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<b>Microscopy data, analysis code, and segmentation models for Phenotypic drug susceptibility testing for </b><b><i>Mycobacterium tuberculosis </i></b><b>variant</b><b><i> bovis </i></b><b>BCG in </b><b>12-hour </b>

dataset
posted on 2025-03-25, 13:18 authored by Buu Minh TranBuu Minh Tran, Jimmy Larsson, Anastasia Grip, Praneeth KarempudiPraneeth Karempudi, Johan Elf
<p dir="ltr">The study aimed to improve the speed of drug susceptibility testing for tuberculosis (TB), particularly drug-resistant strains. Current phenotypic drug susceptibility testing (pDST) methods take at least two weeks. This study used microfluidic chips, microscopy, and deep neural network algorithms to monitor the growth of <i>Mycobacterium bovis</i> Bacillus Calmette–Guérin (BCG) and <i>Mycobacterium smegmatis</i> in the presence of antibiotics. The findings suggest that pDST for TB could potentially be completed in less than 12 hours for slow-growing mycobacteria, offering a significant improvement in diagnosing drug-resistant TB. The entry contains raw microscopy data, analysis code and output, and code to generate figures.</p>

Funding

SSF (ARC19-0016)

KAW (2023.0531)

Novo Nordisk (0083419)

History

Publisher

Uppsala University

Contact email

johan.elf@icm.uu.se

SciLifeLab acknowledgement

  • SciLifeLab Data Centre