Evolution of breast cancer therapeutics: Breast tumour kinase’s role in breast cancer and hope for breast tumour kinase targeted therapy

This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). © 2014 Baishideng Publishing Group Inc.There have been significant improvements in the detection and treatment of breast cancer in recent decades. However, there is still a need to develop more effective therapeutic techniques that are patient specific with reduced toxicity leading to further increases in patients’ overall survival; the ongoing progress in understanding recurrence, resistant and spread also needs to be maintained. Better understanding of breast cancer pathology, molecular biology and progression as well as identification of some of the underlying factors involved in breast cancer tumourgenesis and metastasis has led to the identification of novel therapeutic targets. Over a number of years interest has risen in breast tumour kinase (Brk) also known as protein tyrosine kinase 6; the research field has grown and Brk has been described as a desirable therapeutic target in relation to tyrosine kinase inhibition as well as disruption of its kinase independent activity. This review will outline the current “state of play” with respect to targeted therapy for breast cancer, as well as discussing Brk’s role in the processes underlying tumour development and metastasis and its potential as a therapeutic target in breast cancer

Addressing apoptosis to tumor zip codes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112004/1/cncr29346.pd

Molecular Characterization of Glucose-6-Phosphate Dehydrogenase Deficiency in Abu Dhabi District, United Arab Emirates

Purpose: To investigate the frequency of glucose-6-phosphate dehydrogenase (G6PD) variants and their associated enzyme deficiencies among different age groups of individuals in Abu Dhabi, United Arab Emirates (UAE).Methods: A total of 15,995 patients (6302 UAE nationals and 9693 non-UAE nationals) who presented at Mafraq Hospital, Abu Dhabi, UAE between January 2006 and January 2009 were screened for G6PD deficiency using fluorescent spot test. Molecular analysis including polymerase chain reaction– restriction fragment length polymorphism (PCR-RFLP), denaturing high performance liquid chromatography (DHPLC) and DNA sequencing were utilized to identify common mutations in individuals with G6PD deficiency.Results: The prevalence of G6PD deficiency among UAE nationals was 7.4% and non-UAE nationals 3.8%. UAE males showed prevalence of 11.6% while for UAE females it was 3.6%. The prevalence of G6PD deficiency among non-UAE nationals was 5 and 1.7% for males and females, respectively. The Mediterranean mutation, 563C→T, was predominant in non-UAE nationals.Conclusion: G6PD Mediterranean mutation is the most prevalent mutation underlying G6PD deficiency followed by Aures mutations in both UAE nationals and non-UAE nationals. On the other hand, Africa Awas found to be more in non-UAE compared with UAE nationals.Keywords: Glucose-6-Phosphate Dehydrogenase deficiency, Mutation, Abu Dhabi, Polymerase chain reaction–restriction, Fragment length polymorphis

Deep learning based single image super-resolution : a survey

Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing “the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research

Machine Learning based Energy Management Model for Smart Grid and Renewable Energy Districts

The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model

4-Hydroxy­phenyl 4-fluoro­benzoate

In the title compound, C13H9FO3, the dihedral angle between the two benzene rings is 59.86 (4)°. In the crystal, inter­molecular O—H⋯H hydrogen bonds lead to molecular chains propagating in [010]

Prevalence and demographics of methicillin resistant Staphylococcus aureus in culturable skin and soft tissue infections in an urban emergency department

<p>Abstract</p> <p>Background</p> <p>The rising incidence of methicillin resistant <it>Staph. aureus </it>(MRSA) infections is a concern for emergency practitioners. While studies have examined MRSA in inpatients, few have focused on emergency department populations. We sought to describe predictors of MRSA skin infections in an emergency department population.</p> <p>Methods</p> <p>This was a prospective observational cohort study conducted over three months in 2005. A convenience sample of patients with culturable skin infections presenting to a busy, urban emergency department was enrolled. Demographic and risk factor information was collected by structured interview. The predictive value of each risk factor for MRSA, as identified by culture, was tested using univariable logistic regression, and a multivariable predictive model was developed.</p> <p>Results</p> <p>Patients were 43% black, 40% female and mean age was 39 years (SD 14 years). Of the 182 patients with cultures, prevalence of MRSA was 58% (95%CI 50% to 65%). Significant predictors of MRSA were youth, lower body mass index, sexual contact in the past month, presence of an abscess cavity, spontaneous infection, and incarceration. The multivariable model had a C-statistic of 0.73 (95%CI 0.67 to 0.79) with four significant variables: age, group living, abscess cavity, and sexual contact within one month.</p> <p>Conclusion</p> <p>In this population of emergency department patients, MRSA skin infection was related to youth, recent sexual contact, presence of abscess, low body mass index, spontaneity of infection, incarceration or contact with an inmate, and group home living.</p

A multi-targeted approach to suppress tumor-promoting inflammation

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes