EVALUATION OF CARDIOPROTECTIVE ACTIVITY OF ALLIUM CEPA AERIAL LEAVES

Objective: The present work deals with the study of the ethanolic extract of fruits of Allium cepa aerial leaves for cardioprotective activity.Methods: Cardioprotective activity of the ethanolic extract of aerial leaves of Allium cepa was determined by the administration of isoproterenol (60 mg/kg, s. c) for two days.Results: Pretreatment with ethanolic extract of Allium cepa aerial leaves (200 mg/kg, p. o and 100 mg/kg, p. o) for 28 d in significantly (p<0.01) reduce the levels of serum transaminases, alkaline phosphates, lactate dehydrogenase, creatinine kinase, total cholesterol, triglycerides, LDL-cholesterol, VLDL-cholesterol and increase the levels of HDL-cholesterol. Histopathological studies of the hearts of isoproterenol treated rats have showed infiltration of inflammatory cells and lacking of continuity in muscle fiber was suggesting an irreversible cell injury.Conclusion: Animals treated with ethanolic extract of Allium cepa aerial leaves showed less degenerative changes compared to isoproterenol-treated animals

Effect of gas flow on electronic transport in a DNA-decorated carbon nanotube

We calculate the two-time current correlation function using the experimental data of the current-time characteristics of the Gas-DNA-decorated carbon nanotube field effect transistor. The pattern of the correlation function is a measure of the sensitivity and selectivity of the sensors and suggest that these gas flow sensors may also be used as DNA sequence detectors. The system is modelled by a one-dimensional tight-binding Hamiltonian and we present analytical calculations of quantum electronic transport for the system using the time-dependent nonequilibrium Green's function formalism and the adiabatic expansion. The zeroth and first order contributions to the current $I^{(0)}(\bar{t})$ and $I^{(1)}(\bar{t})$ are calculated, where $I^{(0)} (\bar{t})$ is the Landauer formula. The formula for the time-dependent current is then used to compare the theoretical results with the experiment.Comment: 14 pages, 5 figures and 2 table

Identification of Sickle Cell Anemia Using Deep Neural Networks

A molecule called hemoglobin is found in red blood cells that holds oxygen all over the body. Hemoglobin is elastic, round, and stable in a healthy human. This makes it possible to float across red blood cells. But the composition of hemoglobin is unhealthy if you have sickle cell disease. It refers to compact and bent red blood cells. The odd cells obstruct the flow of blood. It is dangerous and can result in severe discomfort, organ damage, heart strokes, and other symptoms. The human life expectancy can be shortened as well. The early identification of sickle calls will help people recognize signs that can assist antibiotics, supplements, blood transfusion, pain-relieving medications, and treatments etc. The manual assessment, diagnosis, and cell count are time consuming process and may result in misclassification and count since millions of red blood cells are in one spell. When utilizing data mining techniques such as the multilayer perceptron classifier algorithm, sickle cells can be effectively detected with high precision in the human body. The proposed approach tackles the limitations of manual research by implementing a powerful and efficient MLP (Multi-Layer Perceptron) classification algorithm that distinguishes Sickle Cell Anemia (SCA) into three classes: Normal (N), Sickle Cells(S) and Thalassemia (T) in red blood cells. This paper also presents the precision degree of the MLP classifier algorithm with other popular mining and machine learning algorithms on the dataset obtained from the Thalassemia and Sickle Cell Society (TSCS) located in Rajendra Nagar, Hyderabad, Telangana, India. Doi: 10.28991/esj-2021-01270 Full Text: PD

Power Scheduling Scheme for a Charging Facility Considering the Satisfaction of Electric Vehicle Customers

