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1. Measurement of Post-Exercise Response of Local Arterial Parameters Using an Adjustable Microfluidic Tactile Sensor

   https://doi.org/10.1109/EMBC46164.2021.9630432  

   Rahman, Md Mahfuzur; Twiddy, Hannah; Reynolds, Leryn; Hao, Zhili (November 2021, Annual International Conference of the IEEE Engineering in Medicine and Biology Society)

   In this work, we demonstrate an adjustable microfluidic tactile sensor for measurement of post-exercise response of local arterial parameters. The sensor entailed a polydimethylsiloxane (PDMS) microstructure embedded with a 5×1 resistive transducer array. The pulse signal in an artery deflected the microstructure and registered as a resistance change by the transducer aligned at the artery. PDMS layers of different thicknesses were added to adjust the microstructure thickness for achieving good sensor-artery conformity at the radial artery (RA) and the carotid artery (CA). Pulse signals of nine (n=9) young healthy male subjects were measured at-rest and at different times post-exercise, and a medical instrument was used to simultaneously measure their blood pressure and heart rate. Vibration-model-based analysis was conducted on a measured pulse signal to estimate local arterial parameters: elasticity, viscosity, and radius. The arterial elasticity and viscosity increased, and the arterial radius decreased at the two arteries 1min post-exercise, relative to at-rest. The changes in pulse pressure (PP) and mean blood pressure (MAP) between at-rest and 1min post-exercise were not correlated with that of heart rate and arterial parameters. After the large 1min post-exercise response, the arterial parameters and PP all went back to their at-rest values over time post-exercise.Clinical Relevance— The study results show the potential application of an affordable, user-friendly device for a more comprehensive arterial health assessment. 

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2. 213 Extrapulmonary Gas Exchange Through Peritoneal Perfluorocarbon Perfusion

   https://doi.org/10.1017/cts.2022.115  

   Leibowitz, Joshua L.; Naselsky, Warren; Doosthosseini, Mahsa; Aroom, Kevin; Shah, Aakash; Bittle, Gregory J.; Hahn, Jin-Oh; Fathy, Hosam K.; Friedberg, Joseph S. (April 2022, Journal of Clinical and Translational Science)

   OBJECTIVES/GOALS: For patients suffering from respiratory failure there are limited options to support gas exchange aside from mechanical ventilation. Our goal is to design, investigate, and refine a novel device for extrapulmonary gas exchange via peritoneal perfusion with perfluorocarbons (PFC) in an animal model. METHODS/STUDY POPULATION: Hypoxic respiratory failure will be modeled using 50 kg swine mechanically ventilated with subatmospheric (10-12%) oxygen. Through a midline laparotomy, two cannulas, one for inflow and one for outflow, will be placed into the peritoneal space. After abdominal closure, the cannulas will be connected to a device capable of draining, oxygenating, regulating temperature, filtering, and pumping perfluorodecalin at a rate of 3-4 liters per minute. During induced hypoxia, the physiologic response to PFC circulation through the peritoneal space will be monitored with invasive (e.g. arterial and venous blood gases) and non-invasive measurements (e.g. pulse oximetry). RESULTS/ANTICIPATED RESULTS: We anticipate that the initiation of oxygenated perfluorocarbons perfusion through the peritoneal space during induced hypoxia will create an increase in hemoglobin oxygen saturation and partial pressure of oxygen in arterial blood. As we expect gas exchange to be occurring in the microvascular beds of the peritoneal membrane, we expect to observe an increase in the venous blood oxygen content sampled from the inferior vena cava. Using other invasive hemodynamic measures (e.g. cardiac output) and blood samples taken from multiple venous sites, a quantifiable rate of oxygen delivery will be calculable. DISCUSSION/SIGNIFICANCE: Peritoneal perfluorocarbon perfusion, if able to deliver significant amounts of oxygen, would provide a potentially lifesaving therapy for patients in respiratory failure who are unable to be supported with mechanical ventilation alone, and are not candidates for extracorporeal membrane oxygenation. 

