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Digital health interventions in adult intensive care and recovery after critical illness to promote survivorship care.
J Intensive Care Soc
January 2025
Department of Physiotherapy, Faculty of Medicine, Dentistry and Health Sciences, School of Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.
Digital health refers to the field of using and developing technology to improve health outcomes. Digital health and digital health interventions (DHIs) within the area of intensive care and critical illness survivorship are rapidly evolving. Digital health interventions refer to technologies in clinical interventional format.
View Article and Find Full Text PDFThe promises and perils of circulating tumor DNA for monitoring immunotherapy response in non-small cell lung cancer.
Explor Target Antitumor Ther
November 2024
Division of Pulmonary, Critical Care, and Sleep Disorders Medicine, Department of Medicine, University of Louisville School of Medicine, Louisville, KY 40202, USA.
There has been a rapid expansion of immunotherapy options for non-small cell lung cancer (NSCLC) over the past two decades, particularly with the advent of immune checkpoint inhibitors. Despite the emerging role of immunotherapy in adjuvant and neoadjuvant settings though, relatively few patients will respond to immunotherapy which can be problematic due to expense and toxicity; thus, the development of biomarkers capable of predicting immunotherapeutic response is imperative. Due to the promise of a noninvasive, personalized approach capable of providing comprehensive, real-time monitoring of tumor heterogeneity and evolution, there has been wide interest in the concept of using circulating tumor DNA (ctDNA) to predict treatment response.
View Article and Find Full Text PDFCurrent neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network (RNN) to human reaction times (RTs). We describe an approximation that allows us to constrain the number of time steps an RNN takes to solve a task with human RTs.
View Article and Find Full Text PDFBackground: Limited-angle (LA) dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from LA projections. While optimization-based and deep learning-based methods have been proposed for image reconstruction, their utilization is limited by the requirement for X-ray spectra measurement or paired datasets for model training.
View Article and Find Full Text PDFBackground While key to interpreting findings and assessing generalizability, implementation fidelity is underreported in mobile health (mHealth) literature. We evaluated implementation fidelity of an opt-in, hybrid, two-way texting (2wT) intervention previously demonstrated to improve 12-month retention on antiretroviral therapy (ART) among people living with HIV (PLHIV) in a quasi-experimental study in Lilongwe, Malawi. Methods Short message service (SMS) data and ART refill visit records were used to evaluate adherence to 2wT content, frequency and duration through the lens of the Conceptual Framework for Implementation Fidelity.
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