Intraperitoneal injection of thalidomide attenuates bone cancer pain and decreases spinal tumor necrosis factor-α expression in a mouse model

<p>Abstract</p> <p>Background</p> <p>Tumor necrosis factor α (TNF-α) may have a pivotal role in the genesis of mechanical allodynia and thermal hyperalgesia during inflammatory and neuropathic pain. Thalidomide has been shown to selectively inhibit TNF-α production. Previous studies have suggested that thalidomide exerts anti-nociceptive effects in various pain models, but its effects on bone cancer pain have not previously been studied. Therefore, in the present study, we investigated the effect of thalidomide on bone cancer-induced hyperalgesia and up-regulated expression of spinal TNF-α in a mouse model.</p> <p>Results</p> <p>Osteosarcoma NCTC 2472 cells were implanted into the intramedullary space of the right femurs of C3H/HeJ mice to induce ongoing bone cancer related pain behaviors. At day 5, 7, 10 and 14 after operation, the expression of TNF-α in the spinal cord was higher in tumor-bearing mice compared to the sham mice. Intraperitoneal injection of thalidomide (50 mg/kg), started at day 1 after surgery and once daily thereafter until day 7, attenuated bone cancer-evoked mechanical allodynia and thermal hyperalgesia as well as the up-regulation of TNF-α in the spinal cord.</p> <p>Conclusions</p> <p>These results suggest that thalidomide can efficiently alleviate bone cancer pain and it may be a useful alternative or adjunct therapy for bone cancer pain. Our data also suggest a role of spinal TNF-α in the development of bone cancer pain.</p

Correlation of PK/PD Indices with Resistance Selection for Cefquinome against Staphylococcus aureus in an In Vitro Model

Cefquinome is a fourth-generation Cephalosporin approved for use in animals exclusively. The objective of this study was to explore the relationship of cefquinome pharmacokinetic/pharmacodynamic (PK/PD) indices with resistance selection of Staphylococcus aureus ATCC25923 in an in vitro model. Six dosing regiments of cefquinome at an interval of 24 h for three consecutive times were simulated, resulting in maximum concentrations (Cmax) from 1/2 MIC to 16 MIC and half-lives (t1/2β) of 3 and 6 h, respectively. The in vitro sensitivity of S. aureus was monitored by bacterial susceptibility and dynamic time-kill curve experiments over the six cefquinome concentrations. The correlation between changes in bacterial susceptibility (MIC72/MIC0) and the percentage of time within mutant selection window (MSW) versus dosing interval (TMSW %) was subjected to Gaussian function and regression analysis. The results favored the consensus that time above MIC (T>MIC) was recognized as an important PK/PD parameter of cephalosporins for antibacterial efficiency. Cefquinome reached the maximum killing effect when T>MIC% attained approximately 40%~60%. The subsequent correlation analysis demonstrated that resistant S. aureus ATCC25923 was easy to occur when TMSW% attained an index of about 20% with t1/2β of 3 h after multiple dosing, and 40% with t1/2β of 6 h after multiple dosing

The Expressive Power of Graph Neural Networks: A Survey

Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement

Austenite-Bainite Transformation Kinetics in Austempered AISI 5160 Steel

This research investigates the process of the formation of bainite in austempered 5160 steel. Steel bar samples were austenitized at 1128 K for 20 minutes followed by holding at various times from 10 seconds to 2 hours and isothermal temperatures from 561K to 728K to obtain a multi-phase matrix. Micro-hardness analysis and metallurgical optical microscopy were used to analyze the experimental results. Hardness results indicated that at the 561K, 589K, and 566K isothermal temperatures for 5160 steel, lower bainite transformation occurred. However, from 644K to 728K, upper bainite transformation was found from the steel. The formation of the bainitic phase in SAE 5160 steel was characterized using thermodynamic and kinetic theories

