The effect of gabapentin versus intrathecal fentanyl on postoperative pain and morphine consumption in cesarean delivery: a prospective, randomized, double‑blind study

Pain after cesarean delivery is a leading cause of chronic pain and there are many attempts to reduce it without total success. Gabapentin is effective in reducing acute and chronic pain with little experience in parturient. The purpose of this study is to compare the effect of pre-emptive gabapentin with intrathecal fentanyl on reducing postoperative pain and morphine consumption in cesarean surgery. METHODS: Seventy-eight primiparous women who scheduled for non-emergency cesarean delivery were enrolled in the study and separated into two groups. The control group received 12.5 mg of heavy bupivacaine 0.5 % plus 10 μg of fentanyl intrathecally and the case group received 300 mg of gabapentin orally 2 h before surgery and 12.5 mg of heavy bupivacaine 0.5 % intrathecally. Data collection including blood pressure, heart rate, neonate sedation, Apgar score, visual analogous scale at several hours, at first, need to analgesic postoperatively. RESULTS: In the fentanyl group, the need for analgesic drug was earlier, total dose of morphine in 24 h and patient satisfaction was higher than the gabapentin group. The mean visual analogous scale at several hours postoperatively in the fentanyl groups was significantly higher than the gabapentin groups (P = 0.001). CONCLUSION: Preemptive use of gabapentin is a safe and effective way to reduce postoperative pain and morphine consumption in parturients after cesarean surgery

Clustering based on Mixtures of Sparse Gaussian Processes

Creating low dimensional representations of a high dimensional data set is an important component in many machine learning applications. How to cluster data using their low dimensional embedded space is still a challenging problem in machine learning. In this article, we focus on proposing a joint formulation for both clustering and dimensionality reduction. When a probabilistic model is desired, one possible solution is to use the mixture models in which both cluster indicator and low dimensional space are learned. Our algorithm is based on a mixture of sparse Gaussian processes, which is called Sparse Gaussian Process Mixture Clustering (SGP-MIC). The main advantages to our approach over existing methods are that the probabilistic nature of this model provides more advantages over existing deterministic methods, it is straightforward to construct non-linear generalizations of the model, and applying a sparse model and an efficient variational EM approximation help to speed up the algorithm

Introducing the immunomodulatory effects of mesenchymal stem cells in an experimental model of Behçet’s disease

AbstractBehçet’s disease (BD) is a systemic vasculitis which is characterised by oral, aphthous ulcers, genital ulcers, skin lesions and ocular manifestations. Although the aetiopathogenesis of BD is still unknown, the critical role of Th1 immune responses, neutrophil hyperactivation alongside overproduction of pro-inflammatory cytokines such as interleukin-1 (IL-1), IL-6, IL-8, tumour necrosis factor-alpha (TNFα) and particularly IL-17 have been demonstrated in the immunopathogenesis of the disease. Despite significant progress in understanding of the aetiology of the disease, its treatment remains intricate, and is still treated with immune-suppressive drugs and biological agents with probable systemic side effects. Accordingly, there is a necessity to establish the more efficient and less toxic therapeutic methods which may offer a long-time remission of BD.Mesenchymal stem cells (MSCs) are non-haematopoietic and multipotential stem cells with immunosuppressive capacities in innate and acquired immune systems. MSCs can migrate to damaged tissues and prevent secretion of proinflammatory cytokines and other immunomodulatory effectors, increasing the survival of damaged cells, although the exact underlying mechanisms are still unknown. For this purpose, numerous herpes simplex viruses are injected into C57BL/6 mice to produce Behçet’s mouse model and transferring a certain number of MSCs may have therapeutic value for control of Behçet’s animal model, so researchers could deliberate the function of MSCs and proinflammatory cytokines particularly IL-17A-F, TNF-α, interferon gamma (IFN-γ), IL-2, IL-6 and IL-8 in an experimental model.The aim of this hypothesis is to evaluate immunosuppressive and immunomodulatory properties of MSCs in syngeneic animal model for BD, in order to clarify the mechanisms of MSCs in BD management, as a broad and more confident treatment in clinical application

Graph Embedding Using Frequency Filtering

The target of graph embedding is to embed graphs in vector space such that the embedded feature vectors follow the differences and similarities of the source graphs. In this paper, a novel method named Frequency Filtering Embedding (FFE) is proposed which uses graph Fourier transform and Frequency filtering as a graph Fourier domain operator for graph feature extraction. Frequency filtering amplifies or attenuates selected frequencies using appropriate filter functions. Here, heat, anti-heat, part-sine and identity filter sets are proposed as the filter functions. A generalized version of FFE named GeFFE is also proposed by defining pseudo-Fourier operators. This method can be considered as a general framework for formulating some previously defined invariants in other works by choosing a suitable filter bank and defining suitable pseudo-Fourier operators. This flexibility empowers GeFFE to adapt itself to the properties of each graph dataset unlike the previous spectral embedding methods and leads to superior classification accuracy relative to the others. Utilizing the proposed part-sine filter set which its members filter different parts of the spectrum in turn improves the classification accuracy of GeFFE method. Additionally, GeFFE resolves the cospectrality problem entirely in tested datasets

Diffusion Wavelet Embedding: a Multi-resolution Approach for Graph Embedding in Vector Space

In this article, we propose a multiscale method of embedding a graph into a vector space using diffusion wavelets. At each scale, we extract a detail subspace and a corresponding lower-scale approximation subspace to represent the graph. Representative features are then extracted at each scale to provide a scale-space description of the graph. The lower-scale is constructed using a super-node merging strategy based on nearest neighbor or maximum participation and the new adjacency matrix is generated using vertex identification. This approach allows the comparison of graphs where the important structural differences may be present at varying scales. Additionally, this method can improve the differentiating power of the embedded vectors and this property reduces the possibility of cospectrality typical in spectral methods, substantially. The experimental results show that augmenting the features of abstract levels to the graph features increases the graph classification accuracies in different datasets