Browse Articles

DyGS: A Dynamic Gene Searching Algorithm for Cancer Detection

Wang et al. | Jun 05, 2018

DyGS: A Dynamic Gene Searching Algorithm for Cancer Detection

Wang and Gong developed a novel dynamic gene-searching algorithm called Dynamic Gene Search (DyGS) to create a gene panel for each of the 12 cancers with the highest annual incidence and death rate. The 12 gene panels the DyGS algorithm selected used only 3.5% of the original gene mutation pool, while covering every patient sample. About 40% of each gene panel is druggable, which indicates that the DyGS-generated gene panels can be used for early cancer detection as well as therapeutic targets in treatment methods.

Read More...

A five-year retrospective analysis of Tuberculosis risk factors and their variability in the United States

Kini et al. | Mar 14, 2026

A five-year retrospective analysis of Tuberculosis risk factors and their variability in the United States
Image credit: Kini, Diaz Gaviria, Diaz, and Kini

The main goal of this study is to determine what demographics are related to tuberculosis incidence in the United States populations, particularly if changing demographics are related to differences in tuberculosis risk over two discrete time periods. The major finding is that in the two studied time periods, tuberculosis risk factors were somewhat consistent and may be influenced by things such as immigration, healthcare access, and race or ethnicity, although the top predictor did change.

Read More...

Quantifying right atrial dilation relative to atrial septal defect size using an experimental model

Lee et al. | Dec 06, 2025

Quantifying right atrial dilation relative to atrial septal defect size using an experimental model
Image credit: jesse orrico

To address the limitations in predicting the severity of Atrial Septal Defect (ASD), here the authors utilized a fluid-filled chamber model to quantify the relationship between defect size and right atrial fluid output. The findings confirmed that larger ASD diameters result in a linear increase in fluid output, validating a cost-effective model that can improve clinical prognosis and treatment planning for heart failure risks.

Read More...

Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ

Bhat et al. | Dec 03, 2024

Genetic Bioaugmentation of Oryza sativa to Facilitate Self-Detoxification of Arsenic In-Situ

Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.

Read More...