Myocardial Extracellular Volume Expansion And The Risk Of Recurrent Atrial Fibrillation After Pulmonary Vein Isolation.

This study tested whether myocardial extracellular volume (ECV) is increased in patients with hypertension and atrial fibrillation (AF) undergoing pulmonary vein isolation and whether there is an association between ECV and post-procedural recurrence of AF. Hypertension is associated with myocardial fibrosis, an increase in ECV, and AF. Data linking these findings are limited. T1 measurements pre-contrast and post-contrast in a cardiac magnetic resonance (CMR) study provide a method for quantification of ECV. Consecutive patients with hypertension and recurrent AF referred for pulmonary vein isolation underwent a contrast CMR study with measurement of ECV and were followed up prospectively for a median of 18 months. The endpoint of interest was late recurrence of AF. Patients had elevated left ventricular (LV) volumes, LV mass, left atrial volumes, and increased ECV (patients with AF, 0.34 ± 0.03; healthy control patients, 0.29 ± 0.03; p < 0.001). There were positive associations between ECV and left atrial volume (r = 0.46, p < 0.01) and LV mass and a negative association between ECV and diastolic function (early mitral annular relaxation [E'], r = -0.55, p < 0.001). In the best overall multivariable model, ECV was the strongest predictor of the primary outcome of recurrent AF (hazard ratio: 1.29; 95% confidence interval: 1.15 to 1.44; p < 0.0001) and the secondary composite outcome of recurrent AF, heart failure admission, and death (hazard ratio: 1.35; 95% confidence interval: 1.21 to 1.51; p < 0.0001). Each 10% increase in ECV was associated with a 29% increased risk of recurrent AF. In patients with AF and hypertension, expansion of ECV is associated with diastolic function and left atrial remodeling and is a strong independent predictor of recurrent AF post-pulmonary vein isolation.

Comparison Between Automated And Microscopic Analysis In Body Fluids Cytology.

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Comparison Of Techniques For The Isolation Of Volatiles From Cashew Apple Juice.

The aim of this study was to compare the performance of the following techniques on the isolation of volatiles of importance for the aroma/flavor of fresh cashew apple juice: dynamic headspace analysis using PorapakQ(®) as trap, solvent extraction with and without further concentration of the isolate, and solid-phase microextraction (fiber DVB/CAR/PDMS). A total of 181 compounds were identified, from which 44 were esters, 20 terpenes, 19 alcohols, 17 hydrocarbons, 15 ketones, 14 aldehydes, among others. Sensory evaluation of the gas chromatography effluents revealed esters (n = 24) and terpenes (n = 10) as the most important aroma compounds. The four techniques were efficient in isolating esters, a chemical class of high impact in the cashew aroma/flavor. However, the dynamic headspace methodology produced an isolate in which the analytes were in greater concentration, which facilitates their identification (gas chromatography-mass spectrometry) and sensory evaluation in the chromatographic effluents. Solvent extraction (dichloromethane) without further concentration of the isolate was the most efficient methodology for the isolation of terpenes. Because these two techniques also isolated in greater concentration the volatiles from other chemical classes important to the cashew aroma, such as aldehydes and alcohols, they were considered the most advantageous for the study of cashew aroma/flavor.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.