A successful oncolytic virus is one that selectively propagates and destroys cancerous tissue without causing excessive damage to the normal surrounding tissue. Oncolytic measles virus (MV) is one such virus that exhibits this characteristic and thus has rapidly emerged as a potentially useful anticancer modality. Derivatives of the Edmonston MV vaccine strain possess a remarkable safety record in humans. Promising results in preclinical animal models and evidence of biological activity in early phase trials contribute to the enthusiasm. Genetic modifications have enabled MV to evolve from a vaccine agent to a potential anticancer therapy. Specifically, alterations of the MV genome have led to improved tumor selectivity and delivery, therapeutic potency, and immune system modulation. In this article, we will review the advancements that have been made in the design and development of MV that have led to its use as a cancer therapy. In addition, we will discuss the evidence supporting its use, as well as the challenges associated with MV as a potential cancer therapeutic.
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http://dx.doi.org/10.2147/OV.S66078 | DOI Listing |
Disabil Rehabil
January 2025
Clinic Institute of Medical and Surgical Specialties (ICEMEQ), Hospital Clinic of Barcelona, Barcelona, Spain.
Purpose: Adherence to home rehabilitation following total knee arthroplasty (TKA) is essential to reach optimal functional outcomes, especially in fast-track procedures. The aim of this study is to identify which sociodemographic and health factors significantly affect adherence in this context.
Methods: This is a secondary analysis of a randomized controlled trial with 52 patients.
JMIR Res Protoc
January 2025
Centre of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan.
Background: The neonatal mortality rate in Pakistan is the third highest in Asia, with 8.6 million preterm babies. These newborns require warmth, nutrition, and infection protection, typically provided by incubators.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFJMIR Form Res
January 2025
School of Psychology, Ulster University, Coleraine, United Kingdom.
Background: Psychologists have developed frameworks to understand many constructs, which have subsequently informed the design of digital mental health interventions (DMHIs) aimed at improving mental health outcomes. The science of happiness is one such domain that holds significant applied importance due to its links to well-being and evidence that happiness can be cultivated through interventions. However, as with many constructs, the unique ways in which individuals experience happiness present major challenges for designing personalized DMHIs.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Division of Services and Interventions Research, National Institute of Mental Health, Bethesda, MD, United States.
Background: Although substantial progress has been made in establishing evidence-based psychosocial clinical interventions and implementation strategies for mental health, translating research into practice-particularly in more accessible, community settings-has been slow.
Objective: This protocol outlines the renewal of the National Institute of Mental Health-funded University of Washington Advanced Laboratories for Accelerating the Reach and Impact of Treatments for Youth and Adults with Mental Illness Center, which draws from human-centered design (HCD) and implementation science to improve clinical interventions and implementation strategies. The Center's second round of funding (2023-2028) focuses on using the Discover, Design and Build, and Test (DDBT) framework to address 3 priority clinical intervention and implementation strategy mechanisms (ie, usability, engagement, and appropriateness), which we identified as challenges to implementation and scalability during the first iteration of the center.
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