An observational cohort feasibility study to identify microvesicle and miRNA biomarkers of acute kidney injury following paediatric cardiac surgery

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Objectives: Micro-RNA, small noncoding RNA fragments involved in gene regulation, and microvesicles, membrane-bound particles less than 1 μm known to regulate cellular processes including responses to injury, may serve as disease-specific biomarkers of acute kidney injury. We evaluated the feasibility of measuring these signals as well as other known acute kidney injury biomarkers in a mixed pediatric cardiac surgery population. Design: Single center prospective cohort feasibility study. Setting: PICU. Patients: Twenty-four children (≤ 17 yr) undergoing cardiac surgery with cardiopulmonary bypass without preexisting inflammatory state, acute kidney injury, or extracorporeal life support. Interventions: None. Measurements and Main Results: Acute kidney injury was defined according to modified Kidney Diseases Improving Global Outcomes criteria. Blood and urine samples were collected preoperatively and at 6–12 and 24 hours. Microvesicles derivation was assessed using flow cytometry and NanoSight analysis. Micro-RNAs were isolated from plasma and analyzed by microarray and quantitative real-time polymerase chain reaction. Data completeness for the primary outcomes was 100%. Patients with acute kidney injury (n = 14/24) were younger, underwent longer cardiopulmonary bypass, and required greater inotrope support. Acute kidney injury subjects had different fractional content of platelets and endothelial-derived microvesicles before surgery. Platelets and endothelial microvesicles levels were higher in acute kidney injury patients. A number of micro-RNA species were differentially expressed in acute kidney injury patients. Pathway analysis of candidate target genes in the kidney suggested that the most often affected pathways were phosphatase and tensin homolog and signal transducer and activator of transcription 3 signaling. Conclusions: Microvesicles and micro-RNAs expression patterns in pediatric cardiac surgery patients can be measured in children and potentially serve as tools for stratification of patients at risk of acute kidney injury

Model reconstruction from temporal data for coupled oscillator networks

In a complex system, the interactions between individual agents often lead to emergent collective behavior like spontaneous synchronization, swarming, and pattern formation. The topology of the network of interactions can have a dramatic influence over those dynamics. In many studies, researchers start with a specific model for both the intrinsic dynamics of each agent and the interaction network, and attempt to learn about the dynamics that can be observed in the model. Here we consider the inverse problem: given the dynamics of a system, can one learn about the underlying network? We investigate arbitrary networks of coupled phase-oscillators whose dynamics are characterized by synchronization. We demonstrate that, given sufficient observational data on the transient evolution of each oscillator, one can use machine learning methods to reconstruct the interaction network and simultaneously identify the parameters of a model for the intrinsic dynamics of the oscillators and their coupling.Comment: 27 pages, 7 figures, 16 table

LOFAR observations of radio burst source sizes and scattering in the solar corona

Low frequency radio wave scattering and refraction can have a dramatic effect on the observed size and position of radio sources in the solar corona. The scattering and refraction is thought to be due to fluctuations in electron density caused by turbulence. Hence, determining the true radio source size can provide information on the turbulence in coronal plasma. However, the lack of high spatial resolution radio interferometric observations at low frequencies, such as with the LOw Frequency ARray (LOFAR), has made it difficult to determine the true radio source size and level of radio wave scattering. Here we directly fit the visibilities of a LOFAR observation of a Type IIIb radio burst with an elliptical Gaussian to determine its source size and position. This circumvents the need to image the source and then de-convolve LOFAR's point spread function, which can introduce spurious effects to the source size and shape. For a burst at 34.76 MHz, we find full width at half maximum (FWHM) heights along the major and minor axes to be $18.8^\prime$ $\pm~0.1^\prime$ and $10.2^\prime$ $\pm~0.1^\prime$, respectively, at a plane of sky heliocentric distance of 1.75 R$_\odot$. Our results suggest that the level of density fluctuations in the solar corona is the main cause of the scattering of radio waves, resulting in large source sizes. However, the magnitude of $\varepsilon$ may be smaller than what has been previously derived in observations of radio wave scattering in tied-array images.Comment: 6 pages, 3 figures, accepted for publication in Astronomy & Astrophysic

