Melanization following afamelanotide is accompanied by reduction in solar urticaria response across a broad spectrum of wavelengths. Further study is warranted to assess clinical benefit under ambient conditions in summer.

Electrocardiogram (ECG) is an efficient and commonly used tool for detecting arrhythmias. With the development of dynamic ECG monitoring, an effective and simple algorithm is needed to deal with large quantities of ECG data. In this study, we proposed a method to detect multiple arrhythmias based on time-frequency analysis and convolutional neural networks. For a short-time (10 s) single-lead ECG signal, the time-frequency distribution matrix of the signal was first obtained using a time-frequency transform method, and then a convolutional neural network was used to discriminate the rhythm of the signal. ECG data in multiple databases were used and were divided into 12 classes. Finally, the performance of three kinds of time-frequency transform methods are evaluated, including short-time Fourier transform (STFT), continuous wavelet transform (CWT), and pseudo Wigner-Ville distribution (PWVD). The best result was obtained by STFT, with an accuracy of 96.65%, an average sensitivity of 96.47%, an average specificity of 99.68%, and an average F 1 score of 96.27%, respectively. Especially, the area under curve (AUC) value is 0.9987. The proposed method in this work may be efficient and valuable to detect multiple arrhythmias for dynamic ECG monitoring. INDEX TERMS Arrhythmia detection, convolutional neural networks, ECG, time-frequency analysis. II. METHODS AND MATERIALS A. OVERVIEW

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This paper presents a highly sensitive closed loop enclosed split ring biosensor operating in microwave frequencies for measuring blood glucose levels in the human body. The proposed microwave glucose biosensor, working on the principle of high field confinement and concentrated energy, has been tested using both in-vitro and in-vivo methods. This principle allows the sensor to concentrate energy at the surface which results in improved accuracy of measurements. For in-vitro measurements, the biosensor has been tested using de-ionized water glucose solutions of different concentrations. The miniaturized micrometer scale biosensor is fabricated over a thin Si-substrate using photolithographic technique. The biosensor has been designed in a way to operate at desired microwave frequencies. Highly confined fields and concentrated energy inside the closed loop line containing the split ring resonators are responsible for the sensitivity enhancement. This new biosensor has obtained a high sensitivity of 82 MHz/mgmL −1 within the clinical diabetic range during in-vivo testing over the human body. In addition, the subjects (undergoing experiments) steady state has been continuously monitored throughout the experiment which helps in improving the accuracy of the results. The proposed biosensor has further obtained a low detection limit of <0.05 wt.% and can be useful for continuous non-invasive blood glucose monitoring.