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Tuberculosis (TB) remains a major international health problem. Rapid differentiation of  Mycobacterium tuberculosis  complex (MTB) from non-tuberculous mycobacteria (NTM) is critical for decisions regarding patient management and choice of therapeutic regimen. Recently we developed a 20-compound model to distinguish between MTB and NTM. It is based on thermally assisted hydrolysis and methylation gas chromatography-mass spectrometry and partial least square discriminant analysis. Here we report the validation of this model with two independent sample sets, one consisting of 39 MTB and 17 NTM isolates from the Netherlands, the other comprising 103 isolates (91 MTB and 12 NTM) from Stellenbosch, Cape Town, South Africa. All the MTB strains in the 56 Dutch samples were correctly identified and the model had a sensitivity of 100% and a specificity of 94%. For the South African samples the model had a sensitivity of 88% and specificity of 100%. Based on our model, we have developed a new decision-tree that allows the differentiation of MTB from NTM with 100% accuracy. Encouraged by these findings we will proceed with the development of a simple, rapid, affordable, high-throughput test to identify MTB directly in sputum.

plos one

Validation of Biomarkers for Distinguishing Mycobacterium tuberculosis from Non-Tuberculous Mycobacteria Using Gas Chromatography−Mass Spectrometry and Chemometrics