Recently malignant Fibrous hystiocytoma (MHF) is the common sarcoma of the soft tissue in adults. The most common sites of MHF are the extremities and retroperitoneum. This lesion is rare in the head and neck. MHF of the larynx often is seen in the old men and after radiotherapy of the head and neck cancers. We report a case of MHF of larynx, without history of previous cancer and radiotherapy, that underwent the total laryngectomy.

Objective: The development of combining mesenchymal stem cells (MSCs) with surface modified three-dimensional (3D) biomaterial scaffold provides a desirable alternative for replacement of damaged and diseased tissue. NanoFibrous scaffolds serve as suitable environment for cell attachment and proliferation due to their similarity to the physical dimension of the natural extracellular matrix. In this study the properties of plasma treated poly-C-caprolactone nanofiber scaffolds (p-PCL) and unaltered PCL scaffolds were compared, and then p-PCL scaffolds were evaluated for MSC culture.Materials and Methods: Aligned and Random PCL nanofibrus scaffolds were fabricated by electrospining and their surface modified with O2 plasma treatment to enhance MSC proliferation, adhesion and interaction. Chemical and mechanical characterizations were carried out using scanning electron microscopy (SEM), water contact angle and tensile testing. Cell adhesion and morphology were evaluated using SEM 1 day after culture. Statistical analysis was carried out using one way analysis of variance (ANOVA).Results: The proliferation of MSCs were evaluated using 3- (4, 5-Dimethylthiazol-2-yl) -2, 5-Diphenyltetrazolium Bromide (MTT) assay on day 1, 3, and 5 after cell culture. Results showed that the numbers of cells that had grown on PCL nanoFibrous scaffolds were significantly higher than those of control surfaces without nanofibers. Furthermore, the proliferation of MSCs on Random nanofiber was significantly higher compared to that on aligned nanofiber.Conclusion: This study showed that while both aligned and Random plasma treated PCL nanoFibrous scaffold are more suitable substrates for MSC growth than tissue culture plates, Random nanofiber best supported the proliferation of MSCs.

In this paper, a 3D micromechanical model is presented to study the effect of Randomly distributed fiber within the matrix on the mechanical characterizations of unidirectional fiber reinforced composites. The Representative Volume Element (RVE) of the model consists of r×c unit cells in which fibers are distributed Randomly within the matrix cells. The presented model is general and can be used to extract the stiffness properties of a Fibrous composite subjected to thermal and mechanical, normal and shear loading and also to investigate the initiation of matrix plastic deformation. Different statistical distribution models including normal, uniform and beta distribution are used to study the fiber arrangement. The obtained results for the elastic properties in which fiber distributed at Random show good agreement with both experimental data and available models. Statistical analysis of dependency of stiffness and strength properties with fiber array within the matrix is carried out by applying hypothesis testing based on the central limit theorem in each normal, uniform and beta distribution. According to the obtained numerical results, the necessary interpretation is done in each case. Numerical results of hypothesis testing show that fiber distribution has no effect on the stiffness properties of a fiber composite, while the strength properties of the composites are highly affected by the fiber arrangement.

The training algorithm of Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN model. Several methods have been proposed for training the WNNs. From the perspective of our research, most of these algorithms are iterative and need to adjust all the parameters of WNN. This paper proposes a one-step learning method which changes the weights between hidden layer and output layer of the Network; meanwhile, the wavelet function parameters are Randomly assigned and kept fixed during the training process. Besides the simplicity and speed of the proposed one-step algorithm, the experimental results verify the performance of the proposed method in terms of final model accuracy and computational time.

Background & Aim: Malignant Fibrous Histiocytoma (MFH), is the most common soft tissue sarcoma in adults and is frequently seen is elderly. This lesion is so rare in bones and if seen, is common in long bones and the incidence of the lesion in maxillofacial bones is uncommon. In addition, maxilla is an unusual site for Malignant Fibrous Histiocytoma (MFH). The aim of this article was to report a case with MFH in maxilla and to review the literature. Report of the Case: The patient was a 37 year-old man with MFH in maxilla who was referred to oral and maxillofacial surgery ambulatory ward of Taleghani hospital (2002). The specific characterization of the patient was the incidence of MFH in maxilla (which is an unusual site for this lesion) in a young age. In this article, the clinical, radiographic and histopathologic characteristics, treatment and prognosis of Malignant Fibrous Histiocytoma (MFH) are discussed. A review of related articles is also done for this rare lesion.Conclusion: With regards to different reported results of MFH follow up, the very poor prognosis of MFH in jaws, regular patient follow up after surgery with regard to its recurrence, is necessary.

Random-velour needling technology is a modified version of conventional needling process. Properties of the Random-velour needled fabric are controlled by the structural alteration that occurs during Random-velour needling, is due to re-orientation of fibers within the pre-consolidated Fibrous assembly by special fork needles. This interaction results in creation of a dual structure, comprising base and pile layers. In this work the effect of needling parameters and fiber characteristics on force exerted on the fork needle was investigated. The effect of principal parameters on total average force "Frms" exerted on individual fork needle was determined using an Artificial Neural Network (ANN) modeling and the error percentage of absolute average of predicted tests data was also calculated. Significance percentages of input parameters on "Frms" was indicative of the similar influence of fiber characteristics and needling parameters on "Frms". Results manifested the importance of punch density and barbed needle penetration depth during initial consolidating needle-felting operation. Result of the neural Network assessment testified that the Network in general was capable of mapping input and output parameters.

Background and Objectives: Embedding social Networks has attracted researchers’ attention so far. The aim of Network embedding is to learn a low-dimensional representation of each Network vertex while maintaining the structure and characteristics of the Network. Most of these existing Network embedding methods focus on only preserving the structure of Networks, but they mostly ignore the semantic and centrality-based information. Moreover, the vertices selection has been done blindly (greedy) in the existing methods.Methods: In this paper, a comprehensive algorithm entitled CSRW stands for centrality, and a semantic-based Random walk is proposed for the Network embedding process based on the main criteria of the centrality concept as well as the semantic impact of the textual information of each vertex and considering the impact of neighboring nodes. in CSRW, textual analysis based on the BTM topic modelling approach is investigated and the final display is performed using the Skip-Gram model in the Network.Results: The conducted experiments have shown the robustness of the proposed method of this paper in comparison to other existing classical approaches such as DeepWalk, CARE, CONE, COANE, and DCB in terms of vertex classification, and link prediction. And in the criterion of link prediction in a Subgraph with 5000 members, an accuracy of 0.91 has been reached for the criterion of closeness centrality and is better than other methods.Conclusion: The CSRW algorithm is scalable and has achieved higher accuracy on larger datasets.

In this paper, we analyze the effect of stress-fiber inclusion on the stiffness of an actin Random Network. We use a discrete Random Network model to analyze the elastic response of this system in terms of apparent Young’ s modulus. First, we showed that for a flat-ended cylindrical indenter the total indentation force has a linear relation with the indentation depth and the indenter radius. Using this relation, we concluded that the stiffening effect of the stress-fiber on the Fibrous Network has a range of effectivity and surprisingly, the stiffening is not maximum when the stress-fiber is straight below the indenter but when it is a certain distance from it. These results shed light on some aspects of the widely used AFM stiffness measurements of cells.