Multi-Scale Blood Vessel Detection and Segmentation in Breast MRIs

An algorithm is proposed to perform segmentation of blood vessels in 3D breast MRIs. The blood vessels play an essential role as an additional tool to detect tumors. Radiologists use a maximum-intensity projection for the exposure of vasculature. The breast is a challenging organ in detecting vascular structures, because of noise bias and presence of fat tissues. There are several existing algorithms for the detection of blood vessels in MRI images, but these usually prove insufficient when it comes to the breast.

Our algorithm provides a three-dimensional model of the blood vessels by utilizing texture enhancement followed by Hessian-based methods. In addition to this, we tackled blood vessel completion by employing center line tracking, where the seeds are the end points of detached blood vessels found through skeletonizing. The results were compared to the manually segmented golden models defined by radiologists in 24 different patients, which yielded an 86% Sensitivity to the ground truth and 88.3% specificity. It appears that with the application of mass detection as the last step, our algorithm provides a helpful tool for tumour enhancement and automated detection of breast cancer.

Computational Drug Design and Molecular Dynamic Studies-A Review

Drug designing and molecular dynamic studies were an intense, lengthy and an interdisciplinary venture. At present, a new approach towards the use of computational chemistry and molecular modeling for in-silico drug design. Computational in-silico drug design skills are used in bioinformatics, computational biology and molecular biology.

Drug designing using in-silico methods is cost effective in research and development of drugs. Currently, a vast number of software’s used in drug design. In-silico drug designing and molecular dynamic studies can be performed by using different methods namely homology modeling, molecular dynamic studies, energy minimization, docking and QSAR etc. By using in-silico drug designing we can produce an active lead molecule from the preclinical discovery stage to late stage clinical development. The lead molecules which are developed will help us in selection of only potent leads to cure particular diseases. Therefore in-silico methods have been of great importance in target identification and in prediction of novel drugs.

Multi-Scale Blood Vessel Detection and Segmentation in Breast MRIs

An algorithm is proposed to perform segmentation of blood vessels in 3D breast MRIs. The blood vessels play an essential role as an additional tool to detect tumors. Radiologists use a maximum-intensity projection for the exposure of vasculature. The breast is a challenging organ in detecting vascular structures, because of noise bias and presence of fat tissues. There are several existing algorithms for the detection of blood vessels in MRI images, but these usually prove insufficient when it comes to the breast. Our algorithm provides a three-dimensional model of the blood vessels by utilizing texture enhancement followed by Hessian-based methods. In addition to this, we tackled blood vessel completion by employing centerline tracking, where the seeds are the endpoints of detached blood vessels found through skeletonizing.

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Decision Tree Eliminates the Computational Complexities of Big Data Processing

Hadoop is one of the reputed general purpose computing platforms used to process big data. Mapreduce is the Hadoop project’s main processing engine that provides a framework for distributed computing. This is generated from the combination of ‘Map’ and Reduce concepts in the functional programming. This functional programming model hides the entire complexities related to distributive computing nodes, so that the developer can focus on the implementation of the Map and Reduce functions.

Decision Tree is one of the most efficient ways of classification and decision making. Decision tree consist of root node and decision nodes and the data related to a training module for example can be divided based on the measurable, functional attribute. The entire data thus can be divided into number of partitions based on the attributes fixed. If the samples within a partition belong to a single class, then the algorithm gets terminated automatically. Otherwise the division process continues until it eliminates all the non-identical samples. The samples that don’t fit into the attributed partitions are labeled as unknown category.

Selenoergothionein as a Potential Inhibitor against Amyloid β-Protein (Aβ): Docking and Molecular Dynamics Studies

Alzheimer‘s disease (AD) is a progressive neurodegenerative disorder, encircling the deterioration of cognitive functions and behavioral changes, characterized by the aggregation of amyloid β-protein (Aβ) into fibrillar amyloid plaques in elected areas of the brain with the lipid-carrier protein apolipoprotein E (apoE), the microtubule associated protein tau, and the presynaptic protein α-synuclein. High levels of fibrillary Aβ, the main constituent of senile plaques, are deposited in the AD brain that outcome in the thrashing of synapses, neurons and destruction of neuronal role.

