CXCL10, SCGN, and H2BC5 as Potential Key Genes Regulated by HCV Infection.

Hepatitis C infections are the main causes of fatal clinical conditions such as cirrhosis and HCC development, and biomarkers are needed to predict the development of these complications. Therefore, it is important to first determine which genes are deregulated in HCV-cells compared to healthy individuals. In our study, we aimed to identify the genes that are commonly upregulated or downregulated in HCV-infected cells using two different databases.

Model reduction of dynamical systems with a novel data-driven approach: The RC-HAVOK algorithm.

This paper introduces a novel data-driven approximation method for the Koopman operator, called the RC-HAVOK algorithm. The RC-HAVOK algorithm combines Reservoir Computing (RC) and the Hankel Alternative View of Koopman (HAVOK) to reduce the size of the linear Koopman operator with a lower error rate. The accuracy and feasibility of the RC-HAVOK algorithm are assessed on Lorenz-like systems and dynamical systems with various nonlinearities, including the quadratic and cubic nonlinearities, hyperbolic tangent function, and piece-wise linear function.

LUNGBANK: a novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer.

Background/aim: LUNGBANK was established as part of Project LUNGMARK, pioneering a biorepository dedicated exclusively to lung cancer research. It employs cutting-edge technologies to streamline the handling of biospecimens, ensuring the acquisition of high-quality samples. This infrastructure is fortified with robust data management capabilities, enabling seamless integration of diverse datasets.

Pediatric mediastinal tumors.

Mediastinal tumors are the most common thoracic tumor in the pediatric population. They include a spectrum of tumors, and most are malignant. These lesions can be anatomically and radiologically classified by means of compartments; anterior, middle, and posterior.

Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification.

Lung cancer is the leading cancer type that causes mortality in both men and women. Computer-aided detection (CAD) and diagnosis systems can play a very important role for helping physicians with cancer treatments. This study proposes a hierarchical deep-fusion learning scheme in a CAD framework for the detection of nodules from computed tomography (CT) scans.