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Physiological closed-loop controlled medical devices are safety-critical systems that combine patient monitors with therapy delivery devices to automatically titrate therapy to meet a patient's current need. Computational models of physiological systems can be used to test these devices and generate pre-clinical evidence of safety and performance before using the devices on patients. The credibility, utility, and acceptability of such model-based test results will depend on, among other factors, the computational model used. We examine how a recently developed risk-informed framework for establishing the credibility of computational models in medical device applications can be applied in the evaluation of physiological closed-loop controlled devices.
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http://dx.doi.org/10.1109/lsc.2018.8572270 | DOI Listing |
bioRxiv
August 2025
Department of Physiology and Pharmacology, State University of New York - Downstate Health Sciences University, Brooklyn, NY 11203, USA.
Despite recent advancements in mapping thalamic and cortical projections, the specific organization of intrathalamic and corticothalamic connectivity remains elusive. Current experimental approaches cannot definitively determine whether these connections are arranged in reciprocal (closed-) or non-reciprocal (open-loop) circuits. We developed a biophysically detailed multi-compartmental model of the mouse whisker pathway, built on anatomical and physiological data.
View Article and Find Full Text PDFMov Disord Clin Pract
August 2025
Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan.
Background: Parkinson's disease (PD) often involves motor fluctuations and dyskinesia, which are difficult to manage with medication alone. Conventional deep brain stimulation (cDBS) effectively alleviates symptoms but has limitations, including the challenge of balancing therapeutic effects against potential side effects, as well as limited battery life. To address these issues, adaptive DBS (aDBS) systems, which dynamically adjust stimulation parameters based on real-time physiological feedback, have attracted growing interest.
View Article and Find Full Text PDFJ Cardiovasc Dev Dis
August 2025
Digital Engineering & Artificial Intelligence Laboratory (DEAL), Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.
Heart failure, a significant global health burden, is divided into heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF), characterized by systolic dysfunction and diastolic stiffness, respectively. While HFrEF benefits from pharmacological and device-based therapies, HFpEF lacks effective treatments, with both conditions leading to high rehospitalization rates and reduced quality of life, especially in older adults with comorbidities. This review explores the role of artificial intelligence (AI) in advancing autonomic neuromodulation for heart failure management.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
June 2025
Circular RNAs (circRNAs), a class of non-coding RNAs characterized by their covalently closed loop structures, play active roles in diverse physiological processes through interactions with biological macromolecules. Despite the growing discovery of circRNAs enabled by high- throughput technologies, their functional annotations remain largely unexplored. This highlights the need for automated batch annotation methods to unveil the functional roles of circRNAs.
View Article and Find Full Text PDFAnesthesiology
August 2025
Department of Anesthesiology, Leiden University Medical Center, Leiden, the Netherlands.
Background: Opioids are associated with potentially severe ventilatory depression. However, in the perioperative setting, when opioids are combined with anesthetics, we remain uninformed on their "isolated" effects on ventilatory control.
Methods: In twelve healthy volunteers of either sex, we tested the effect of 5 clinical doses of fentanyl (initial dose 100 µg, followed by 75 µg at t = 30 and 60 min and 50 µg at t = 90 and 120 min).