AIMC Topic: Mycobacterium tuberculosis

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sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms.

BMC genomics
BACKGROUND: Bacterial small regulatory RNA (sRNA) plays a crucial role in cell metabolism and could be used as a new potential drug target in the treatment of pathogen-induced disease. However, experimental methods for identifying sRNAs still require...

Molecular Indicator for Distinguishing Multi-drug-Resistant Tuberculosis from Drug Sensitivity Tuberculosis and Potential Medications for Treatment.

Molecular biotechnology
The issue of multi-drug-resistant tuberculosis (MDR-TB) presents a substantial challenge to global public health. Regrettably, the diagnosis of drug-resistant tuberculosis (DR-TB) frequently necessitates an extended period or more extensive laborator...

Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis.

PloS one
Despite advancements in detection and treatment, tuberculosis (TB), an infectious illness caused by the Mycobacterium TB bacteria, continues to pose a serious threat to world health. The TB diagnosis phase includes a patient's medical history, physic...

Lite-YOLOv8: a more lightweight algorithm for Tubercle Bacilli detection.

Medical & biological engineering & computing
Deep learning is a transformative force in the medical field and it has made significant progress as a pivotal alternative to conventional manual testing methods. Detection of Tubercle Bacilli in sputum samples is faced with the problems of complex b...

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...

Identification of mycobacterial Thymidylate kinase inhibitors: a comprehensive pharmacophore, machine learning, molecular docking, and molecular dynamics simulation studies.

Molecular diversity
Thymidylate kinase (TMK) is a pivotal enzyme in Mycobacterium tuberculosis (Mtb), crucial for phosphorylating thymidine monophosphate (dTMP) to thymidine diphosphate (dTDP), thereby playing a critical role in DNA biosynthesis. Dysregulation or inhibi...

Prediction of Mycobacterium tuberculosis cell wall permeability using machine learning methods.

Molecular diversity
Tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis (M. tb), continues to pose a significant worldwide health threat. The advent of drug-resistant strains of the disease highlights the critical need for novel treatments. The unique ce...

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...

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...

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...