Aβ is derived from the amyloid precursor protein through sequential protein cleavage by aspartyl protease, β-secretase and presenilin-dependent β-secretase triggering a spill of events such as oxidative damage, neurotoxicity, and inflammation that contributes to the progression of AD. Therefore the Aβ protein may be a target for anti-Alzheimer drugs. Aβ protein was retrieved from the Protein data bank and energy minimized and subjected to molecular dynamic simulations using NAMD 2.9 software with CHARMM27 force field in water.

Neuroprotective Effects of Pine Bark and Aloe vera on the Locomotor Activity in Focal Cerebral Ischemia: Possible Antioxidant Mechanisms

Stroke is one of the principal causes of death and disability worldwide. Cerebral ischemia is the result of insufficient cerebral blood flow for cerebral metabolic functions. Oxidative stress and inflammation have an important role in cerebral infarction which mediated by ischemia and reperfusion. Reperfusion injury stimulates many pathological mechanisms such as leukocyte infiltration, oxidative stress, inflammation, destruction of blood-brain barrier, platelet activation, nitric oxide release, and apoptosis. Consequently, potent anti-inflammatory and antioxidant mediators may be beneficial in the treatment of cerebral ischemia and reperfusion injury. The lack of effective and widely applicable pharmacological treatments for ischemic stroke patients may explain a growing interest in the traditional medicines.

Aloe Vera and Pine Bark as Alternative Traditional Medicine to Heal Ischemia

Ischemia is a condition that happens as a result of insufficient cerebral blood flow, necessary for the brain to conduct the cerebral metabolic functions. Ischemia and reperfusion also lead to Oxidative stress and the inflammation. Reperfusion injury ignites certain pathological mechanisms, including leukocyte infiltration, destruction of blood-brain barrier, platelet activation, nitric oxide release, and apoptosis. Absence of widely applicable treatment procedures compel people to adapt to traditional or alternative medicines. Pine bark extracts that is known for rich flavonoids can act as a natural antioxidant and free radical scavenger. Aloe Vera, known for its curative and therapeutic properties for centuries possess antibacterial, antifungal and antiviral qualities can be effectively used as cure for ischemia.  Topical and oral uses of Aloe vera gel are also effective in addressing this issue.

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A Review on New Horizons of Bioinformatics in Next Generation Sequencing, Viral and Cancer Genomics

Genomics and molecular biology has always been a constant source of inspiration and motivational research for worldwide researchers in field of biology and biotechnology. These two fields have always generated a huge amount of data and in order to compile and analyze those, bioinformatics came into action during last decade. Implementation of bioinformatics has a clear intention of doing all these analysis of data in efficient and fast manner in order to cut down the expensive laboratory equipment, chemicals and most precious time.

Mostly genomic data is composed of sequencing results at a higher scale and that is why manual curating and handling of these data is quite difficult. Supreme aim of this review is to make awareness about bioinformatics options in cancer genomics and viral genomics apart from next generation sequencing. Next generation sequencing or high throughput sequencing has helped a lot to replace old conventional method of sequencing and with the help of recent advances in technologies.

DNA/RNA Fragmentation and Cytolysis in Human Cancer Cells Treated with Diphthamide Nano Particles Derivatives

Molecular structure activity studies for some Diphthamide Nano particles derivatives indicate that the conformational characteristics along with the nature and position of the substituents on the Diphthamide Nano particles derivatives ring play an important role in their biological and biochemical activities. Therefore, we have calculated the optimized molecular geometries of some Diphthamide Nano particles derivatives.

Calculations are carried out on the structures of these medical, medicinal and pharmaceutical Nano drugs using Hartree–Fock calculations and also Density Functional Theory (DFT) by performing HF, PM3, MM2, MM3, AM1, MP2, MP3, MP4, CCSD, CCSD(T), LDA, BVWN, BLYP and B3LYP levels of theory using the standard 31G, 6–31G*, 6–31+G*, 6–31G(3df, 3pd), 6–311G, 6–311G* and 6–311+G* basis sets of the Gaussian 09. The comparative heats of formation and Natural Bond Orbital (NBO) charges are calculated for these Diphthamide Nano particles derivatives. We have finally obtained some conformational rules in terms of the natures and positions of the substituents on the Diphthamide Nano particles derivatives ring.