Hexagonal Boron Nitride Thick Film Grown on a Sapphire Substrate via Low-Pressure Chemical Vapor Deposition

Hexagonal boron nitride (h-BN) with a certain thickness has wide applications in semiconductor electronic devices. In this study, the relationship between the amount of ammonia borane and the thickness of h-BN films was investigated via low-pressure chemical vapor deposition (LPCVD) on a noncatalytic c-plane Al2O3 substrate. Through various characterization methods, the grown film was confirmed to be h-BN. The effect of the precursor mass on the growth thickness of the h-BN film was studied, and it was found that the precursor mass significantly affected the growth rate of the h-BN film. The results from SEM show that the amount of ammonia borane is 2000 mg and a 1.295-μm h-BN film is obtained. It will provide an experimental reference for the growth of thicker h-BN materials to prepare high-efficiency neutron detectors for radiation detection

ERK in Learning and Memory: A Review of Recent Research

The extracellular signal-regulated kinase (ERK) pathway is a member of the mitogen-activated protein kinase (MAPK) superfamily, which is an important, highly conserved family of enzymes associated with cell membrane receptors and regulative targets. In the central nervous system, there is almost no mature neuronal proliferation and differentiation, but the regulation of MAPK and its upstream and downstream molecular pathways is still widespread, with the ERK signaling pathway being one of the most actively studied signal transduction pathways. It is activated by a variety of cell growth factors and substances which promote mitotic activity, and transmits extracellular signals from the cell surface to the nucleus, which transmission plays an important role in the process of cell proliferation and differentiation. In recent years, accumulating evidence has shown that the ERK signaling pathway has an important link with the higher functions of learning and memory

Active and Reactive Power Collaborative Optimization for Active Distribution Networks Considering Bi-Directional V2G Behavior

Due to their great potential for energy conservation and emission reduction, electric vehicles (EVs) have attracted the attention of governments around the world and become more popular. However, the high penetration rate of EVs has brought great challenges to the operation of the Active Distribution Network (ADN). On the other hand, EVs will be equipped with more intelligent chargers in the future, which supports the EVs’ high flexibility in both active and reactive power control. In this paper, a distributed optimization model of ADN is proposed by employing the collaborative active and reactive power control capability of EVs. Firstly, the preference of EV users is taken into account and the charging mode of EVs is divided into three categories: rated power charging, non-discharging, and flexible charging–discharging. Then, the reactive power compensation capacity of the plugged-in EV is deduced based on the circuit topology of the intelligent charger and the active–reactive power control model of the EV is established subsequently. Secondly, considering the operation constraints of ADN and the charging–discharging constraints of EVs over the operation planning horizon, the optimization objective of the model is proposed, which consists of two parts: “minimizing energy cost” and “improving voltage profile”. Finally, a distributed solution method is proposed based on the Alternating Direction Method of Multipliers (ADMM). The proposed model is implemented on a 33-bus ADN. The obtained results demonstrate that it is beneficial to achieve lower energy cost and increase the voltage profile of the ADN. In addition, the energy demand of EV batteries in their plugin intervals is met, and the demand preference of EV users is guaranteed

Improved Synchronized Space Vector PWM Strategy for Three-Level Inverter at Low Modulation Index

Aimed at reducing the switching loss and common-mode voltage amplitude of high-power medium-voltage three-level inverter under low modulation index conditions, an improved synchronous space vector PWM strategy is proposed in this paper. The switching times in each fundamental period are reduced by the re-division of small regions and the full use of the redundant switching state. The sum of switching algebra is introduced as an evaluation index and the switching state with the minimum value of the sum of switching algebra are adopted. Then, the common mode voltage amplitude is reduced. The theoretical analysis and experimental results show that the improved modulation strategy proposed in this paper can effectively reduce the switching loss and common-mode voltage amplitude of the inverter under the condition of the low modulation index. Moreover, the neutral-point voltage ripple is also reduced simultaneously