Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold.

Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design.

Is There a Need for Abdominal CT Scan in Trauma Patients With a Low-Risk Mechanism of Injury and Normal Vital Signs?

Background Clinically significant injuries are often missed in trauma patients with low-risk mechanisms of injury and lack of "red flags," such as abnormal vital signs. The purpose of this retrospective analysis was to evaluate the efficacy of computed axial tomography (CT) for identifying occult injuries in a high-volume trauma center. Methods Records from our institutional trauma registry were retrospectively extracted, examining encounters from January 2015 to October 2019.

Development and analytical validation of a 25-gene next generation sequencing panel that includes the BRCA1 and BRCA2 genes to assess hereditary cancer risk.

Background: Germline DNA mutations that increase the susceptibility of a patient to certain cancers have been identified in various genes, and patients can be screened for mutations in these genes to assess their level of risk for developing cancer. Traditional methods using Sanger sequencing focus on small groups of genes and therefore are unable to screen for numerous genes from several patients simultaneously. The goal of the present study was to validate a 25-gene panel to assess genetic risk for cancer in 8 different tissues using next generation sequencing (NGS) techniques.

Design and validation of an oligonucleotide microarray for the detection of genomic rearrangements associated with common hereditary cancer syndromes.

Background: Conventional Sanger sequencing reliably detects the majority of genetic mutations associated with hereditary cancers, such as single-base changes and small insertions or deletions. However, detection of genomic rearrangements, such as large deletions and duplications, requires special technologies. Microarray analysis has been successfully used to detect large rearrangements (LRs) in genetic disorders.