ArticleScientific reports2025
A comparative study of antibiotic resistance patterns in Mycobacterium tuberculosis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Exploring halophilic microorganisms for novel antimicrobial discovery: molecular mechanisms and computational approaches.Folia microbiologica · 2026Review
- Article
- Article
- Trends and Characteristics of Drug-Resistant Tuberculosis: A Five-Year Study at a Major Specialized Hospital in Guizhou, China (2018-2023).Infection and drug resistance · 2026Article
- AJournal of bacteriology · 2025Article
- AI-Powered Spectral Imaging for Virtual Pathology Staining.Bioengineering (Basel, Switzerland) · 2025Article
- Rapid and Efficient Screening ofDiagnostics (Basel, Switzerland) · 2025Article
- Trends in Drug Resistance and Epidemiological Patterns of Tuberculosis in Elderly Patients in Wenzhou, China (2014-2023).Infection and drug resistance · 2025Article
- Epidemiology and Molecular Drug-Resistance Patterns of Tuberculosis in Non-Elderly Patients in Luoyang, China, 2019-2023.Infection and drug resistance · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
This study leverages the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) to analyze over 27,000 Mycobacterium tuberculosis (MTB) genomic strains, providing a comprehensive and large-scale overview of antibiotic resistance (AMR) prevalence and resistance patterns. We used MTB++, which is the newest and most comprehensive AI-based MTB drug resistance profiler tool, to predict the resistance profile of each of the 27,000 MTB isolates and then used feature analysis to identify key genes that were associated with the resistance. There are three main contributions to this study. Firstly, it provides a detailed picture of the prevalence of specific AMR genes in the BV-BRC dataset as well as their biological implications, providing critical insight into MTB's resistance mechanisms that can help identify genes of high priority for further investigation. The second aspect of this study is to compare the prevalence of antibiotic resistance across previous studies that have addressed both the temporal and geographical evolution of MTB drug resistance. Lastly, this study emphasizes the need for targeted diagnostics and personalized treatment plans. In addition to these contributions, the study acknowledges the limitations of computational prediction and recommends future experimental validation.
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