Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices

The acyl chain specificity of biliary phosphatidylcholine is independent from its biosynthetic origin

Abstrac

Solid-state 3He^3\mathrm{He} NMR of the superconducting rubidium endofulleride Rb3(3He@C60)\mathrm{Rb_3(^3He@C_{60})}

A new variant of the superconducting fulleride $\mathrm{Rb_{3}C_{60}}$ is presented, with $\mathrm{^{3}He}$ atoms encapsulated in the $\mathrm{C_{60}}$ cages. The $\mathrm{^{3}He}$ nuclei act as sensitive NMR probes embedded in the material. The superconducting and normal states are characterised by $\mathrm{^{3}He}$ NMR. Evidence is found for co-existing vortex liquid and vortex solid phases below the superconducting transition temperature. A strong dependence of the spin-lattice relaxation time constant on spectral frequency is observed in the superconducting state, as revealed by two-dimensional NMR utilising an inverse Laplace transform. Surprisingly, this phenomenon persists, in attenuated form, at temperatures well above the superconducting transition.Comment: 20 pages, 15 figure

Complete nucleotide sequences and genome organization of a cherry isolate of cherry leaf roll virus

The complete nucleotide sequence of cherry leaf roll virus (CLRV, genus Nepovirus) from a naturally infected cherry tree (Prunus avium cv. Bing) in North America was determined. RNA1 and RNA2 consist of 7,893 and 6,492 nucleotides, respectively, plus a poly-(A) tail. Each RNA encodes a single potential open reading frame. The first 657 nucleotides of RNA1 and RNA2 are 99% identical and include the 5′-UTR and the first 214 deduced amino acids of the polyproteins following the first of two in-frame start codons. Phylogenetic analysis reveals close relationships between CLRV and members of subgroup C of the genus Nepovirus

A Serious Disease of Groundnut Caused by Cowpea Mild Mottle Virus in the Sudan

A disease of groundnut characterized by stunting, downward rolling, mottling, general chlorosis and reduced leaflet size occurred in the Sudan. During 1992-94, surveys showed that the disease was restricted to irrigated groundnut crops grown between the 2 Niles. The viral causal agent had slightly flexuous filamentous particles (626 nm long) and was transmitted by whiteflies. It was identified serologically as cowpea mild mottle carlavirus (CPMMV). This appears to be the first record of natural occurrence of CPMMV on groundnut in the Sudan

Demonstrating approaches to chemically modify the surface of Ag nanoparticles in order to influence their cytotoxicity and biodistribution after single dose acute intravenous administration

With the advance in material science and the need to diversify market applications, silver nanoparticles (AgNPs) are modified by different surface coatings. However, how these surface modifications influence the effects of AgNPs on human health is still largely unknown. We have evaluated the uptake, toxicity and pharmacokinetics of AgNPs coated with citrate, polyethylene glycol, polyvinyl pyrolidone and branched polyethyleneimine (Citrate AgNPs, PEG AgNPs, PVP AgNPs and BPEI AgNPs, respectively). Our results demonstrated that the toxicity of AgNPs depends on the intracellular localization that was highly dependent on the surface charge. BPEI AgNPs ( potential=+46.5mV) induced the highest cytotoxicity and DNA fragmentation in Hepa1c1c7. In addition, it showed the highest damage to the nucleus of liver cells in the exposed mice, which is associated with a high accumulation in liver tissues. The PEG AgNPs ( potential=-16.2mV) showed the cytotoxicity, a long blood circulation, as well as bioaccumulation in spleen (34.33 mu g/g), which suggest better biocompatibility compared to the other chemically modified AgNPs. Moreover, the adsorption ability with bovine serum albumin revealed that the PEG surface of AgNPs has an optimal biological inertia and can effectively resist opsonization or non-specific binding to protein in mice. The overall results indicated that the biodistribution of AgNPs was significantly dependent on surface chemistry: BPEI AgNPs>Citrate AgNPs=PVP AgNPs>PEG AgNPs. This toxicological data could be useful in supporting the development of safe AgNPs for consumer products and drug delivery applications