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A Multiscale Hemorheological Computer-Based Model of Atherosclerosis: An In-Depth Investigation of Erythrocytes-Driven Flow Characteristics in Atheroma Development

The mortality caused by cardiovascular diseases is dramatically increasing. Atherosclerosis is among the main contributors to this extremely high cardiovascular disease mortality. Atherosclerosis is controlled by mechanical forces exerted by the flow of blood on the inner lining of arteries, the endothelium. In order to fight this lethal disease a realistic computational model is required that offers an accurate understanding of the effect of blood flow on the arterial wall.

In order to realistically describe complex blood flow patterns and their interaction with the arterial wall, we have developed an integrated computational technique that takes into account both the particulate cellular composition of the blood and the interactions between the particulate blood and the vessel wall, in macro-circulation. The cellular composition of the blood was modelled using a multiphase fluid dynamics method by computing both an Eulerian fluid domain for modelling blood plasma, and a Lagrangian solid domain that represented the blood cells.

Novel Drug Designing and Target Identification using Computational Bioinformatics

Both drug designing and molecular dynamic studies involve lengthy and extensive efforts that are interdisciplinary in nature. Of late, computational chemistry and molecular modeling are widely applied in the in-silico drug design. Computational in-silico drug design is widely applied in bioinformatics, computational biology and molecular biology. In-silico methods for drug designing has been proved cost effective and in the drug development. In-silico drug designing is helpful in developing active lead molecules from the preclinical discovery stage to late stage clinical development. Vast number of software is used in drug designing and in-silico methods are useful in target identification and the prediction of novel drugs.

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Shikimate Kinase of Yersinia pestis: A Sequence, Structural and Functional Analysis

Yersinia pestis, the causative organism of Plague, is widely recognized as a potential bioterrorism threat. Due to the absence of homologs in human, Shikimate Kinase (SK) is considered as an excellent drug target in several bacterial and protozoan parasites. Ample literature evidences confirm the suitability of this protein as a good target. Therefore, Shikimate Kinase of Shikimate pathway in Yersinia pestis represents an attractive drug target.

In the present study, a clustering approach was undertaken to select the proper representative for Shikimate Kinase sequences belonging to Yersinia pestis for structure determination. Three-dimensional models of the enzyme for KFB61218.1 (SK1), EFA47400.1 (SK2) and WP_016255950.1 (SK3) were generated using a comparative molecular modeling approach where structures were developed using the single specific template as well as multiple closely associated templates. The structures of Shikimate Kinase developed using comparative modeling were evaluated for stereochemical quality using various structural validation tools. Results from structural assessment tools indicated the reasonably good quality of models.

Preface, or the Truth Revealed

The truth revealed was when a young man, Nick Kostovic, made public his findings about the power of the Universe, the achieved knowledge he performs in the healing systems of our days. Doctors, patients, and common people thought of him as a magician who penetrated from the clouds to announce a new truth. And this is what it was-a new truth. The community of coastal Los Angeles became stunned when terminally sick people began to feel better, when invalids began to walk, when memories returned to Parkinson’s and Alzheimer’s-diseased patients. For the past thirty years, Kostovic has conducted research on the effects of neurological disorders on human beings and spent the last fifteen years working on several projects in connection with bioenergetic physical therapy.

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The Neural Networks with an Incremental Learning Algorithm Approach for Mass Classification in Breast Cancer

Breast cancer is a leading fatality cancer for woman. According to epidemiological data, breast cancer accounts for 20-25% of female malignant tumor, with is expected to increase. These facts have driven us to select this deadly cancer as our domain. Breast cancer has four early signs; micro-calcification, mass, architectural distortion and breast asymmetries. However, only data regarding mass will be used as a pilot project to test our system later on. Masses of 2 cm in diameter are palpable with regular breast self-examination while mammogram images can capture it from 5 mm in diameter.

However, these images were to be determined by an expert radiologist who is familiar with breast cancer. Generally, there are 2 types of breast cancer which are in situ and invasive. In situ starts in the milk duct and does not spread to other organs even if it grows. Invasive breast cancer on the contrary, is very aggressive and spreads to other nearby organs and destroys them as well. It is very important to detect the cancerous cell before it spreads to other organs, thus the survival rate for patient will increase to more than 97%. However, time taken from taking mammogram images to biopsy dangerous result varies between 2weeks to a month in average.

A Multi-Layer Non-Newtonian Model of Cardiovascular Inflammation

Cellular functions related to the maintenance of homoeostasis are regulated by shear forces sensed by endothelial cells. The endothelial cells sense local changes in shear stress. The resulting signals are either transduced into chemical responses or transmitted to the surroundings to regulate the cellular activity.

