Fatal outcome related to drug reaction with eosinophilia and systemic symptoms: a disproportionality analysis of FAERS database and a systematic review of cases.

Background: Drug rash with eosinophilia and systemic symptoms (DRESS) is a life-threatening severe cutaneous adverse reaction.

Objective: This study aims to study fatal DRESS cases using FAERS database and systematic review.

Methods: Data of the FDA Adverse Event Reporting System (FAERS) database were extracted and manipulated.

Transcriptional landscape and predictive potential of long noncoding RNAs in peritoneal recurrence of gastric cancer.

Background: Long noncoding RNAs (lncRNAs) play a critical role in gastric cancer (GC) progression and metastasis. However, research comprehensively exploring tissue-derived lncRNAs for predicting peritoneal recurrence in patients with GC remains limited. This study aims to investigate the transcriptional landscape of lncRNAs in GC with peritoneal metastasis (PM) and to develop an integrated lncRNA-based score to predict peritoneal recurrence in patients with GC after radical gastrectomy.

Discovery of a Natural Ent-Kaurene Diterpenoid Oridonin as an E3 Ligase Recruiter for PROTACs.

PROTACs have emerged as a therapeutic modality for the targeted degradation of proteins of interest (POIs). Central to PROTAC technology are the E3 ligase recruiters, yet only a few of them have been identified due to the lack of ligandable pockets in ligases, especially among single-subunit ligases. We propose that binders of partner proteins of single-subunit ligases could be repurposed as new ligase recruiters.

Aiding Diagnosis and Classifying of Early Stage Osteonecrosis of the Femoral Head with Convolutional Neural Network Based on Magnetic Resonance Imaging.

Introduction: The Steinberg classification system is commonly used by orthopedic surgeons to stage the severity of patients with osteonecrosis of the femoral head (ONFH), and it includes mild, moderate, and severe grading of each stage based on the area of the femoral head affected. However, clinicians mostly grade approximately by visual assessment or not at all. To accurately distinguish the mild, moderate, or severe grade of early stage ONFH, we propose a convolutional neural network (CNN) based on magnetic resonance imaging (MRI) of the hip joint of patients to accurately grade and aid diagnosis of ONFH.