Tuberculosis (TB) remains the deadliest infectious disease globally, with one in five patients facing drug-resistant strains. A recent study from Tufts University reveals a groundbreaking AI tool, DECIPHAER, designed to decode how TB treatments kill bacteria. Effective TB treatment is critical, requiring new drug combinations that can overcome existing resistance. Bree Aldridge, the study’s senior author, emphasizes the need for a more effective multidrug regimen, highlighting traditional treatment limitations. DECIPHAER enhances understanding of drug mechanisms by combining morphological profiling with transcriptional data, revealing precise bacterial responses to medications. This innovation allows researchers to predict drug impacts based solely on visual cues, expediting the identification of effective TB therapies. The study also indicates that DECIPHAER could extend its applicability to other infectious diseases and cancer, promising a significant advance in global health strategies against resistant infections. For more, refer to the study in Cell Systems (Johnson et al., 2025).
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