In the current literature, models of blood flow applied to the characterization of atherosclerotic plaques consider blood as a Newtonian fluid because of the characteristic length of the domain. At predilection sites for plaque deposition, the diameter of the blood particles is much smaller than the normal arterial diameter. However, under disease condition, the proportions can dramatically change due to a reduction greater than 80% in the arterial cross-section, in cases of severe stenosis. Here we show that in diseased arteries, the local particle concentration can peak at locations associated to high inflammation.

The Neural Networks with an Incremental Learning Algorithm Approach for Mass Classification in Breast Cancer

As breast cancer can be very aggressive, only early detection can prevent mortality. The proposed system is to eliminate the unnecessary waiting time as well as reducing human and technical errors in diagnosing breast cancer. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain.

This paper uses the neural networks with an incremental learning algorithm as a tool to classify a mass in the breast (benign and malignant) using selection of the most relevant risk factors and decision making of the breast cancer diagnosis To test the proposed algorithm we used the Wisconsin Breast Cancer Database (WBCD). ANN with an incremental learning algorithm performance is tested using classification accuracy, sensitivity and specificity analysis, and confusion matrix. The obtained classification accuracy of 99.95%, a very promising result compared with previous algorithms already applied and recent classification techniques applied to the same database.

The Standing Acoustic Wave Principle within the Frequency Analysis of Acoustic Signals in the Cochlea

The organ of hearing is responsible for the correct frequency analysis of auditory perceptions coming from the outer environment. The article deals with the principles of the analysis of auditory perceptions in the cochlea only, i.e., from the overall signal leaving the oval window to its decomposition realized by the basilar membrane. The paper presents two different methods with the function of the cochlea considered as a frequency analyzer of perceived acoustic signals.

First, there is an analysis of the principle that cochlear function involves acoustic waves travelling along the basilar membrane; this concept is one that prevails in the contemporary specialist literature. Then, a new principle with the working name “the principle of standing acoustic waves in the common cavity of the scala vestibuli and scala tympani” is presented and defined in depth. According to this principle, individual structural modes of the basilar membrane are excited by continuous standing waves of acoustic pressure in the scale tympani.

Meta-Analysis of Genomic Data: Between Strengths, Weaknesses and New Perspective

The rapid advances in high-throughput technologies, such as microarrays have revolutionizing the knowledge and understanding of biological systems and genetic signatures of human diseases. This has led to the generation and accumulation of a large amount of genomic data that need to be adequately integrated to obtain more reliable and valid results than those from individual experiments. Meta-analysis of microarray data is one of the most common statistical techniques used for combining multiple data sets. Despite its remarkable successes in discovering molecular subtypes, underlying pathways and biomarkers for the pathological process of interest, this method possesses several limitations.

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Dose Optimization Studies by Selecting Kilovoltage in Oncologic Chest CT

In this work we have studied the adequacy of dose levels of irradiation in oncologic chest CT obtained in our daily practice. The secondary objective was to evaluate the effect on radiation dose of individual adjustment of kilovoltage in thoracic multidetector row computed tomography (MDCT) images acquired with both single and dual-source technology. The impact of lowering the kilovoltage in the diagnostic quality of these studies was also evaluated. 161 patients were included in the study. CT examinations were performed using two different equipments: a conventional CT scanner and a dual-source computed tomography. The average values of dose length product (DLP) obtained in our daily practice meet the recommendations of the existing referral guidelines. Lower values can be achieved through individual adjustment of kilovoltage and with dual-source CT technology, maintaining the diagnostic quality of these studies.

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A Bayesian Analysis of Copy Number Variations in Array Comparative Genomic Hybridization Data

Array Comparative Genomic Hybridization (CGH) has been widely used for detecting genomic copy number variations (CNVs). The central goal of array CGH data analysis is to accurately detect homogeneous regions of log intensity ratios which represent relative changes in DNA copy number. Various methods have been proposed in recent years. Most methods, however, do not consider correlations of neighboring probe measurements, and are usually designed for analysis at single sample level rather than detecting common or recurrent CNVs among multiple samples. We propose a Bayesian segment-based approach for efficient analysis of array CGH data. The proposed method is based on simple assumptions but is general enough to accommodate various spatial correlations among probe measurements. It also allows for multiple samples with recurrent CNVs, therefore is able to borrow strength across samples.

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