Characterization of Microbial Activity

The overall goal of this study is to investigate the phenomena that affect the fate and transport of radionuclides in the environment. The objective of this task, “Characterization of Microbial Activity”, is to develop a molecular biological method for the characterization of the microbial population indigenous to the Yucca Mountain Project site, with emphasis in detection and measurement of species or groups of microorganisms that could be involved in actinide and/or metal reduction, and subsurface transport. Subtasks consist of QA planning and preparation, and literature review. This task is part of a cooperative agreement between the UNLV Research Foundation and the U.S. Department of Energy (#DE-FC28-04RW12237) titled “Yucca Mountain Groundwater Characterization”

Making the Postclosure Safety Case for the Proposed Yucca Mountain Repository

The International Atomic Energy Agency (IAEA), in its advisory standard for geological repositories promulgated jointly with the Nuclear Energy Agency (NEA) of the Organization for Economic Co-operation and Development, explicitly distinguishes between the concepts of a safety case and a safety assessment. As defined in the advisory standard, the safety case is a broader set of arguments that provide confidence and substantiate the formal analyses of system safety made through the process of safety assessment. Although the IAEAYs definitions include both preclosure (i.e., operational) safety and post-closure performance in the overall safety assessment and safety case, the emphasis in here is on long-term performance after waste has been emplaced and the repository has been closed. This distinction between pre- and postclosure aspects of the repository is consistent with the U.S. regulatory framework defined by the U.S. Environmental Protection Agency (Chapter 40 of the Code of Federal Regulations, Part 197, or 40 CFR 197) [2] and implemented by the U.S. Nuclear Regulatory Commission (Chapter 10 of the Code of Federal Regulations, Part 63, or 10 CFR 63) [3]. The separation of the pre- and postclosure safety cases is also consistent with the way in which the U.S. Department of Energy has assigned responsibilities for developing the safety case. Bechtel SAIC Company is the Management and Operating contractor responsible for the design and operation of the Yucca Mountain facility and is therefore responsible for the preparation of the preclosure aspects of the safety case. Sandia National Laboratories has lead responsibility for scientific work evaluating post-closure performance, and therefore is responsible for developing the post-closure aspects of the safety case. In the context of the IAEA definitions, both preclosure and postclosure safety, including safety assessment and the safety case, will be documented in the license application being prepared for the proposed Yucca Mountain repository, and in the documents that support that license application

Ablation of Left Atrial Tachycardia following Catheter Ablation of Atrial Fibrillation: 12-Month Success Rates

The treatment of atrial tachycardia following catheter ablation of atrial fibrillation is often challenging. Electrophysiological studies using high-resolution 3D mapping systems have contributed significantly to their understanding, and new ablation approaches have shown high rates of acute terminations with low recurrences for the clinical AT. However, patient populations are very heterogeneous, and long-term data of the freedom from any atrial tachycardia or any arrhythmia are still sparse. To evaluate long-term success, a unified patient population and predefined ablation strategies are preferred. In this study, we present 12-month success and mean 30 month follow-up data of catheter ablation of left atrial tachycardia. All 35 patients had a history of pulmonary vein isolation (PVI), 71% of which had a previous substrate modification. A total of 54 ATs, with a mean cycle length 297 ± 86 ms, 31 macro-reentries, and 4 localized reentries, were targeted. The ablation strategy to be used was given by the study protocol, depending on the type of reentry and the number of critical isthmuses. All available ablation strategies were included: standard (anatomical) lines, individual lines, critical isthmuses, and focal ablation. All ATs were terminated by ablation. A total of 91% terminated upon the first ablation strategy. Freedom from any AT after 12 months was 82%, and from any arrhythmia, it was 77%. The multi-procedure success after 30 months was 65% for any AT and 55% for any arrhythmia. In conclusion, individual ablation strategies based on the reentry mechanism and the number of critical isthmuses seems promising and demonstrates a high long-term clinical success. Tachycardia comprising a single critical isthmus can be ablated by critical isthmus ablation only. These patients present with the highest 12-month and long-term success rates

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Background: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers. Objectives: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. Methods: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). Results: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. Conclusion: Machine learning–based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI

Lifetime prevalence and associated factors of itch with skin conditions:atopic dermatitis, psoriasis and dry skin in individuals aged > 50 years

Background: Itch, common in dermatological conditions, is often accompanied by psychological distress and reduced quality of life. However, research on the prevalence and associated factors of itch with skin conditions in general populations is limited. Objectives: This cross-sectional study aimed to determine the lifetime prevalence of itch with skin conditions and to identify its associated factors in individuals aged &gt; 50 years. Methods: Participants from the Rotterdam Study, a population-based cohort, were interviewed to assess whether they had ever had an itchy skin condition, defining lifetime itch with skin conditions. Over 20 demographic, lifestyle, dermatological and nondermatological factors were recorded. Multivariable logistic regression analysis explored associations between these factors and itch with skin conditions, reported as odds ratios (ORs) with 95% confidence intervals (CIs). Results: In total, 5246 eligible participants were included (age range 51-100 years, median age 67; 56.0% women). The results revealed a -lifetime prevalence of 33.7% for itch with skin conditions. Factors significantly associated with itch were female sex (OR 1.26, 95% CI 1.11-1.43), body mass index (1.02, 1.01-1.03), self-reported atopic dermatitis (4.29, 3.74-4.92), presence of atopic dermatitis (1.97, 1.60-2.43), self - reported psoriasis (2.31, 1.77-3.01), presence of psoriasis (2.11, 1.55-2.87), self-reported dry skin (1.95, 1.73-2.20), self-reported asthma (1.40, 1.08-1.83), renal impairment (1.45, 1.17-1.79), and clinically relevant depressive (1.85, 1.52-2.25) and anxiety symptoms (1.36, 1.11-1.66). Conclusions: This study reveals a substantial one-third lifetime prevalence of itch with skin conditions in individuals aged &gt; 50 years. Significant associations with diverse lifestyle, demographic, dermatological and, intriguingly, nondermatological factors, including renal impairment, imply additional contributors to induction or persistence of itch in individuals with skin conditions.</p

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram

Aims Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments