Small implantable medical devices, such as wireless capsule endoscopes, that can be swallowed have previously been developed. However, these devices cannot continuously operate for more than 8 h because of battery limitations; moreover, additional functionalities cannot be introduced. This paper proposes a design method for a high-efficiency energy transmission transformer (ETT) that can transmit energy transcutaneously to small implantable medical devices using electromagnetic induction. First, the authors propose an unconventional design method to develop such a high-efficiency ETT. This method can be readily used to calculate the exact transmission efficiency for changes in the material and design parameters (i.e., the magnetic material, transmission frequency, load resistance, etc.). Next, the ac-to-ac energy transmission efficiency is calculated and compared with experimental measurements. Then, suitable conditions for practical transmission are identified. A maximum efficiency of 33.1% can be obtained at a transmission frequency of 500 kHz and a receiving power of 100 mW for a receiving coil size of ¿5 mm × 20 mm. Future design optimization is possible by using this method.

Background: A new device for automatic measurement of flow‐mediated vasodilation ( FMD ) using an oscillometric method has been developed to solve technical problems of conventional FMD measurement. This device measures enclosed zone FMD (ez FMD ). The purpose of this study was to evaluate the prognostic value of endothelial function assessed by ez FMD for future cardiovascular events. Methods and Results: We measured ez FMD in 272 participants who underwent health‐screening examinations. First, we investigated cross‐sectional associations between ez FMD and cardiovascular risk factors, and then we assessed the associations between ez FMD and first major cardiovascular events (death from cardiovascular causes, stroke, and coronary revascularization). Univariate regression analysis revealed that ez FMD was significantly correlated with age, triglycerides, glucose, smoking pack‐years, estimated glomerular filtration rate, high‐sensitivity C‐reactive protein, and Framingham risk score. During a median follow‐up period of 36.1 months (interquartile range 18.8–40.1 months), 12 participants died (6 from cardiovascular causes), 3 had stroke, 8 had coronary revascularization, and 10 were hospitalized for heart failure. There was no episode of acute coronary syndrome during the study period. Participants were divided into tertiles (low, intermediate, and high) based on ez FMD . Kaplan–Meier curves for first major cardiovascular events among the 3 groups were significantly different ( P =0.004). After adjustment for cardiovascular risk factors, the low group was significantly associated with an increased risk of first major cardiovascular events compared with the high group (hazard ratio 6.47; 95% CI 1.09–125.55; P =0.038). Conclusions: These findings suggest that endothelial function assessed by ez FMD may be useful as a surrogate marker of future cardiovascular events. Clinical Trial Registration: URL : https://upload.umin.ac.jp . Unique identifier: UMIN 000004902.

In clinical practice, subjective pain evaluations, e.g., the visual analogue scale and the numeric rating scale, are generally employed, but these are limited in terms of their ability to detect inaccurate reports, and are unsuitable for use in anesthetized patients or those with dementia. We focused on the peripheral sympathetic nerve activity that responds to pain, and propose a method for evaluating pain sensation, including intensity, sharpness, and dullness, using the arterial stiffness index. In the experiment, electrocardiogram, blood pressure, and photoplethysmograms were obtained, and an arterial viscoelastic model was applied to estimate arterial stiffness. The relationships among the stiffness index, self-reported pain sensation, and electrocutaneous stimuli were examined and modelled. The relationship between the stiffness index and pain sensation could be modelled using a sigmoid function with high determination coefficients, whereR² ≥ 0.88,p < 0.01 for intensity,R² ≥ 0.89,p < 0.01 for sharpness, andR² ≥ 0.84,p < 0.01 for dullness when the stimuli could appropriately evoke dull pain.

Although it has been shown that specific foods and nutrients are associated with sleep quality, few studies have examined the association of dietary variety and appetite with sleep quality in older adults.

In this paper, we propose a novel method for predicting acute clinical deterioration triggered by hypotension, ventricular fibrillation, and an undiagnosed multiple disease condition using biological signals, such as heart rate, RR interval, and blood pressure. Efforts trying to predict such acute clinical deterioration events have received much attention from researchers lately, but most of them are targeted to a single symptom. The distinctive feature of the proposed method is that the occurrence of the event is manifested as a probability by applying a recurrent probabilistic neural network, which is embedded with a hidden Markov model and a Gaussian mixture model. Additionally, its machine learning scheme allows it to learn from the sample data and apply it to a wide range of symptoms. The performance of the proposed method was tested using a dataset provided by Physionet and the University of Tokyo Hospital. The results show that the proposed method has a prediction accuracy of 92.5% for patients with acute hypotension and can predict the occurrence of ventricular fibrillation 5 min before it occurs with an accuracy of 82.5%. In addition, a multiple disease condition can be predicted 7 min before they occur, with an accuracy of over 90%.