AIMC Topic: Antitubercular Agents

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Clinical testing of drug treatment shortening in patients with TB using PET/CT imaging of lung lesions.

Science translational medicine
Six months of drug treatment is standard of care for drug-sensitive pulmonary tuberculosis (TB). Understanding the factors determining the length of treatment required for durable cure would allow individualization of treatment durations. We conducte...

Mycobacterium tuberculosis FAS-II pathway targeted integrative deep learning based identification of potential anti-tubercular agents.

Journal of computer-aided molecular design
Mycobacterium tuberculosis (Mtb) continues to be one of the major contributors to the global burden of infectious diseases. Many drugs used in the current treatment regime have fallen prey to the puzzling phenomenon of antimicrobial resistance. Despi...

Diagnostic assistance method for RR-TB/MDR-TB patients under treatment based on CNN-LSTM.

Scientific reports
The rapid development of deep learning has promoted its application in disease diagnosis, treatment, and prognosis prediction. Medical imaging plays a crucial role in the management of rifampicin-resistant tuberculosis/multidrug-resistant tuberculosi...

Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa.

Nature communications
Africa's vast genetic diversity poses challenges for optimising drug treatments in the continent, which is exacerbated by the fact that drug discovery and development efforts have historically been performed outside Africa. This has led to suboptimal...

Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models.

Journal of medical Internet research
BACKGROUND: Early prediction of treatment outcomes for patients with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) undergoing extended therapy is crucial for enhancing clinical prognoses and preventing the transmission of this ...

Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations.

BMC medical informatics and decision making
BACKGROUND: Understanding early predictors of treatment outcomes allows better outcome prediction and resource allocation for efficient tuberculosis (TB) management.

Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherence.

Scientific reports
Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005-2024), we appl...

Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.

BMC genomic data
BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine lear...

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...

Tuberculosis of the central nervous system: current concepts in diagnosis and treatment.

Current opinion in neurology
PURPOSE OF REVIEW: The outcome of central nervous system (CNS) tuberculosis has shown little improvement over several decades, with diagnosis remaining unconfirmed in nearly half of the cases. This review highlights current insights and advancements ...