Current Signal Transduction Therapy - Volume 15, Issue 2, 2020

Background: Some interpolators cannot be used in an image magnification problem in a freely scalable form. For instance, when we want to magnify an image to a 16-time bigger scale, some interpolators have to do this process in two steps including two 4-time magnification steps, however, some are able to do it directly. Materials and Methods: For generating data of this study, MATLAB as a simulator has been used. Bi-; Linear (BL) and Cubic Convolution (CC) interpolators are the two applied re-samplers in the reconstruction of digital images. Results: Data shows that the performance of both free-size interpolators (BL and CC) is obviously different in both direct and indirect pixel reconstruction. Conclusion: The acquired data shows a less error in the condition of direct interpolation. The relative results of experiments are different from the type of core interpolators (BL and CC).

Background: 3,4-Dihydro-6-[4-{3,4-dimethoxybenzoyl}-1-piperazinyl]-2(1H)-quinolinone (vesnarinone), a novel inotropic drug with unique and complex mechanisms of action, is known to show antitumor activity against several human malignancies. In the present study, vesnarinone-induced signal transduction of S-phase kinase-associated protein 2 (Skp2) and Nuclear Factor-kappa Beta (NF-ΚB) as molecular targets of oral malignant Burkitt’s lymphoma (Raji cells) was evaluated. Materials and Methods: Raji cells were incubated with vesnarinone at concentrations of 0, 1.25x10-2, 2.50x10-2, or 5.0x10-2 Molar. After 24 h, chemotactic cell migration was examined by a Boyden chamber kit. Apoptosis induction was observed by caspase-9 colorimetric assay. To evaluate levels of Skp2, NF-kB, and α-tubulin, Western blot analysis was performed. Results: Vesnarinone markedly suppressed chemotactic cell migration and significantly induced apoptosis by increasing the caspase-9 activity of Raji cells through down regulation of Skp2 and NF-ΚB. Conclusion: Vesnarinone decreased the expression of Skp2 and NF-ΚB indicating these molecules may be targeted for the treatment of oral malignant Burkitt’s lymphoma (BL). The results of this work offer a promising therapeutic approach for BL tumors.

Background: Accurate detection of brain tumor and its severity is a challenging task in the medical field. So there is a need for developing brain tumor detecting algorithms and it is an emerging one for diagnosis, planning the treatment and outcome evaluation. Materials and Methods: Brain tumor segmentation method using deep learning classification and multi-modal composition has been developed using the deep convolutional neural networks. The different modalities of MRI such as T1, flair, T1C and T2 are given as input for the proposed method. The MR images from the different modalities are used in proportion to the information contents in the particular modality. The weights for the different modalities are calculated blockwise and the standard deviation of the block is taken as a proxy for the information content of the block. Then the convolution is performed between the input image of the T1, flair, T1C and T2 MR images and corresponding to the weight of the T1, flair, T1C, and T2 images. The convolution is summed between the different modalities of the MR images and its corresponding weight of the different modalities of the MR images to obtain a new composite image which is given as an input image to the deep convolutional neural network. The deep convolutional neural network performs segmentation through the different layers of CNN and different filter operations are performed in each layer to obtain the enhanced classification and segmented spatial consistency results. The analysis of the proposed method shows that the discriminatory information from the different modalities is effectively combined to increase the overall accuracy of segmentation. Results: The proposed deep convolutional neural network for brain tumor segmentation method has been analysed by using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013). The complete, core and enhancing regions are validated with Dice Similarity Coefficient and Jaccard similarity index metric for the Challenge, Leaderboard, and Synthetic data set. To evaluate the classification rates, the metrics such as accuracy, precision, sensitivity, specificity, under-segmentation, incorrect segmentation and over segmentation also evaluated and compared with the existing methods. Experimental results exhibit a higher degree of precision in the segmentation compared to existing methods. Conclusion: In this work, deep convolution neural network with different modalities of MR image are used to detect the brain tumor. The new input image was created by convoluting the input image of the different modalities and their weights. The weights are determined using the standard deviation of the block. Segmentation accuracy is high with efficient appearance and spatial consistency. The assessment of segmented images is completely evaluated by using well-established metrics. In future, the proposed method will be considered and evaluated with other databases and the segmentation accuracy results should be analysed with the presence of different kind of noises.

