Analysis of electrocardiograms during atrial fibrillation

The research discussed in this article is motivated by the search for an optimal classification of the different types of atrial fibrillation (AF) on the basis of recorded atrial signals. This would facilitate the selection of an optimal therapy. This article focuses on the biophysical models of the genesis of ECG waveforms during AF. The model of the electric activity of the atria was found to have sufficient realism to be used to describe the electric sources during AF. The inclusion of the volume conduction model resulted in electrocardiographic signals that are in all aspects similar to those observed clinically. The model is currently applied to solve various problems related to optimal signal preprocessing and extraction of features in AF signals for the classification of AF abnormalities. The biophysical model of the atrial activity is an essential element in this research, since it is capable of describing the electric source specifications derived from different hypotheses relating to the etiology of AF     

Improved compressed sensing reconstruction for $$^{19}$$ F magnetic resonance imaging

Magnetic Resonance Materials in Physics, Biology and Medicine - In magnetic resonance imaging (MRI), compressed sensing (CS) enables the reconstruction of undersampled sparse data sets. Thus,...     

Automated reference-free detection of motion artifacts in magnetic resonance images

Objectives

Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture.

Materials and methods

T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis.

Results

On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively.

Conclusion

Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

     

Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction

The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic mechanisms into phenotypic AMR, machine learning has been successfully applied. AMR machine learning models typically use nucleotide k-mer counts to represent genomic sequences. While k-mer representation efficiently captures sequence variation, it also results in high-dimensional and sparse data. With limited training data available, achieving acceptable model performance or model interpretability is challenging. In this study, we explore the utility of feature engineering with several biologically relevant signals. We propose to predict the functional impact of observed mutations with PROVEAN to use the predicted impact as a new feature for each protein in an organism’s proteome. The addition of the new features was tested on a total of 19,521 isolates across nine clinically relevant pathogens and 30 different antibiotics. The new features significantly improved the predictive performance of trained AMR models for Pseudomonas aeruginosa, Citrobacter freundii, and Escherichia coli. The balanced accuracy of the respective models of those three pathogens improved by 6.0% on average.     

The Human Host Defense Ribonucleases 1, 3 and 7 Are Elevated in Patients with Sepsis after Major Surgery—A Pilot Study

Sepsis is the most common cause of death in intensive care units and associated with widespread activation of host innate immunity responses. Ribonucleases (RNases) are important components of the innate immune system, however the role of RNases in sepsis has not been investigated. We evaluated serum levels of RNase 1, 3 and 7 in 20 surgical sepsis patients (Sepsis), nine surgical patients (Surgery) and 10 healthy controls (Healthy). RNase 1 and 3 were elevated in Sepsis compared to Surgery (2.2- and 3.1-fold, respectively; both p < 0.0001) or compared to Healthy (3.0- and 15.5-fold, respectively; both p < 0.0001). RNase 1 showed a high predictive value for the development of more than two organ failures (AUC 0.82, p = 0.01). Patients with renal dysfunction revealed higher RNase 1 levels than without renal dysfunction (p = 0.03). RNase 1 and 3 were higher in respiratory failure than without respiratory failure (p < 0.0001 and p = 0.02, respectively). RNase 7 was not detected in Healthy patients and only in two patients of Surgery, however RNase 7 was detected in 10 of 20 Sepsis patients. RNase 7 was higher in renal or metabolic failure than without failure (p = 0.04 and p = 0.02, respectively). In conclusion, RNase 1, 3 and 7 are secreted into serum under conditions with tissue injury, such as major surgery or sepsis. Thus, RNases might serve as laboratory parameters to diagnose and monitor organ failure in sepsis.     

Ultrasonic Fabrication of Polymer Plate Reactors with a Surface Coating

Microreactors offer several advantages compared to the industrial scale when developing new chemical processes. Especially the production and investment costs are low if polymer microreactors are generated by ultrasonic processes. In order to observe the chemical reaction and the flow configuration, these microreactors need to be optically transparent, mechanically stable, and chemically inert to several reagents. The manufacturing process of a transparent polymer plate reactor with a chemically inert surface coating by ultrasonic fabrication is described. Experimental characterization of the microreactors showed that they are leak tight up to a pressure difference of at least 300 kPa and the mixing times are in the range of milliseconds.     

Influence of vacuum on the porosity and mechanical properties in rotational molding

Rotational molding allows the production of seamless, hollow parts with a high degree of design flexibility. Nevertheless, the process has limitations such as long cycle times as long holding times are necessary to produce components free of air inclusions. Within the scope of this article, an experimental setup was developed to evaluate the possibility and resulting effects of using vacuum in the rotational molding process. For this purpose, trials with different vacuum pressure levels and holding times were carried out and the resulting porosity and surface quality as well as the mechanical properties of the specimens were determined. The investigations showed that the use of vacuum allows a significant reduction in porosity even at low holding times, which can considerably reduce the cycle time of the process. However, the extent of possible time saving is limited by the requirement of a good surface quality, on which the vacuum application shows no effect. Furthermore, the mechanical properties of the parts could be slightly improved by using vacuum. POLYM. ENG. SCI., 59:1544–1551 2019. © 2019 Society of Plastics Engineers     

Schnelle lokale Raman‐Messung zur Untersuchung von Konzentrationsprofilen bei Mischvorgängen in Mikrokanälen

For the evaluation of mixing processes in microreactors, it is necessary to be able to investigate them in detail. For this purpose, a novel optical measuring system is presented, which allows the visualization of mixing and diffusion‐limited processes as well as concentration differences in fluid films. It consists of focusing optics with a microscope objective. In combination with a Raman spectrometer, various processes such as diffusion in the microreactor can be measured and investigated non‐invasively.     

Tetrahydroindoles as Multipurpose Screening Compounds and Novel Sirtuin Inhibitors

Indoles are privileged structures in medicinal and bioorganic chemistry that are particularly well suited to serve as platforms for diversity. Among many other therapeutic areas, the indole scaffold has been used to design aromatic compounds useful to interfere with enzymes engaged in the regulation of substrate acylation status, such as sirtuins. However, the planarity of the indole ring is not necessarily optimal for all target enzymes, especially when functionalization with aromatic side chains is required. Replacement of flat scaffolds by nonplanar molecular cores dominated by sp³ hybridization is a common strategy to avoid the disadvantages associated with poor solubility and high promiscuity, while covering less-well-explored areas of chemical space. Thus, we synthesized fragment-like tetrahydroindoles suitable for fragment-based drug discovery as well as a well-characterized small library intended as multipurpose screening compounds. For proof of principle, these compounds were screened against sirtuins 1–3, enzymes known to be addressable by indoles. We found that 2,6,6-trimethyl-4-oxo-4,5,6,7-tetrahydro-1H-indole-3-carboxamides are potent and selective SIRT2 inhibitors. Compound 16 t displayed an IC₅₀ value of 0.98 μm and could serve as exquisite starting point for hit-to-lead profiling.