Development of a DNA-Based Lateral Flow Strip Membrane Assay for Rapid Screening and Genotyping of Six High-Incidence STD Pathogens.

Sexually transmitted diseases (STDs) are a global concern because approximately 1 million new cases emerge daily. Most STDs are curable, but if left untreated, they can cause severe long-term health implications, including infertility and even death. Therefore, a test enabling rapid and accurate screening and genotyping of STD pathogens is highly awaited.

Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network.

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice.

Calibration-free quantitative phase imaging using data-driven aberration modeling.

We present a data-driven approach to compensate for optical aberrations in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field.

Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography.

We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images.

Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions.

Background An angiography-based supervised machine learning ( ML ) algorithm was developed to classify lesions as having fractional flow reserve ≤0.80 versus >0.80.

6 HCV genotyping 9G test and its comparison with VERSANT HCV genotype 2.0 assay (LiPA) for the hepatitis C virus genotyping.

In this article, we describe the 6 HCV Genotyping 9G test and its evaluation by using clinical samples and plasmid DNA standards. In tests with 981 plasmid DNA standards, the 6 HCV Genotyping 9G test showed higher than 92.5% sensitivity and 99.