Multivariate statistical data processing used for TB

A new technique has been developed that will help researchers quickly and easily distinguish between various infectious Mycobacterium species, which is important for differentiating between potentially lethal and benign Mycobacterium bacteria.

Multivariate statistical data processing has been used to create a model from gas chromatography-mass spectrometry (GC-MS) data of metabolite profiles of the various types of Mycobacterium species tuberculosis (TB).

There have been multiple drug-resistant species of TB reported around the world recently, which is having a detrimental effect on the millions of sufferers. Recent data has shown there were 8.8 million new cases in 2010, and 1.45 million deaths.

Current TB detection assays can take weeks to incubate colonies, by which time a patient's infection may have progressed or been passed on to others. The test also has a 15 to 20 per cent rate of false negatives in adults tested.

A team from the Centre for Human Metabonomics, at North-West University, in Potchefstroom, South Africa has created the first true GC-MS metabolomics research approach to reduce this time to less than 16 hours. According to the team: "This study proves the capacity of a GC-MS, metabolomics pattern recognition approach for its use in TB diagnosis and disease characterisation."

Posted by Fiona Griffiths