ANN ARBOR—With a shortage of new tuberculosis drugs in the pipeline, a software tool from the University of Michigan can predict how current drugs—including unlikely candidates—can be combined in new ways to create more effective treatments.

How a recently identified defensive compound called itaconate tricks the bacteria behind tuberculosis.

ANN ARBOR—New diagnostic tools such as machine learning and precision medicine could help identify tuberculosis patients with the highest risk of reactivation of the disease, according to a new University of Michigan study.

An in-depth look at the work of T-cells, the body's bacteria killers, could provide a roadmap to effective drug treatments.

A computer model of tuberculosis has shown that approved treatments prescribing antibiotic doses once or twice a week are more likely to lead to drug resistant strains than are daily antibiotic regimens.