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Tuberculosis, Multidrug-Resistant

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Treating drug-resistant tuberculosis in an era of shorter regimens: Insights from rural South Africa.

South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
BACKGROUND: Progressive interventions have recently improved programmatic outcomes in drug-resistant tuberculosis (DR-TB) care in South Africa (SA). Amidst these, a shorter regimen was introduced in 2017 with weak evidence, and has shown mixed result...

A machine-learning based model for automated recommendation of individualized treatment of rifampicin-resistant tuberculosis.

PloS one
BACKGROUND: Rifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen g...

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.

BMC genomics
The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biolog...

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.

Proceedings of the National Academy of Sciences of the United States of America
Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiot...

Machine learning investigation of tuberculosis with medicine immunity impact.

Diagnostic microbiology and infectious disease
Tuberculosis (T.B.) remains a prominent global cause of health challenges and death, exacerbated by drug-resistant strains such as multidrug-resistant tuberculosis MDR-TB and extensively drug-resistant tuberculosis XDR-TB. For an effective disease ma...

Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning.

PLoS computational biology
There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration w...

Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm.

Pathogens (Basel, Switzerland)
Drug-resistant tuberculosis (DR-TB) and HIV coinfection present a conundrum to public health globally and the achievement of the global END TB strategy in 2035. A descriptive, retrospective review of medical records of patients, who were diagnosed wi...

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.

Scientific reports
Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each year. Drug resistance (DR), including the multi-drug form (MDR-TB), is challenging control of the dis...

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BMC infectious diseases
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...

Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.

Nature communications
New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-po...