Tuberculosis

SecProMTB: Support Vector Machine-Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis.

Secretory proteins of Mycobacterium tuberculosis have created more concern, given their dominant immunogenicity and role in pathogenesis. In view of expensive and time-consuming traditional biochemical experiments, an advanced support vector machine model named SecProMTB is constructed in this study and the proteins are identified by a bioinformatic approach. First, an improved pseudo-amino acid c...

Creating the Black Box: A Primer on Convolutional Neural Network Use in Image Interpretation.

Convolutional neural networks have been shown to demonstrate high diagnostic performance in radiologic image interpretation tasks ranging from recognition of acute stroke on computed tomography to identification of tuberculosis on plain radiographs. To a radiologist not immersed in computer science jargon, it may seem that this inscrutable black box is best treated warily, at arm's length. In this...

Morphology-based classification of mycobacteria-infected macrophages with convolutional neural network: reveal EsxA-induced morphologic changes indistinguishable by naked eyes.

EsxA is an essential virulence factor for Mycobacterium tuberculosis (Mtb) pathogenesis as well as an important biomarker for Mtb detection. In this s...

An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks.

BACKGROUND AND OBJECTIVE: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical c...

Ultrasonic exposure parameters screening in permeability of mycobacterium smegmatis cytoderm induced by cavitation based on artificial neural network identification.

The low intensity ultrasound has been adopted by researchers to enhance the bactericidal effect against bacteria in vitro and in vivo. Although the me...

Compact and Computationally Efficient Representation of Deep Neural Networks.

At the core of any inference procedure, deep neural networks are dot product operations, which are the component that requires the highest computation...

Perceptions of drones, digital adherence monitoring technologies and educational videos for tuberculosis control in remote Madagascar: a mixed-method study protocol.

INTRODUCTION: Poor road and communication infrastructure pose major challenges to tuberculosis (TB) control in many regions of the world. TB surveilla...

Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction.

BACKGROUND: The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of c...

Identifying floating plastic marine debris using a deep learning approach.

Estimating the volume of macro-plastics which dot the world's oceans is one of the most pressing environmental concerns of our time. Prevailing method...

Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization.

Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either hand-crafted algorithms or machine learning approaches s...

Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN.

Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compar...

Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.

Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is ke...

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of dr...

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks.

Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy re...

Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data.

We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of c...

Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography.

A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed ...

A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States.

Despite the usefulness of artificial neural networks (ANNs) in the study of various complex problems, ANNs have not been applied for modeling the geog...

The Addition of Viriditec Aqueous Ozone to Peracetic Acid as an Antimicrobial Spray Increases Air Quality While Maintaining Typhimurium, Non-pathogenic , and Reduction on Whole Carcasses.

Currently, the most utilized antimicrobial in processing facilities is peracetic acid, PAA; however, this chemical is increasingly recognized as a haz...

Rapid microarray-based assay for detection of pyrazinamide resistant Mycobacterium tuberculosis.

Pyrazinamide (PZA) is a key antibiotic for the treatment of drug susceptible tuberculosis. PZA-resistance is mainly mediated by mutations in the pncA ...

Quantum dot modified interface for electrochemical immunosensing of procalcitonin for the detection of urinary tract infection.

An electrochemical immunoelectrode is fabricated and investigated for quantification of the Procalcitonin (PCT), a protein over expressed in urinary t...