Developmental regulation of mitochondrial apoptosis by c-Myc governs age- and tissue-specific sensitivity to cancer therapeutics

It is not understood why healthy tissues can exhibit varying levels of sensitivity to the same toxic stimuli. Using BH3 profiling, we find that mitochondria of many adult somatic tissues, including brain, heart, and kidneys, are profoundly refractory to pro-apoptotic signaling, leading to cellular resistance to cytotoxic chemotherapies and ionizing radiation. In contrast, mitochondria from these tissues in young mice and humans are primed for apoptosis, predisposing them to undergo cell death in response to genotoxic damage. While expression of the apoptotic protein machinery is nearly absent by adulthood, in young tissues its expression is driven by c-Myc, linking developmental growth to cell death. These differences may explain why pediatric cancer patients have a higher risk of developing treatment-associated toxicities

Li3MxV22x(PO4)3/C (M=Fe, Co) composite cathodes with extended solubility limit and improved electrochemical behavior

A first attempt has been made to prepare Li3MxV22x(PO4)3/C (M=Fe, Co) composite solutions by adopting a novel oxalic dihyrazide assisted combustion (ODHAC) method. The pillaring effect of Fe in Li3FexV22x(PO4)3/C and the possible electrochemical activity of the Co3+/4+ redox couple of Li3CoxV22x(PO4)3/C at a 4.8 V limit increases the structural and cycling stability of the native Li3V2(PO4)3/C cathode respectively, thereby ultimately improving the electrochemical behaviour of Li3MxV22x(PO4)3/C solid solutions. An extended solubility limit of x = 0.10 for Fe dopant has been achieved for the first time through the present study against the reported value of x = 0.05 in Li3FexV22x(PO4)3/C compounds. The study demonstrates the suitability of the ODHAC synthesis approach in preparing a wide variety of phase pure Li3MxV22x(PO4)3/C cathodes. Further, the superiority of Li3Co0.10V1.90(PO4)3/C in exhibiting the highest capacity (178 mAh g21) and negligible fade (4%) and the demonstrated cyclability under the influence of 10 C rate has been understood as a function of the synergistic effect of the ODHAC synthesis method and the optimum concentration of Co dopant chosen for the stud

INFLUENCE OF DEMOGRAPHIC FACTORS ON THE CONSUMERS' AWARENESS OF FMCG PRODUCTS A CASE OF CONSUMERS IN KRISHNAGIRI DISTRICT OF TAMILNADU

Fast moving consumers goods (FMCG) market is highly competitive due to innumerous players in the field. It is true to all kind of FMCG products. Yet only few brands are firmly foot in the mind of customers. If a company suceed in making aware of a product brand, it ensures the major success of its marketing campaign. The awareness about a particular brand exixtence is caused by many factors. The existing customers as unpaid ambassodors, the reach of marketing communications and the receptive nature of the consumers are the factors generally influence the awareness level of the customers. The demographic factors such as age, education and gender predominantly playing a vital role in receiving portion of any communication due to communicational influesors like selective perception etc. Hence the study focuses on the influence of demographic factors on the Consumers' Awareness of FMCG products. The study is very significant due to lack of studies that identifie the influence of demaographic factors on the consumers’ awareness. To achieve the stated objectivea a self administered questionnairre was designed and developed to measure the consumers’ awareness level of the FMCG customers. There are 150 samples of respondents; resposes were collected from the FMCG consumers of Krishnagiri district. Convinience sampling technique was applied to collect the data. Retail stores in the city and nearby villages were selected to collect the repsonses from the respondents who are the consumers of FMCG products from the geographic study area Krishnagiri District of Tamilnadu state. The collected data were properly edited coded and computed in excel sheet. Statistical tests such as mean, standard devaition, ANOVA were exexcuted using Ms-office excel data analysis tool. The results have indicated clearly that the consumer awareness level is influenced by the age and income level of the customers

Combustion synthesized nanocrystalline Li3V2(PO4)3/C cathode for lithium-ion batteries

Nanocrystalline Li3V2(PO4)3/C composite synthesized using a novel corn assisted combustion method at 850 8C exhibits superior physical and electrochemical properties than the one synthesized at 800 8C. Despite the charge disproportionation of V4+ and a possible solid solution behavior of Li3V2(PO4)3 cathode upon insertion and extraction of Li+ ions, the structural stability of the same is appreciable, even with the extraction of third lithium at 4.6 V. An appreciable specific capacity of 174 mAh g�1 and better capacity retention upon high rate applications have been exhibited by Li3V2(PO4)3/C cathode, thus demonstrating the suitability of the same for lithium-ion battery application

MYC regulates ductal-neuroendocrine lineage plasticity in pancreatic ductal adenocarcinoma associated with poor outcome and chemoresistance

Neuroendocrine differentiation of epithelial tumor cells can contribute to cancer cell resistance and survival. Here, the authors show that dysregulated c-Myc promotes neuroendocrine differentiation in pancreatic ductal adenocarcinoma, leading to poor survival and chemoresistance

Accuracy Improvement for Diabetes Disease Classification: A Case on a Public Medical Dataset

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. Providing diagnostic aid for diabetes disease by using a set of data that contains only medical information obtained without advanced medical equipment, can help numbers of people who want to discover the disease or the risk of disease at an early stage. This can possibly make a huge positive impact on a lot of peoples lives. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use SOM, PCA and NN for clustering, noise removal and classification tasks, respectively. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction in relation to methods developed in the previous studies. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system