Cardiovascular hardware simulator and artificial aorta-generated central blood pressure waveform database according to various vascular ages for cardiovascular health monitoring applications.

This study presents a database of central blood pressure waveforms according to cardiovascular health conditions, to supplement the lack of clinical data in cardiovascular health research, constructed by a cardiovascular simulator. Blood pressure (BP) is the most frequently measured biomarker, and in addition to systolic and diastolic pressure, its waveform represents the various conditions of cardiovascular health. A BP waveform is formed by overlapping the forward and reflected waves, which are affected by the pulse wave velocity (PWV). The increase in vascular stiffness with aging increases PWV, and the PWV-age distribution curve is called vascular age. For cardiovascular health research, extensive data of central BP waveform is essential, but the clinical data published so far are insufficient and imbalanced in quantity and quality. This study reproduces the central BP waveform using a cardiovascular hardware simulator and artificial aortas, which mimic the physiological structure and properties of the human. The simulator can adjust cardiovascular health conditions to the same level as humans, such as heart rate of 40-100 BPM, stroke volume of 40-100 mL, and peripheral resistance of 12 steps. Also, 6 artificial aortas with vascular ages in the 20-70 were fabricated to reproduce the increase in vascular stiffness due to aging. Vascular age calculated from measured stiffness of artificial aorta and central BP waveform showed an error of less than 3 years from the clinical value. Through this, a total of 636 waveforms were created to construct a central BP waveform database according to controlled various cardiovascular health conditions.

Reproduction of human blood pressure waveform using physiology-based cardiovascular simulator.

This study presents a cardiovascular simulator that mimics the human cardiovascular system's physiological structure and properties to reproduce the human blood pressure waveform. Systolic, diastolic blood pressures, and its waveform are key indicators of cardiovascular health. The blood pressure waveform is closely related to the pulse wave velocity and the overlap of the forward and reflected pressure waves. The presented cardiovascular simulator includes an artificial aorta made of biomimetic silicone. The artificial aorta has the same shape and stiffness as the human standard and is encased with a compliance chamber. The compliance chamber prevents distortion of the blood pressure waveform from strain-softening by applying extravascular pressure. The blood pressure waveform reproduced by the simulator has a pressure range of 80-120 mmHg, a pulse wave velocity of 6.58 m/s, and an augmentation index of 13.3%. These values are in the middle of the human standard range, and the reproduced blood pressure waveform is similar to that of humans. The errors from the human standard values are less than 1 mmHg for blood pressure, 0.05 m/s for pulse wave velocity, and 3% for augmentation index. The changes in blood pressure waveform according to cardiovascular parameters, including heart rate, stroke volume, and peripheral resistance, were evaluated. The same pressure ranges and trends as in humans were observed for systolic and diastolic blood pressures according to cardiovascular parameters.

Correlation analysis of human upper arm parameters to oscillometric signal in automatic blood pressure measurement.

Cardiovascular diseases are the leading cause of global deaths, making cardiovascular health monitoring important. Measuring blood pressure using an automatic sphygmomanometer is the most widely used method to monitor cardiovascular health due to its accessibility, convenience, and strong correlation with cardiovascular diseases. In this work, in order to estimate brachial artery diameter, stiffness, or thickness using an automatic sphygmomanometer, the correlation between upper arm parameters and the oscillometric signal was intensively investigated through analytical, numerical, and experimental approaches. The parametric studies commonly revealed that the inner radius of the brachial artery is the most influential parameter in determining the amplitude of the oscillometric signal. The experimental results of using a cardiovascular simulator (a virtual patient) combined with upper arm phantoms with various inner radii of the brachial artery showed a 6.5% change in the oscillometric signal amplitude with a 10% artery radius variation. It was concluded that the oscillometric signal can be used to evaluate brachial artery diameter. Based on the clinical relationship between brachial artery diameter and cardiovascular risk factors such as hypertension, diabetes, and obesity, this study showed and verified a novel method to monitor brachial artery diameter and hence, cardiovascular risks while measuring blood pressure.

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI.

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903-1.000) for local recurrence; 0.864 (0.726-0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or "radiomic risk score", increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.

Recovery of N2O: Energy-Efficient and Structure-Driven Clathrate-Based Greenhouse Gas Separation.

N2O has 300 times more global warming potential than CO2 and is also one of the main stratospheric ozone-depleting substances emitted by human activities such as agriculture, industry, and the combustion of fossil fuels and solid waste. We present here an energy-efficient clathrate-based greenhouse gas-separation (CBGS) technology that can operate at room temperature for selectively recovering N2O from gas mixtures. Clathrate formation between α-form/ß-form hydroquinone (α-HQ/ß-HQ) and gas mixtures reveals guest-specific and structure-driven selectivity, revealing the preferential capture of N2O in ß-HQ and the molecular sieving characteristics of α-HQ. With a maximum gas storage capacity and cage occupancy of 54.1 cm3 g-1 and 0.86, respectively, HQ clathrate compounds including N2O are stable at room temperature and atmospheric pressure and thus can be easily synthesized, treated, and recycled via commercial CBGS processes. High selectivity for N2O recovery was observed during ß-HQ clathrate formation from N2O/N2 gas mixtures with N2O concentrations exceeding 20%, whereas α-HQ traps only N2 molecules from gas mixtures. Full characterization using X-ray diffraction, scanning electron microscopy, Raman spectroscopy, solid-state nuclear magnetic resonance, and compositional analysis and the formation kinetics of HQ clathrates was conducted to verify the peculiar selectivity behavior and to design the conceptual CBGS process. These results provide a new playground on which to tailor host-guest materials and develop commercial processes for the recovery and/or sequestration of greenhouse gases.