Electric vehicles (EVs) are rapidly becoming a popular choice for transportation due to their low emissions and high fuel efficiency. However, one of the major challenges in EV adoption is the lack of charging infrastructure and the potential for grid overload during peak demand. To address these challenges, we propose a power scheduling scheme for a charging facility that optimizes power utilization and enhances the user experience of the EV drivers. The scheme considers the satisfaction of vehicle users by balancing the charging demands of the vehicles with the power supply capabilities of the facility. Our results demonstrate that the proposed scheme effectively reduces charging time and enhances the accessibility of charging stations, thereby improving the user experience, and encouraging EV adoption. The scheme also optimizes power utilization and reduces peak demand on the grid, thus contributing to the overall sustainability of the transportation system

Evolutionary Models in Software Engineering

Software development life cycle models play a vital role in developing a software application. This research deals with such advanced models which are the evolutionary models namely: incremental model, and spiral model. Both these models have their own advantages and disadvantages as well. The main objective of this research paper is to represent the two evolutionary modelsrsquo features and limitations

Lung morphology: a cadaver study in Indian population

Knowledge of anatomical variations of lung is required by clinicians for accurate interpretation on different imaging techniques. During routine dissection in the anatomy department, a single lung extending uniformly throughout the thoracic cavity was detected in a 35 year old male cadaver. Thereafter a cadaver study was undertaken to report the prevalence of variations involving number, lobes and fissures of lung in Indian population. The thoracic cavities of 29 properly embalmed cadavers containing lungs were dissected and morphological features like number, fissures and lobes were observed for the presence of anatomical variations. The aforementioned single lung cadaver had associated dextrocardia. One accessory lobe on the inferior aspect was observed in 27.2% of lungs studied, whereas supernumerary fissures which were most common in right lower lobe were detected in 35% of lung specimens. The transverse fissure on the right lung was absent in 7.1% and incomplete in 50% of lung specimens. In the right lung, the oblique fissure was absent in 7.1% and incomplete in 39.3% of specimens. The oblique fissure was absent in 10.7% and incomplete in 35.7% of left lungs. Comparative analysis of our work with previous data in the literature suggest that different studies performed on radiological images reported greater prevalence of incomplete or absent pulmonary fissures as compared to various cadaver studies. Our aforementioned findings regarding the variations seen in fissures and lobes of both lungs were different from previous studies. Variations of lung anatomy are important for both the diagnosis and treatment of various diseases involving all the domains of medicine

Structure of human frizzled5 by fiducial-assisted cryo-em supports a heterodimeric mechanism of canonical wnt signaling

Frizzleds (Fzd) are the primary receptors for Wnt morphogens, which are essential regulators of stem cell biology, yet the structural basis of Wnt signaling through Fzd remains poorly understood. Here we report the structure of an unliganded human Fzd5 determined by single-particle cryo-EM at 3.7 Å resolution, with the aid of an antibody chaperone acting as a fiducial marker. We also analyzed the topology of low-resolution XWnt8/Fzd5 complex particles, which revealed extreme flexibility between the Wnt/Fzd-CRD and the Fzd-TM regions. Analysis of Wnt/b-catenin signaling in response to Wnt3a versus a ‘surrogate agonist’ that cross-links Fzd to LRP6, revealed identical structure-activity relationships. Thus, canonical Wnt/b-catenin signaling appears to be principally reliant on ligand-induced Fzd/LRP6 heterodimerization, versus the allosteric mechanisms seen in structurally analogous class A G protein-coupled receptors, and Smoothened. These findings deepen our mechanistic understanding of Wnt signal transduction, and have implications for harnessing Wnt agonism in regenerative medicine

A Framework of New Hybrid Features for Intelligent Detection of Zero Hour Phishing Websites

Existing machine learning based approaches for detecting zero hour phishing websites have moderate accuracy and false alarm rates and rely heavily on limited types of features. Phishers are constantly learning their features and use sophisticated tools to adopt the features in phishing websites to evade detections. Therefore, there is a need for continuous discovery of new, robust and more diverse types of prediction features to improve resilience against detection evasions. This paper proposes a framework for predicting zero hour phishing websites by introducing new hybrid features with high prediction performances. Prediction performance of the features was investigated using eight machine learning algorithms in which Random Forest algorithm performed the best with accuracy and false negative rates of 98.45% and 0.73% respectively. It was found that domain registration information and webpage reputation types of features were strong predictors when compared to other feature types. On individual features, webpage reputation features were highly ranked in terms of feature importance weights. The prediction runtime per webpage measured at 7.63s suggest that our approach has a potential for real time applications. Our framework is able to detect phishing websites hosted in either compromised or dedicated phishing domains