Background: Image segmentation is an important problem in both image processing and computer vision. Given an image, the goal of image segmentation is to label each pixel in the image such that the pixels with the same label collectively represent an object. Materials and Methods: Due to the inherent complexity and noise that may exist in images, developing an algorithm that can generate excellent segmentation results for an arbitrary image is still a challenging problem. In this paper, a new adaptive Hidden Markov Model is developed to describe the spatial and semantic relationships among pixels in an image. Based on this statistical model, image segmentation can be efficiently performed with an adaptive Viterbi algorithm in linear time. Results: The algorithm is unsupervised and does not require being used along with any other approach in image segmentation. Testing results on synthetic and real images show that this algorithm is able to achieve excellent segmentation results in both ordinary images and medical images. Conclusion: An implementation of this algorithm in MATLAB is freely available upon request.

Background: Recent advances in medical associated technologies have drastically increased the amount of electronic medical records collected, stored and transferred through the network. Considering the significance and level of sensitivity of the collected medical data, the security of the transmitted data has become a very vital and challenging task for researchers. The protection of these medical images with embedded data is usually guaranteed using encryption or data hiding techniques. Conventional techniques that employ encryption or data hiding are often insecure and also time-consuming during transmission through the network. Materials and Methods: A method combining encryption and data hiding together can result in compression of data that reduces the transmission time and increases the security level. Reversible data hiding in images can reestablish the cover image after extracting the hidden embedded data exclusive of alterations. Here a new reversible crypto-watermarking system is proposed using cryptographic algorithms that encrypts and hides an Electronic Patient Record (EPR) into an image corresponding to that patient using Rhombus Prediction Scheme. It embeds a big amount of encrypted data into an image with hardly noticeable modification using spatial pixel manipulations based on prediction errors. The marked image is hashed using SHA-256 algorithm. Results and Conclusion: Hashing and cryptography increases the robustness and guarantees authenticity with integrity. The proposed method results in improved safety with a lower transmission time than the existing methods.

Background: Achieving the best possible classification accuracy is the main purpose of each pattern recognition scheme. An interesting area of classifier design is to design for biomedical signal and image processing. Materials and Methods: In the current work, in order to increase recognition accuracy, a theoretical frame for combination of classifiers is developed. This method uses different pattern representations to show that a wide range of existing algorithms could be incorporated as the particular cases of compound classification where all the pattern representations are used jointly to make an accurate decision. Results: The results show that the combination rules developed under the Naive Bayes and Fuzzy integral method outperforms other classifier combination schemes. Conclusions: The performance of different combination schemes has been studied through an experimental comparison of different classifier combination plans. The dataset used in the article has been obtained from biological signals.

Background: Real-time video coding is a very interesting area of research with extensive applications into remote sensing and medical imaging. Many research works and multimedia standards for this purpose have been developed. Some processing ideas in the area are focused on second-step (additional) compression of videos coded by existing standards like MPEG 4.14. Materials and Methods: In this article, an evaluation of some techniques with different complexity orders for video compression problem is performed. All compared techniques are based on interpolation algorithms in spatial domain. In details, the acquired data is according to four different interpolators in terms of computational complexity including fixed weights quartered interpolation (FWQI) technique, Nearest Neighbor (NN), Bi-Linear (BL) and Cubic Cnvolution (CC) interpolators. They are used for the compression of some HD color videos in real-time applications, real frames of video synthetic aperture radar (video SAR or ViSAR) and a high resolution medical sample. Results: Comparative results are also described for three different metrics including two reference- based Quality Assessment (QA) measures and an edge preservation factor to achieve a general perception of various dimensions of the mentioned problem. Conclusion: Comparisons show that there is a decidable trade-off among video codecs in terms of more similarity to a reference, preserving high frequency edge information and having low computational complexity.

Background: Hedgehog pathway plays a crucial role in the neovascularisation and angiogenesis during the embryonic stage in humans. Three genes of hedgehog protein isolated from humans are Sonic hedgehog, Desert hedgehog and Indian hedgehog gene. Two G-protein coupled receptors identified in the sonic hedgehog pathway served as patched receptor and smoothened receptor. Materials and Methods: Particularly, sonic hedgehog gene plays a versatile role in cellular homeostasis and can be a novel therapeutic target in the prevention of cardiovascular disorders. Further various sonic hedgehog modulators have been reported working as futuristic drug molecules in the modulation of cardiovascular dysfunctions. Results: However, there was limited literature availability that has summarized the possible mechanism of targeting Sonic hedgehog signaling pathway. Conclusion: Thus, the present review is aimed at exploring the role of targeting sonic hedgehog protein signaling and modulators as well as to enlighten that how targeting sonic hedgehog protein involves in the amelioration of atherosclerosis, ischemic heart diseases, vascular endothelial dysfunction, heart failure and congenital heart diseases.

