A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features.

Atrial fibrillation is the most common sustained cardiac arrhythmia and the electrocardiogram (ECG) is a powerful non-invasive tool for its clinical diagnosis. Automatic AF detection remains a very challenging task due to the high inter-patient variability of ECGs. In this paper, an automatic AF detection scheme is proposed based on a deep learning network that utilizes both raw ECG signal and its discrete wavelet transform (DWT) version.

CapNet: A Deep Learning-based Framework for Estimation of Capnograph Signal from PPG.

Ambulatory respiration signal extraction system is required to maintain continuous surveillance of a patient with respiratory deficiency. The capnograph signal has received a lot of attention in recent years as a valuable indicator of respiratory conditions. However, the typical capnograph signal extraction method is quite expensive and also unpleasant to the patient due to the involvement of a nasal cannula.

Efficacy of a Novel Zigzag Application Pattern for Micropulse Transscleral Cyclophotocoagulation for the Management of Glaucoma.

Prcis: A zigzag mode of application effectively lowered IOP in glaucoma patients at 6 months with a low incidence of complications despite the longer application time. Comparative studies to assess the potential advantages of treating a larger area of ciliary body with this approach are needed.

Purpose: The purpose of this study is to evaluate the clinical outcome of micropulse transscleral laser cyclophotocoagulation with zigzag mode of application in adult glaucomatous patients.

DSWE-Net: A deep learning approach for shear wave elastography and lesion segmentation using single push acoustic radiation force.

Ultrasound-based non-invasive elasticity imaging modalities have received significant consideration for tissue characterization over the last few years. Though substantial advances have been made, the conventional Shear Wave Elastography (SWE) methods still suffer from poor image quality in regions far from the push location, particularly those which rely on single focused ultrasound push beam to generate shear waves. In this study, we propose DSWE-Net, a novel deep learning-based approach that is able to construct Young's modulus maps from ultrasonically tracked tissue velocity data resulting from a single acoustic radiation force (ARF) push.

Population-based estimated reference creatinine values: a novel method of a robust electronic acute kidney injury alert system.

Background: Acute kidney injury (AKI) is common in hospitalized patients. Despite the progress that has been made in the last decade, early identification of AKI cases remains a challenge. In recent years, electronic AKI alert (e-AKI alert) systems have been tested and are usually based on changes in serum creatinine (Cr) values.

Release of an acid phosphatase activity during lily pollen tube growth involves components of the secretory pathway.

An acid phosphatase (acPAse) activity was released during germination and tube growth of pollen of Lilium longiflorum Thunb. By inhibiting components of the secretory pathway, the export of the acPase activity was affected and tube growth stopped. Brefeldin A (1 microM) and cytochalasin D (1 microM), which block the production and transport of secretory vesicles, respectively, inhibited the acPase secretion.