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We aimed to compare the hypothesized local anesthetic action of amantadine (1-adamantanamine) with that of the known local anesthetic mepivacaine. Motor, proprioceptive, and nociceptive functions were evaluated in rats after intrathecal administration. Amantadine elicited spinal anesthesia in a dose-related fashion and produced a better sensory-selective action over motor blockade ( P < 0.01). On the 50% effective dose ( ED50) basis, the rank of potency on spinal motor, proprioceptive, and nociceptive block was mepivacaine > amantadine ( P < 0.01 for the differences). Amantadine (63.5 μmol/kg) and mepivacaine (7.1 μmol/kg) produced complete spinal block of motor function, proprioception, and nociception. On an equipotent basis ( ED25, ED50, and ED75), the duration of amantadine was longer ( P < 0.01) than that of mepivacaine on spinal motor, proprioceptive, and nociceptive block. Our preclinical data demonstrated that amantadine was less potent than mepivacaine at producing spinal anesthesia. The spinal block duration produced by amantadine was greater than that produced by mepivacaine. Both amantadine and mepivacaine produced a markedly nociceptive-specific blockade. [ABSTRACT FROM AUTHOR]

Veno‐venoarterial extracorporeal membrane oxygenation is an upgraded configuration that provides well‐oxygenated blood to the systemic and pulmonary circulations. Sufficient sized cannulas, gas exchange capability, and other equipment should be selected to fulfil the desired extracorporeal supporting flow for temporary life support. Therefore, an accurate prediction model is used to screen the optimal combinations of cannula sizes and determine adequate extracorporeal flow for gas exchange. Prediction models use a substitution‐rate matrix to choose optimal combinations under hemodynamic stability, and a generalised regression neural network to estimate the desired pump speed required to maintain sufficient flow. Experimental studies are used to validate the proposed model and suggest further modifications in the base/reference for the intensive care unit. [ABSTRACT FROM AUTHOR]

The aorta, including the thoracic and abdominal aortas, distributes blood from the heart to the upper and lower circulatory systems. Abdominal aortic sclerosis can cause abdominal aortic aneurysm. When the diameter of the aorta increases by more than 1.5 times its normal size (e.g., ≥4−5 cm, mean diameter in an adult is 2−3 cm), the risk of aneurysm rupture gradually increases. Aneurysm rupture and its associated bleeding have high mortality rates. Hence, the prediction of large diameter aortic aneurysms is an important issue with great clinical implications. In computed tomography angiography (CTA) imaging examination, an assistive tool for automatic image segmentation must be developed to help cardiologists and radiologists evaluate the risk of rupture progression and make appropriate treatment decisions. In this study, two‐dimensional (2D) spatial convolution operations (corner detection) and a multilayer machine vision classifier enabled the automatic segmentation of the luminal region, vascular intima, and calcified plaques in the abdominal aorta. The 2D spatial convolution operations can efficiently increase focus, remove unwanted noise in the convolutional layer, and segment calcified plaques and the vessel border by using a general regression neural network (GRNN)‐based classifier. The classifier's outputs can also be visualized in color presentation (red, green, and blue color series) to identify vessel borders and calcified plaques. Luminal change rate (LCR), calcified plaque rate (CPR), and ellipse ratio (ER) can be defined as criteria for evaluating changes in the luminal region and locating calcified plaques, which can be used to identify the locations of cross‐sectional shape changes and calcified plaques. Based on the collected CTA images from the clinical case studies, the proposed multilayer classifier has promising results for the intended medical purpose. With the LCR index, the correlation coefficients (R2) for manual image segmentation and automatic image segmentation is >0.90 in positive correlation for vessel borders segmentation. The proposed method is an adaptive learning scheme with potential clinical applications in automatic CTA image segmentation for further abdominal aortic aneurysm (AAA) risk evaluation. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Peripheral arterial disease (PAD) is highly prevalent in haemodialysis (HD) patients with type 2 diabetes. Atherosclerosis may occur in both lower and upper peripheral arteries, causing progressive dialysis access stenosis in HD patients. To assess the risk of PAD, non‐invasive bilateral photoplethysmography (PPG) can be used to obtain continuous variations in blood flow volume in in vivo examinations. The authors propose an astable multivibrator to model the peripheral circulation system and to produce PPG oscillation with time constants, duty ratio (rising time), and amplitude ratio of systolic and diastolic pressures. Then, the bilateral differences in the time constant and duty ratio are used to separate the normal condition from PAD. The machine learning decision‐making process utilises a screening method to automatically detect subjects with and without the risk of PAD. The radial‐based function is employed to parameterise the similarity and dissimilarity levels using probability values. Colour relation analysis incorporates the probability values into the perceptual colour relationships for PAD screening. The experimental results indicate that in comparison with bilateral timing parameters, degree of stenosis, and resistive index, the proposed screening method is efficient in preventing complications of PAD and is easily implemented in an embedded system. [ABSTRACT FROM AUTHOR]

In clinical examinations, narrowed vessel accesses will produce unsteady flow and cause changes in blood flow volume. These phenomena result in pressure, velocity and flow resistance increases and induce stress on vessel walls. The vessel wall vibrates and squeezes the low axial blood flow, a lumped resistor (R) representing resistance to blood flow. The compliance is a pressure against blood volume relationship, a lumped capacitor (C) representing the compliance capacity of the blood vessel. Vibration phenomena results on the elastic vessel wall because of transverse vibration pressure. Turbulent and instability flow will induce simple harmonic motions, and the rise time, amplitude and pulse duration of transverse vibrations are determined by the flow resistances and vessel compliances. Hence, an astable multivibrator as detection model employs time constant, τ = (R × C), to evaluate the flow instability and the dysfunction risk in in‐vitro arteriovenous grafts (AVGs). Experimental results show the time constants have linear regression with a positive correlation as the degree of stenosis increases. Positive pole values, s = (−1/τ), are used to validate that the AVG be unstable because the force responses will continuously grow with time because of vibration increases. For comparison with the computerised analysis and auscultation method, in‐vivo examinations of the proposed equivalent model can be further carried out for screening of AVG dysfunction. [ABSTRACT FROM AUTHOR]