MitoNeoD:a mitochondria-targeted superoxide probe

Mitochondrial superoxide (O2⋅−) underlies much oxidative damage and redox signaling. Fluorescent probes can detect O2⋅−, but are of limited applicability in vivo, while in cells their usefulness is constrained by side reactions and DNA intercalation. To overcome these limitations, we developed a dual-purpose mitochondrial O2⋅− probe, MitoNeoD, which can assess O2⋅− changes in vivo by mass spectrometry and in vitro by fluorescence. MitoNeoD comprises a O2⋅−-sensitive reduced phenanthridinium moiety modified to prevent DNA intercalation, as well as a carbon-deuterium bond to enhance its selectivity for O2⋅− over non-specific oxidation, and a triphenylphosphonium lipophilic cation moiety leading to the rapid accumulation within mitochondria. We demonstrated that MitoNeoD was a versatile and robust probe to assess changes in mitochondrial O2⋅− from isolated mitochondria to animal models, thus offering a way to examine the many roles of mitochondrial O2⋅−production in health and disease

Coupling MALDI-TOF mass spectrometry protein and specialized metabolite analyses to rapidly discriminate bacterial function

Publisher's version (útgefin grein).For decades, researchers have lacked the ability to rapidly correlate microbial identity with bacterial metabolism. Since specialized metabolites are critical to bacterial function and survival in the environment, we designed a data acquisition and bioinformatics technique (IDBac) that utilizes in situ matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to analyze protein and specialized metabolite spectra recorded from single bacterial colonies picked from agar plates. We demonstrated the power of our approach by discriminating between two Bacillus subtilis strains in 99% sequence similarity in the 16S rRNA gene. Finally, we used IDBac to simultaneously extract protein and specialized metabolite MS profiles from unidentified Lake Michigan sponge-associated bacteria isolated from an agar plate. In just 3 h, we created hierarchical protein MS groupings of 11 environmental isolates (10 MS replicates each, for a total of 110 spectra) that accurately mirrored phylogenetic groupings. We further distinguished isolates within these groupings, which share nearly identical 16S rRNA gene sequence identity, based on interspecies and intraspecies differences in specialized metabolite production. IDBac is an attempt to couple in situ MS analyses of protein content and specialized metabolite production to allow for facile discrimination of closely related bacterial colonies.We thank the following contributors: Russel Cuhel and crew of R. V. Neeskay (University of Milwaukee Wisconsin) for assistance with sediment collection; Keith Jung for collection of the freshwater sponge; Dr. Amanda Bulman (Bruker) for assistance with MALDI-TOF MS protein acquisition parameters; Dr. Terry Moore and Dr. Atul Jain for recrystallizing alpha-cyano-4-hydroxycinnamic acid matrix (CHCA); and Dr. Darrell McCaslin (University of Wisconsin Madison) for testing our method on legacy instruments. This work was supported by National Institute of Child Health and Human Development Grant K12HD055892; the National Institutes of Health Office of Research on Women’s Health; National Geographic Grant CP-044R17; Icelandic Research Fund Grant 152336-051; and University of Illinois at Chicago startup funds.Peer Reviewe

Progressive left ventricular remodeling in patients with hypertrophic cardiomyopathy and severe left ventricular hypertrophy

AbstractObjectivesThe aim of this study was to determine the natural history of patients with hypertrophic cardiomyopathy (HCM) and severe left ventricular hypertrophy (LVH) (i.e., maximal left ventricular wall thickness [MLVWT] ≥30 mm) and whether changes in cardiac morphology influence the course of the disease.BackgroundSevere LVH is common in young and rare among elderly patients with HCM. This has been explained by a high incidence of sudden death. We hypothesized that this age-related difference might be explained by left ventricular wall thinning.MethodsA total of 106 (age 33 ± 15 years; 71 males) consecutive patients with severe LVH underwent history taking, examination, electrocardiography, echocardiography, cardiopulmonary exercise testing, and Holter analysis. Survival data were collected at subsequent clinic visits or by communication with patients and their general practioners. In order to assess morphologic and functional changes, 71 (67.0%) patients (mean age 31 ± 15 years; 47 males) followed at our institution underwent serial (≥1 year) assessment.ResultsOf the 106 patients, the majority (78 [71.6%]) were <40 years of age. During follow-up (92 ± 50 months [range 1 to 169]), 18 (17.0%) patients died or underwent heart transplantation (13 sudden cardiac deaths, 2 heart failure deaths, 1 heart transplantation, 1 stroke, 1 postoperative death). Five-year survival from sudden death was 90.1% (95% confidence interval [CI] 84.0% to 96.3%), and that from heart failure death or transplantation was 97.7% (95% CI 94.5 to 100). In patients serially evaluated over 85 ± 51 months, there was an overall reduction in MLVWT of 0.6 mm/year (95% CI 0.31 to 0.81, p = 0.00004). Wall thinning ≥5 mm was observed in 41 patients (57.7%; age 35 ± 13 years; 28 males). On multivariate analysis, the follow-up duration only predicted wall thinning (0.6 mm/year, 95% CI 0.38 to 0.85, p < 0.00001).ConclusionsLeft ventricular remodeling is common in patients with severe LVH and contributes to the low prevalence of severe LVH seen in middle age and beyond