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3. Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

   https://doi.org/10.3389/fmedt.2022.1034801  

   Tanade, Cyrus; Chen, S James; Leopold, Jane A; Randles, Amanda (December 2022, Frontiers in Medical Technology)

   BackgroundPersonalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR ${}_{\mathrm{baseline}}$), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines. AimsTo determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR ${}_{\mathrm{invasive}}$) as gold standard. Materials and MethodsPersonalized coronary geometries ( $N=50$) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR ${}_{\mathrm{baseline}}$, was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR ${}_{\mathrm{baseline}}$was validated against FFR ${}_{\mathrm{invasive}}$and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR ${}_{\mathrm{streamlined}}$was created by only incorporating the most sensitive inputs and FFR ${}_{\text{semi-streamlined}}$additionally included patient-specific distal location. ResultsFFR ${}_{\mathrm{baseline}}$was validated against FFR ${}_{\mathrm{invasive}}$via correlation ( $r=0.714$, $p<0.001$), agreement (mean difference: $0.01\pm 0.09$), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR ${}_{\text{semi-streamlined}}$provided identical diagnostic performance with FFR ${}_{\mathrm{baseline}}$. Compared to FFR ${}_{\mathrm{baseline}}$vs. FFR ${}_{\mathrm{invasive}}$, FFR ${}_{\mathrm{streamlined}}$vs. FFR ${}_{\mathrm{invasive}}$had decreased correlation ( $r=0.64$, $p<0.001$), improved agreement (mean difference: $0.01\pm 0.08$), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90). ConclusionStreamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement. 

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4. Computational framework for the generation of one‐dimensional vascular models accounting for uncertainty in networks extracted from medical images

   https://doi.org/10.1113/JP286193  

   Bartolo, Michelle_A; Taylor‐LaPole, Alyssa_M; Gandhi, Darsh; Johnson, Alexandria; Li, Yaqi; Slack, Emma; Stevens, Isaiah; Turner, Zachary_G; Weigand, Justin_D; Puelz, Charles; et al (July 2024, The Journal of Physiology)

   AbstractOne‐dimensional (1D) cardiovascular models offer a non‐invasive method to answer medical questions, including predictions of wave‐reflection, shear stress, functional flow reserve, vascular resistance and compliance. This model type can predict patient‐specific outcomes by solving 1D fluid dynamics equations in geometric networks extracted from medical images. However, the inherent uncertainty inin vivoimaging introduces variability in network size and vessel dimensions, affecting haemodynamic predictions. Understanding the influence of variation in image‐derived properties is essential to assess the fidelity of model predictions. Numerous programs exist to render three‐dimensional surfaces and construct vessel centrelines. Still, there is no exact way to generate vascular trees from the centrelines while accounting for uncertainty in data. This study introduces an innovative framework employing statistical change point analysis to generate labelled trees that encode vessel dimensions and their associated uncertainty from medical images. To test this framework, we explore the impact of uncertainty in 1D haemodynamic predictions in a systemic and pulmonary arterial network. Simulations explore haemodynamic variations resulting from changes in vessel dimensions and segmentation; the latter is achieved by analysing multiple segmentations of the same images. Results demonstrate the importance of accurately defining vessel radii and lengths when generating high‐fidelity patient‐specific haemodynamics models.image Key pointsThis study introduces novel algorithms for generating labelled directed trees from medical images, focusing on accurate junction node placement and radius extraction using change points to provide haemodynamic predictions with uncertainty within expected measurement error.Geometric features, such as vessel dimension (length and radius) and network size, significantly impact pressure and flow predictions in both pulmonary and aortic arterial networks.Standardizing networks to a consistent number of vessels is crucial for meaningful comparisons and decreases haemodynamic uncertainty.Change points are valuable to understanding structural transitions in vascular data, providing an automated and efficient way to detect shifts in vessel characteristics and ensure reliable extraction of representative vessel radii. 

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5. Observer-Based De-Convolution of Deterministic Input in Coprime Multi-Channel Systems with Its Application to Non-Invasive Central Blood Pressure Monitoring

   Ghasemi, Z; Jeon, W; Kim, CS; Gupta, A; Rajamani, R; Hahn, JO (January 2020, Journal of dynamic systems measurement and control)

   Estimating central aortic blood pressure is important for cardiovascular health and risk prediction purposes. Cardiovascular system is a multi-channel dynamical system that yields multiple blood pressures at various body sites in response to central aortic blood pressure. This paper concerns the development and analysis of an observer-based approach to de-convolution of unknown input in a class of coprime multi-channel systems applicable to non-invasive estimation of central aortic blood pressure. A multi-channel system yields multiple outputs in response to a common input. Hence, the relationship between any pair of two outputs constitutes a hypothetical input-output system with unknown input embedded as a state. The central idea underlying our approach is to derive the unknown input by designing an observer for the hypothetical input-output system. In this paper, we developed an unknown input observer (UIO) for input de-convolution in coprime multi-channel systems. We provide a universal design algorithm as well as meaningful physical insights and inherent performance limitations associated with the algorithm. The validity and potential of our approach was illustrated using a case study of estimating central aortic blood pressure waveform from two non-invasively acquired peripheral arterial pulse waveforms. The UIO could reduce the root-mean-squared error associated with the central aortic blood pressure by up to 27.5% and 28.8% against conventional inverse filtering and peripheral arterial pulse scaling techniques. 

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