Significance of Relative Left Ventricle Wall Thickness in Predicting Acute Kidney Injury After Video-Assisted Thoracoscopic Surgery for Lung Cancer.

Given the strong association between relative wall thickness (RWT) and cardiovascular dysfunction, this study aims to explore RWT as a novel cardiovascular indicator to predict the risk of acute kidney injury (AKI) after lung cancer surgery and guide clinical interventions. This study retrospectively analyzed 170 patients who underwent video-assisted thoracoscopic surgery (VATS) for lung cancer in Nanjing First Hospital, China, between January 2022 and December 2023. Patients were divided into AKI group (n = 52) and non-AKI group (n = 118) based on the occurrence of AKI.

Real-World Experience With Maribavir for Treatment of Refractory or Resistant Cytomegalovirus Infection in Hematopoietic Cell Transplant Recipients and Hematologic Malignancy Patients.

Background: Refractory and/or resistant (R/R) cytomegalovirus (CMV) infection is a serious complication after allogeneic hematopoietic cell transplantation (HCT). Maribavir, an oral antiviral agent, was approved in November 2021 for the treatment of R/R CMV in transplant recipients. However, real-world data on the use of maribavir in HCT recipients and hematologic malignancy (HM) patients are limited.

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.

Rapid whole genome characterization of antimicrobial-resistant pathogens using long-read sequencing to identify potential healthcare transmission.

Objective: Whole genome sequencing (WGS) can help identify transmission of pathogens causing healthcare-associated infections (HAIs). However, the current gold standard of short-read, Illumina-based WGS is labor and time intensive. Given recent improvements in long-read Oxford Nanopore Technologies (ONT) sequencing, we sought to establish a low resource approach providing accurate WGS-pathogen comparison within a time frame allowing for infection prevention and control (IPC) interventions.