New Drug-resistant TB Strains Could Become Widespread, Says New Study


http://www.sciencedaily.com/releases/2009/08/090810161957.htm

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Development of Alzheimer’s trademark cell-killing plaques slowed by researchers


Researchers have learned how to fix a cellular structure called the Golgi that mysteriously becomes fragmented in all Alzheimer’s patients and appears to be a major cause of the disease. They say that understanding this mechanism helps decode amyloid plaque formation in the brains of Alzheimer’s patients — plaques that kills cells and contributes to memory loss and other Alzheimer’s symptoms.

Commonly used pain relievers have added benefit of fighting bacterial infection


Some commonly used drugs that combat aches and pains, fever, and inflammation are also thought to have the ability to kill bacteria. New research reveals that these drugs, better known as nonsteroidal anti-inflammatory drugs, act on bacteria in a way that is fundamentally different from current antibiotics. The discovery could open up new strategies for fighting drug-resistant infections and ‘superbugs.’

 

Building new drugs just got easier


A method for modifying organic molecules has been developed that significantly expands the possibilities for developing new pharmaceuticals and improving old ones. The innovation makes it easier to modify existing organic compounds by attaching biologically active “functional group” to drug molecules. A typical small-molecule drug derives its activity from such functional groups, which are bound to a relatively simple backbone structure consisting chiefly of carbon atoms.
 
Journal Reference:

  1. Ri-Yuan Tang, Gang Li, Jin-Quan Yu. Conformation-induced remote meta-C–H activation of aminesNature, 2014; 507 (7491): 215 DOI: 10.1038/nature12963

Microbes help to battle infection: Gut microbes help develop immune cells, study finds


Beneficial gut bacteria are necessary for the development of innate immune cells — specialized types of white blood cells that serve as the body’s first line of defense against invading pathogens — new research has found. The research suggests that a healthy population of gut microbes can actually provide a preventative alternative to antibiotics.

An artist’s representation of gut microbes promoting hematopoiesis.

 

Journal Reference:

  1. Arya Khosravi, Alberto Yáñez, Jeremy G. Price, Andrew Chow, Miriam Merad, Helen S. Goodridge, Sarkis K. Mazmanian. Gut Microbiota Promote Hematopoiesis to Control Bacterial InfectionCell Host & Microbe, March 2014 DOI: 10.1016/j.chom.2014.02.006

Project Proposal


VALIDATION OF A SAR MODEL OF DATASET-1696 BY APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES SUCH AS KNOCKING OUT OF THE FEATURE SELECTION.

 

  • INTRODUCTION

Tuberculosis is a infectious disease caused by Mycobacterium tuberculosis (Mtb), affecting more than two billion people around the globe and is one of the major causes of death and mortalityin the developing world. Recent reports suggest that TB has been developing resistance to the widely used anti-tubercular drugs, resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. But the research of molecules against such furious disease remains neglected among the research community. Hence OSDD is focusing such a neglected disease, I myself involving in such contribution and presenting my knowledge.

  • IDEA

Artificial intelligence in Drug Designing include the generation of manual descriptor frame model structure on the basis of which a model is developed. On basis of which we can be able to predict the activity of new molecules. The machine learning methods to generate involves the use of all the descriptors for predicting a model. This work is an attempt of finding out the importance of each descriptor by knocking out. So and observing the effect of knocking out that descriptor on the model in this way. We can find out the important descriptors for predicting activity which may be useful in designing new compound.

  • METHODOLOGY

Methodology begins with the experimented dataset search followed by generating models using machine learning method such as WEKA, followed by validating the model to be accurate and examining the essential molecular descriptors manually by eliminating one by one in order to expose the vital descriptors.

  • WORKFLOW

work flow