Background: Salivary Adenoid Cystic Carcinoma (ACC) is a malignant tumor located at oral and maxillofacial regions, and its conventional treatments are surgery, chemotherapy and radiotherapy. However, its poor survival rates and prognosis resulting from the molecular mechanisms underlying the carcinogenesis remain obscure. To date, there are insufficient reviews to summarize the genes and molecular pathways for ACC. Therefore, it is required for us to highlight the main oncogenes, tumor suppressor genes and genetic signal transduction pathways associated with ACC in this review. Materials and Methods: A literature review based on PubMed for the genetic characteristics and molecular transduction pathways for ACC was conducted. Ninety articles were selected as references using the search terms or keywords such as “genes, molecular pathways, salivary adenoid cystic carcinoma or ACC”. Results: We have briefly described histopathology, current treatments and main clinical features in ACC. Besides, we have also elaborated the associated genes and pathways in this review according to the searched articles in recent years. Conclusion: We have summarized vital genes and proteins targeting or mechanism-based on proliferation, apoptosis, invasion and metastasis. Although there are few kinds of research on ACC currently exist, we expect that better detailed genetic studies would pave the way for promising advancement in our understanding of the molecular biology and pathogenesis mechanisms underlying tumors.

Background: The presence of speckle noise in synthetic aperture radar (SAR) images makes the images of low quality in terms of textural features and spatial resolution which are required for processing issues such as image classification and clustering. Already, there are many adaptive filters to remove noise in SAR images. ENVI software is a fully applicable tool for this purpose which has a good library including several filters in the classes of adaptive, orderstatistics and non-linear filters. Materials and Methods: In this study, the toolbox of ENVI is reviewed, analyzed and then numerically evaluated based on several single-band images along with multi-band polarimetric SAR (Pol-SAR) images achieved from SAR sensors such as TerraSAR-X. For evaluation, two metrics including Equivalent Number of Looks (ENL) and Edge Preservation Index (EPI) are used which show the ability of the filters in preserving jointly spatial/textural features based on general information and edges quality, respectively. Results: It is notable that both metrics illustrate that some classic filters are better in comparison to newer filters. Conclusion: The experiments can help us in selecting a better filter towards our aims. In this respect, attention to the results of commercial filters of ENVI software and their analysis can guide us to find the best case in order to process commercial data of SAR sensors in the applications of environmental monitoring, geo-science studies, industrial usages and so on.

Introduction: Chronic Kidney Disease (CKD) is a highly prevalent condition and it is a major risk factor for End-Stage Renal Disease (ESRD), cardiovascular disease, and premature death. Some congenital and acquired anomalies of the kidneys and lower urinary tract (CAKUT and CALUT) are well-known causes of CKD and ESRD, but often remain undiagnosed and their prevalence is underestimated. This study aims to provide an overview that considered mainly some of the major congenital and acquired urological diseases that could lead to renal clinical manifestations common even to the most widespread renal pathologies, for which often underdiagnosed. Materials and Methods: PubMed search was conducted for available English literature describing the actual knowledge on congenital and acquired urological disorders determining acute and chronic kidney disease. Prospective and retrospective studies as well as meta-analyses and latest systematic reviews were included. Results: Most of the studies examined and reviewed were discarded for wrong population or intervention or deemed unfit, and only 87 met the inclusion criteria for the review. The studies included in the review related to urological disorders that may determine chronic and acute kidney disease. Conclusion: Some urological diseases, as CAKUT and CALUT, especially in adults, show symptoms, as renal failure, proteinuria and hypertension, very common to other kidney diseases, for this reason may remain undiagnosed and their prevalence is not completely known. Therefore, in doubtful cases, non-invasive and inexpensive tests, as cystourethrogram, should be made, to rule out urological disorders and if necessary, ultrasonography, urography and scintigraphy, might allow a correct and early diagnosis of these defects and thus adequate therapy, preventing or at least slowing down an evolution toward CKD and ESRD.