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SMAC-armed oncolytic virotherapy enhances the anticancer activity of PD1 blockade by modulating PANoptosis.
Biomark Res
January 2025
Department of Hematology and Medical Oncology, Emory University, 201 Dowman Dr, Atlanta, GA, 30322, USA.
Background: Oncolytic viruses (OVs) are increasingly recognized as promising tools for cancer therapy, as they selectively infect and destroy tumor cells while leaving healthy cells unharmed. Despite considerable progress, the limited therapeutic efficacy of OV-based virotherapy continues to be a significant challenge in cancer treatment.
Methods: The SMAC/DIABLO gene was inserted into the genome of vesicular stomatitis virus (VSV) to generate VSV-S.
Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.
BMC Med Inform Decis Mak
January 2025
Department of Pediatrics, School of Medicine, Ekbatan Hospital, Hamadan University of Medical Sciences, Hamadan, Iran.
Background: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method.
View Article and Find Full Text PDFDevelopment and pilot testing of INTERVENER, a web-based tool to match barriers to the cancer continuum organization to evidence-based interventions.
BMC Health Serv Res
January 2025
Early Detection, Prevention & Infections Branch, International Agency for Research on Cancer, 25 Avenue Tony Garnier, Lyon, 69366 Cedex 07, France.
Background: Barriers to the cancer continuum organization and interventions to approach them have been identified; however, there is a lack of a tool matching them. Our aim was to develop a web-based tool to identify the main barriers to the process of the cancer continuum organization, and propose matched evidence-based interventions (EBI) to overcome them.
Methods: A questionnaire on barriers at six steps of the process of the cancer continuum organization was answered by collaborators.
A machine learning model accurately identifies glycogen storage disease Ia patients based on plasma acylcarnitine profiles.
Orphanet J Rare Dis
January 2025
Laboratory of Metabolic Diseases, Department of Laboratory Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus, Groningen, 30001 - 9700 RB, the Netherlands.
Background: Glycogen storage disease (GSD) Ia is an ultra-rare inherited disorder of carbohydrate metabolism. Patients often present in the first months of life with fasting hypoketotic hypoglycemia and hepatomegaly. The diagnosis of GSD Ia relies on a combination of different biomarkers, mostly routine clinical chemical markers and subsequent genetic confirmation.
View Article and Find Full Text PDFDevelopment and external validation of a machine learning model for brain injury in pediatric patients on extracorporeal membrane oxygenation.
Crit Care
January 2025
Department of Pediatric, West China Second University Hospital, Sichuan University, Chengdu, China.
Background: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research.
Methods: Data from pediatric patients undergoing ECMO were collected from the Chinese Society of Extracorporeal Life Support (CSECLS) registry database and local hospitals.
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