Extended nonnegative matrix factorization for dynamic functional connectivity analysis of fMRI data.

Dynamic functional connectivity (DFC) analysis using functional magnetic resonance imaging (fMRI) technology has attracted increasing attention in revealing brain dynamics in recent years. Although the nonnegative matrix factorization (NMF) method was applied to dynamic subgraph analysis to reveal brain dynamics, its application in DFC analysis was largely limited due to its nonnegative constraint on the input data. This study proposed the extended NMF (eNMF) method that allowed the input matrix and decomposed basis matrix to have negative values without altering the NMF algorithm.

The Associations of Two Novel Inflammation Biomarkers, SIRI and SII, with Mortality Risk in Patients with Chronic Heart Failure.

Background: The associations of two novel inflammation biomarkers, systemic inflammation response index (SIRI) and systemic immune inflammation index (SII), with mortality risk in patients with chronic heart failure (CHF) are not well-characterized.

Methods: This retrospective cohort study included patients with CHF in two medical centers of Chinese People's Liberation Army General Hospital, Beijing, China. The outcomes of this study included in-hospital mortality and long-term mortality.

Impact of body mass index on long-term outcomes in patients undergoing percutaneous coronary intervention stratified by diabetes mellitus: a retrospective cohort study.

Background: Patients with diabetes mellitus (DM) caused by obesity have increased in recent years. The impact of obesity on long-term outcomes in patients undergoing percutaneous coronary intervention (PCI) with or without DM remains unclear.

Methods: We retrospectively analysed data from 1918 patients who underwent PCI.

Joint association of hyperuricemia and chronic kidney disease with mortality in patients with chronic heart failure.

Background: The joint association of hyperuricemia and chronic kidney disease (CKD) with mortality in patients with chronic heart failure (CHF) is not conclusive.

Methods: This retrospective cohort study was conducted in Chinese People's Liberation Army General Hospital, Beijing, China. We included 9,367 patients with CHF, who were hospitalized between January 2011 and June 2019.

Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.

Background: Coronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers.

Automated Analysis of Doppler Echocardiographic Videos as a Screening Tool for Valvular Heart Diseases.

Objectives: This study sought to develop a deep learning (DL) framework to automatically analyze echocardiographic videos for the presence of valvular heart diseases (VHDs).

Background: Although advances in DL have been applied to the interpretation of echocardiograms, such techniques have not been reported for interpretation of color Doppler videos for diagnosing VHDs.

Methods: The authors developed a 3-stage DL framework for automatic screening of echocardiographic videos for mitral stenosis (MS), mitral regurgitation (MR), aortic stenosis (AS), and aortic regurgitation (AR) that classifies echocardiographic views, detects the presence of VHDs, and, when present, quantifies key metrics related to VHD severities.

DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications.

Summary: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research. To improve the performance of DeepKG, a cascaded hybrid information extraction framework is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference.

AIBN-initiated direct thiocyanation of benzylic sp C-H with -thiocyanatosaccharin.

Direct thiocyanations of benzylic compounds have been implemented. Here, a new strategy, involving a free radical reaction pathway initiated by AIBN, was used to construct the benzylic sp C-SCN bond. In this way, the disadvantage of other strategies involving introducing leaving groups in advance to synthesize benzyl thiocyanate compounds was overcome.

Kaempferol ameliorates H9N2 swine influenza virus-induced acute lung injury by inactivation of TLR4/MyD88-mediated NF-κB and MAPK signaling pathways.

Kaempferol, a very common type of dietary flavonoids, has been found to exert antioxidative and anti-inflammatory properties. The purpose of our investigation was designed to reveal the effect of kaempferol on H9N2 influenza virus-induced inflammation in vivo and in vitro. In vivo, BALB/C mice were infected intranasally with H9N2 influenza virus with or without kaempferol treatment to induce acute lung injury (ALI) model.