Vancomycin and linezolid: severe cutaneous adverse reactions to drugs.

Background: Severe cutaneous adverse reactions (SCARs), including Stevens-Johnson Syndrome (SJS), Toxic Epidermal Necrolysis (TEN), Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS), and Acute Generalized Exanthematous Pustulosis (AGEP), pose significant therapeutic challenges. Vancomycin and linezolid have been linked to these life-threatening conditions, necessitating a better understanding of their associated risks.

Methods: We conducted a retrospective analysis using data from the FDA Adverse Event Reporting System (FAERS) database.

Using 3D facial information to predict malnutrition and consequent complications.

Background: Phase angle (PhA) correlates with body composition and could predict the nutrition status of patients and disease prognosis. We aimed to explore the feasibility of predicting PhA-diagnosed malnutrition using facial image information based on deep learning (DL).

Methods: From August 2021 to April 2022, inpatients were enrolled from surgery, gastroenterology, and oncology departments in a tertiary hospital.

Immune Checkpoint Inhibitors-Associated Diabetes and Ketoacidosis Were Found in FDA Adverse Event Reporting System: A Real-World Evidence Database Study.

Objective: To investigate the risk of developing diabetes and ketoacidosis in clinical patients with immune checkpoint inhibitors (ICIs).

Methods: We looked in the FDA Adverse Event Reporting System for reports of ICIs-associated diabetes mellitus (DM) and ketoacidosis between January 2004 and March 2022. We explored the signals using fourfold table-based proportional imbalance algorithms.

Development and evaluation of a deep learning framework for the diagnosis of malnutrition using a 3D facial points cloud: A cross-sectional study.

Background: The feasibility of diagnosing malnutrition using facial features has been validated. A tool to integrate all facial features associated with malnutrition for disease screening is still demanded. This work aims to develop and evaluate a deep learning (DL) framework to accurately determine malnutrition based on a 3D facial points cloud.

Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study.

Background: Prompt diagnosis of malnutrition and appropriate interventions can substantially improve the prognosis of patients with cancer; however, it is difficult to unify the tools for screening malnutrition risk. 3D imaging technology has been emerging as an approach to assisting in the diagnosis of diseases, and we designed this study to explore its application value in identifying the malnutrition phenotype and evaluating nutrition status.

Methods: Hospitalized patients treating with maintenance chemotherapy for advanced malignant tumor of digestive system were recruited from the Department of Oncology, whose NRS 2002 score > 3.