Tuberculosis is one of the deadliest public health threats today, but there remains a lack of effective diagnostic tools. Traditionally, tuberculosis is mostly being diagnosed by a combination of chest X-rays, the staining of sputum with special dyes followed by microscopy, the growth of Mycobacterium tuberculosis in culture and the Mantoux test. These methods have problems of sensitivity, specificity and/or speed. In particular, the sputum smear microscopy test does not work well in HIV-positive patients and children and relies on the expertise of the microscopist. Drug susceptibility could only be diagnosed from the growth of Mycobacterium tuberculosis in culture which can take as long as six weeks and fails in about 50% of the cases. The determination of drug susceptibility is particularly relevant because Mycobacterium tuberculosis becomes increasingly resistant to two of the major anti-tuberculosis drugs, isoniazide and rifampicin. This form of tuberculosis is called multi-drug-resistant tuberculosis (MDR-TB) and needs to be treated with different antibiotics. It is therefore relevant to detect cases of MDR-TB because these patients remain a source of infection even if treated in the standard manner.

The recently developed Xpert MTB/RIF test  is an automated molecular-beacons-based approach to diagnosing M. tuberculosis and rifampin resistance Molecular beacons are hybridization probes that, when attached to their target, emit fluorescence. The test has been shown to have high sensitivity and specificity for detection of M. tuberculosis and associated rifampin resistance in high-incidence settings, and plans are in place to pursue FDA approval for use of the test in the United States. Capable of providing results in less than 2 hours, it may also reliably diagnose extrapulmonary tuberculosis. Due to the automated, rapid, and sensitive nature of the test, Xpert has been endorsed by WHO and is to be rolled out as part of national plans for tuberculosis and MDR tuberculosis care and control . A recent implementation of Xpert in South Africa highlighted the need for clinical pathways and algorithms for the optimal integration of the test into tuberculosis programs. For example, management of HIV-infected persons with suspected tuberculosis who test negative, among other clinical scenarios, warrants the study and institution of such algorithms.