Exploration of Anti-HIV Phytocompounds against SARS-CoV-2 Main Protease: Structure-Based Screening, Molecular Simulation, ADME Analysis and Conceptual DFT Studies

The ever-expanding pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has gained attention as COVID-19 and caused an emergency in public health to an unmatched level to date. However, the treatments used are the only options; currently, no effective and licensed medications are available to combat disease transmission, necessitating further research. In the present study, an in silico-based virtual screening of anti-HIV bioactive compounds from medicinal plants was carried out through molecular docking against the main protease (Mpro) (PDB: 6LU7) of SARS-CoV-2, which is a key enzyme responsible for virus replication. A total of 16 anti-HIV compounds were found to have a binding affinity greater than −8.9 kcal/mol out of 150 compounds screened. Pseudohypericin had a high affinity with the energy of −10.2 kcal/mol, demonstrating amino acid residual interactions with LEU141, GLU166, ARG188, and GLN192, followed by Hypericin (−10.1 kcal/mol). Moreover, the ADME (Absorption, Distribution, Metabolism and Excretion) analysis of Pseudohypericin and Hypericin recorded a low bioavailability (BA) score of 0.17 and violated Lipinski’s rule of drug-likeness. The docking and molecular simulations indicated that the quinone compound, Pseudohypericin, could be tested in vitro and in vivo as potent molecules against COVID-19 disease prior to clinical trials.This was also supported by the theoretical and computational studies conducted. The global and local descriptors, which are the underpinnings of Conceptual Density FunctionalTheory (CDFT) have beenpredicted through successful model chemistry, hoping that they could be of help in the comprehension of the chemical reactivity properties of the molecular systems considered in this study.Fil: Murali, Mahadevamurthy. University Of Mysore; IndiaFil: Gowtham, Hittanahallikoppal Gajendramurthy. Nrupathunga University; IndiaFil: Shilpa, Natarajamurthy. University Of Mysore; IndiaFil: Krishnappa, Hemanth Kumar Naguvanahalli. University Of Mysore; IndiaFil: Ledesma, Ana Estela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet Noa Sur. Centro de Investigación en Biofísica Aplicada y Alimentos. - Universidad Nacional de Santiago del Estero. Centro de Investigación en Biofísica Aplicada y Alimentos; ArgentinaFil: Jain, Anisha S.. University Of Mysore; IndiaFil: Shati, Ali A.. King Khalid University; Arabia SauditaFil: Alfaifi, Mohammad Y.. Vacsera Holding Company; EgiptoFil: Elbehairi, Serag Eldin I.. Jss Academy Of Higher Education And Research; IndiaFil: Achar, Raghu Ram. Pirogov Russian National Research Medical University; RusiaFil: Silina, Ekaterina. Universitat de Les Illesbalears; EspañaFil: Stupin, Victor. Centro de Investigaciónen Materiales Avanzados; MéxicoFil: Ortega Castro, Joaquín. Jss Academy Of Higher Education And Research; IndiaFil: Frau, Juan. Universitat de Les Illesbalears; EspañaFil: Flores Holguín, Norma. Centro de Investigaciónen Materiales Avanzados; MéxicoFil: Amruthesh, Kestur Nagaraj. University Of Mysore; IndiaFil: Shivamallu, Chandan. Jss Academy Of Higher Education And Research; IndiaFil: Kollur, Shiva Prasad. University Of Mysore; IndiaFil: Glossman Mitnik, Daniel. Centro de Investigaciónen Materiales Avanzados; Méxic

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

Abstract Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.http://deepblue.lib.umich.edu/bitstream/2027.42/112972/1/13104_2010_Article_700.pd