectiveness of the proposed method is validated through several numerical experiments.
In this study, an image-assisted Approximate Bayesian Computation (ABC) parameter inverse method is proposed to identify the design parameters. In the proposed method, the images are mapped to a low-dimensional latent space by Variational Auto-Encoder (VAE), and the information loss is minimized by network training. Therefore, an effective trade-off between information loss and computational cost can be achieved by using the latent variables of VAE as summary statistics of ABC, which overcomes the difficulty of selecting summary statistics in the ABC. Besides, for some practical engineering problems, processing the images as objective function can effective show the response result. Meanwhile, the relationship between design parameters and the latent variables is constructed by Least Squares Support Vector Regression (LSSVR) surrogate model. With the well-constructed LSSVR model, the simulation coefficient vectors under given parameters will be determined effectively. Then, the parameters to be identified are determined by comparing the simulated and observed coefficient vectors in ABC. Finally, a sheet forming problem is investgated by the suggested method. The material parameters of the blank and the process parameters of the forming process are identified. Results show that the method is feasibility and effective for the identification of sheet forming parameters.
The high-belite calcium sulfoaluminate cement (HB-CSA) has displayed outstanding acid resistance. However, further research is needed to understand the mechanism behind its acid resistance fully. This study investigates the resistance of a new type of HB-CSA to hydrochloric and sulfuric acid. Additionally, the impact of the ye'elimite (C 4 A 3 S¯ )-to-gypsum (C S¯ ) ratio on the acid resistance of HB-CSA is discussed. The resistance performance is investigated by flexural strength and resistance index analyses, X-ray diffraction (XRD), thermogravimetric analysis (TGA), electronic computed tomography (CT) scans, and scanning electron microscopy (SEM). The results show that the HB-CSA exhibits superior resistance to sulfuric acid and hydrochloric acid compared to Portland cement. The absence of portlandite contributes to the enhanced performance of HB-CSA. In addition, as the C 4 A 3 S¯ -to-C S¯ ratio decreases, the content of Aft in HB-CSA increases, leading to an increase in the acid resistance of HB-CSA.
How to solve inverse problems is the challenge of many engineering and industrial applications. Recently, physics-informed neural networks (PINNs) have emerged as a powerful approach to solve inverse problems efficiently. However, it is difficult for PINNs to quantify the uncertainty of results. Therefore, this study proposed ensemble PINNs (E-PINNs) to handle this issue. The E-PINN uses ensemble statistics of several basic models to provide uncertainty quantifications for the inverse solution based on the PINN framework, and it is employed to solve the inverse problems in which the unknown quantity is propagated through partial differential equations (PDEs), especially the identification of the unknown field (e.g., space function) of a given physical system. Compared with other data-driven approaches, the suggested method is more than straightforward to implement, and also obtains high-quality uncertainty estimates of the quantity of interest (QoI) without significantly increasing the complexity of the algorithm. This work discusses the good properties of ensemble learning in field inversion and uncertainty quantification. The effectiveness of the proposed method is demonstrated through several numerical experiments. To enhance the robustness of models, adversarial training (AT) is applied. Furthermore, an adaptive active sampling (AS) strategy based on the uncertainty estimates from E-PINNs is also proposed to improve the accuracy of material field inversion problems.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)