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Tumor is the major cause of death all around the world in recent days. Early detection and prediction of a cancer type are important for a patient's well-being. Functional genomic data has recently been used in the effective and early detection of cancer. According to previous research, the use of microarray data in cancer prediction has evidenced two main problems as high dimensionality and limited sample size. Several researchers have used numerous statistical and machine learning-based methods to classify cancer types but still, limitations are there which makes cancer classification a difficult job. Deep Learning (DL) and Convolutional Neural Networks (CNN) have been proven with effective analyses of unstructured data including gene expression data. In the proposed method gene expression data for five types of cancer is collected from The Cancer Genome Atlas (TCGA). Prominent features are selected using a hybrid Particle Swarm Optimization (PSO) and Random Forest (RF) algorithm followed by the use of Principal Component Analysis (PCA) for dimensionality reduction. Finally, for classification blend of Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) is used to predict the target type of cancer. Experimental results demonstrate that accuracy of the proposed method is 96.89%. As compared to existing work, our method outperformed with better results.

Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios., Competing Interests: Conflict of interestThe authors declare that they have no conflicts of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Celiac Disease (CeD) is a chronic small intestinal immune-mediated enteropathy caused by the ingestion of dietary gluten proteins in genetically susceptible individuals. CeD is one of the most common autoimmune diseases, affecting around 1.4% of the population globally. To date, the only acceptable treatment for CeD is strict, lifelong adherence to a gluten-free diet (GFD). However, in some cases, GFD does not alter gluten-induced symptoms. In addition, strict adherence to a GFD reduces patients' quality of life and is often a socio-economic burden. This narrative review offers an interdisciplinary overview of CeD pathomechanism and the limitations of GFD, focusing on current research on possible dietary interventions. It concentrates on the recent research on the degradation of gluten through enzymes, the modulation of the microbiome, and the different types of "biotics" strategies, from probiotics to the less explored "viromebiotics" as possible beneficial complementary interventions for CeD management. The final aim is to set the context for future research that may consider the role of gluten proteins and the microbiome in nutritional and non-pharmacological interventions for CeD beyond the sole use of the GFD.

Tensile strength is one of the important mechanical properties of concrete, but it is difficult to measure accurately due to the brittle nature of concrete in tension. The three widely used test methods for measuring the tensile strength of concrete each have their shortcomings: the direct tension test equipment is not easy to set up, particularly for alignment, and there are no standard test specifications; the tensile strengths obtained from the test method of splitting tensile strength (American Society for Testing and Materials, ASTM C496) and that of flexural strength of concrete (ASTM C78) are significantly different from the actual tensile strength owing to mechanisms of methodologies and test setup. The objective of this research is to develop a new concrete tensile strength test method that is easy to conduct and the result is close to the direct tension strength. By applying the strut-and-tie concept and modifying the experimental design of the ASTM C78, a new concrete tensile strength test method is proposed. The test results show that the concrete tensile strength obtained by this proposed method is close to the value obtained from the direct tension test for concrete with compressive strengths from 25 to 55 MPa. It shows that this innovative test method, which is precise and easy to conduct, can be an effective alternative for tensile strength of concrete.

Peptidase therapy is suggested to be effective to minimize gliadin toxicity in celiac disease (CD). Hence, present study deals with gliadin-hydrolysing peptidases. The efficient peptidase from the Bacillus tequilensis was purified using ammonium sulfate fractionation and preparative electrophoresis. Analysis of in-solution and in-gel hydrolysis of gliadin using one and two-dimensional SDS-PAGE revealed nearly complete hydrolysis of gliadin peptides after 180 min of incubation with B. tequilensis protease. Purified peptidase was found to be stable at acidic (pH 3.5) to neutral (pH 7.2) pH range. The molecular mass and isoelectric point of the peptidase were observed around 29 kDa and 5.2, respectively. The internal protein sequence obtained through mass spectrometric analysis suggested that peptidase might belong to peptidase S9 family known for prolyl-specific peptidases. This study recommends the possible applicability of this peptidase for elimination of immunotoxic gliadin peptides and may prove useful in CD treatment.

This paper evaluates α-amylase inhibitor (α-AI) mediated defense of pigeonpea against Helicoverpa armigera. A bifunctional α-amylase/trypsin inhibitor was purified from the seeds of pigeonpea by native liquid phase isoelectric focusing (N-LP-IEF), affinity chromatography and preparative electrophoresis. Its in-vivo and in-vitro interaction with midgut amylases of H. armigera was studied along with growth inhibitory activity. One and two dimensional (2D) zymographic analyses revealed that the purified inhibitor is dimeric glycoprotein (60.2kDa and 56kDa) exist in a multi-isomeric form with five pI variants (pI 5.5 to 6.3). It was found to be heat labile with complete inactivation up to 80°C and stable over a wide range of pH (4-11). The slow binding and competitive type of α-amylase inhibition was observed with 0.08μM of dissociation constant (Ki) for the enzyme-inhibitor complex (EI). The internal protein sequence of two subunits obtained by mass spectrometry matched with cereal-type α-AI, a conserved domain from AAI_LTSS superfamily and sialyltransferase-like protein respectively. In-vivo studies indicated up-regulation of total midgut α-amylase activity with negative effect on growth rate of H. armigera suggesting its suitability for pest control., (Copyright © 2015 Elsevier B.V. All rights reserved.)