Significance of preoperative external limiting membrane height on visual prognosis in patients undergoing macular hole surgery

Purpose: To investigate the association between the vertical elevation of the external limiting membrane (ELM) and visual outcome in patients undergoing surgery for idiopathic full-thickness macular hole. Methods: Retrospective observational study of a consecutive cohort of patients undergoing vitrectomy to treat macular hole. The greatest vertical height of the central ELM above the retinal pigment epithelium (ELM height) was measured on spectral domain optical coherence tomography preoperatively. The relationship of ELM height to other preoperative and postoperative variables, including macular hole width and height, and visual acuity was analyzed. Results: Data from 91 eyes of 91 patients who had undergone successful hole closure were included. The mean ELM height was 220 μm (range 100–394). There were significant correlations between the ELM height and the diameter of the hole, hole height, and worsening preoperative visual acuity. For holes less than 400 μm in width, better postoperative visual acuity was significantly predicted by a lower ELM height. Conclusion: The ELM height varies widely in idiopathic macular hole. It is higher in eyes where the hole is wider and also when the hole itself is higher. For holes of less than 400 μm in width, a lower ELM height is a strong independent predictor of a good postoperative outcome. Reprint requests: David H. W. Steel, FRCOphth, Sunderland Eye Infirmary, Queen Alexandra Road, Sunderland, United Kingdom SR2 9HP, e-mail: [email protected] Presented in part at the Floretina meeting, Florence, Italy, April 27, 2017. D. H. W. Steel is a consultant to Alcon. The remaining authors have no financial/conflicting interests to disclose. © 2018 by Ophthalmic Communications Society, Inc

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods.

Peer reviewedNumerous statistical machine learning methods suitable for application to highly correlated features, as exists for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN) and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated (a60, alpha s1 CN, alpha s2 CN, kappa CN, alpha lactalbumin, and beta lactoglobulin B), while NN and RR were the best algorithms for 3 traits each (RCT, k20, and heat stability, and a30, beta CN, and beta lactoglobulin A, respectively), PLSR was best for pH and LASSO was best for CN micelle size. When traits were divided into two classes, SVM had the greatest accuracy for the majority of the traits investigated. While the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error when compared to PLSR from between 0.18% (kappa CN) to 3.67% (heat stability). The use of modern statistical ML methods for trait prediction from MIRS may improve the prediction accuracy for some traits

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

Publisher's version (útgefin grein)In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, we developed IDBac-a low-cost and high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDITOF MS) bioinformatics pipeline. IDBac software is designed for non-experts, is freely available, and capable of analyzing a few to thousands of bacterial colonies. Here, we present procedures for the preparation of bacterial colonies for MALDI-TOF MS analysis, MS instrument operation, and data processing and visualization in IDBac. In particular, we instruct users how to cluster bacteria into dendrograms based on protein MS fingerprints and interactively create Metabolite Association Networks (MANs) from specialized metabolite data.This work was supported by National Institute of General Medical Sciences Grant R01 GM125943, National Geographic Grant CP-044R-17; Icelandic Research Fund Grant 152336-051; and University of Illinois at Chicago startup funds. Also, we thank the following contributors: Dr. Amanda Bulman for assistance with MALDI-TOF MS protein acquisition parameters; Dr. Terry Moore and Dr. Atul Jain for recrystallizing alpha-cyano-4-hydroxycinnamic acid matrix (CHCA).Peer Reviewe