Achieving Real-Time Prediction of Paroxysmal Atrial Fibrillation Onset by Convolutional Neural Network and Sliding Window on R-R Interval Sequences.

Early diagnosis of paroxysmal atrial fibrillation (PAF) could prompt patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but are challenged by real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning-based PAFNet model that integrates a sliding window technique on raw R-R intervals of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onset.

Determination of α-Dicarbonyl compounds in traditional Chinese herbal medicines.

α-DCs (α-dicarbonyls) have been proven to be closely related to aging and the onset and development of many chronic diseases. The wide presence of this kind of components in various foods and beverages has been unambiguously determined, but their occurrence in various phytomedicines remains in obscurity. In this study, we established and evaluated an HPLC-UV method and used it to measure the contents of four α-DCs including 3-deoxyglucosone (3-DG), glyoxal (GO), methylglyoxal (MGO), and diacetyl (DA) in 35 Chinese herbs after they have been derivatized with 4-nitro-1,2-phenylenediamine.

Re-investigation of in vitro activity of acetohydroxyacid synthase I holoenzyme from Escherichia coli.

Acetohydroxyacid synthase (AHAS) is one of the key enzymes of the biosynthesis of branched-chain amino acids, it is also an effective target for the screening of herbicides and antibiotics. In this study we present a method for preparing Escherichia coli AHAS I holoenzyme (EcAHAS I) with exceptional stability, which provides a solid ground for us to re-investigate the in vitro catalytic properties of the protein. The results show EcAHAS I synthesized in this way exhibits similar function to Bacillus subtilis acetolactate synthase in its catalysis with pyruvate and 2-ketobutyrate (2-KB) as dual-substrate, producing four 2-hydroxy-3-ketoacids including (S)-2-acetolactate, (S)-2-aceto-2-hydroxybutyrate, (S)-2-propionyllactate, and (S)-2-propionyl-2-hydroxybutyrate.

A Semi-supervised Algorithm for Atrial Fibrillation Attack Prediction Using Convolution Auto-encoder of Time Series Signal.

During the initial stages, atrial fibrillation (AF) typically presents as paroxysmal atrial fibrillation (PAF), which may further progress into persistent atrial fibrillation, leading to high-risk diseases such as ischemic stroke and heart failure. Given that the current machine learning algorithms used for predicting AF involve time-consuming and labor-intensive processes of feature extraction and labeling electrocardiogram data, this study proposes a novel two-stage semi-supervised AF attack prediction algorithm. The first stage is designed as unsupervised learning based on convolutional autoencoder (CAE) network when inputting RR interval time series signal, while the second stage is designed as supervised learning using a Long Short-Term Memory (LSTM) model.

[Therapeutic effect of autologous bone marrow stem cell transplantation in the treatment of severe liver damage].

Objective: To evaluate the effect of autologous bone marrow stem cell transplantation in the treatment of severe liver damage.

Methods: Autologous bone marrow (50 ml) was harvested from 6 patients aged 44 to 69 years admitted for severe liver damage. Human bone marrow stem cells (HMSCs) were isolated and transplanted in to the patients' liver.