Oral anticoagulants for nonvalvular atrial fibrillation in frail elderly patients: insights from the ARISTOPHANES study

Background Patient frailty amongst patients with nonvalvular atrial fibrillation (NVAF) is associated with adverse health outcomes and increased risk of mortality. Additional evidence is needed to evaluate effective and safe NVAF treatment in this patient population. Objectives This subgroup analysis of the ARISTOPHANES study compared the risk of stroke/systemic embolism (S/SE) and major bleeding (MB) amongst frail NVAF patients prescribed nonvitamin K antagonist oral anticoagulants (NOACs) or warfarin. Methods This comparative retrospective observational study of frail, older NVAF patients who initiated apixaban, dabigatran, rivaroxaban or warfarin from 01JAN2013‐30SEP2015 was conducted using Medicare and 3 US commercial claims databases. To compare each drug, 6 propensity score‐matched (PSM) cohorts were created. Patient cohorts were pooled from 4 databases after PSM. Cox models were used to estimate hazard ratios (HR) of S/SE and MB. Results Amongst NVAF patients, 34% (N = 150 487) met frailty criteria. Apixaban and rivaroxaban were associated with a lower risk of S/SE vs warfarin (apixaban: HR: 0.61, 95% CI: 0.55–0.69; rivaroxaban: HR: 0.79, 95% CI: 0.72–0.87). For MB, apixaban (HR: 0.62, 95% CI: 0.57–0.66) and dabigatran (HR: 0.79, 95% CI: 0.70–0.89) were associated with a lower risk and rivaroxaban (HR: 1.14, 95% CI: 1.08–1.21) was associated with a higher risk vs warfarin. Conclusion Amongst this cohort of frail NVAF patients, NOACs were associated with varying rates of stroke/SE and MB compared with warfarin. Due to the lack of real‐world data regarding OAC treatment in frail patients, these results may inform clinical practice in the treatment of this patient population

Sequence-selective detection of double-stranded DNA sequences using pyrrole-imidazole polyamide microarrays

We describe a microarray format that can detect double-stranded DNA sequences with a high degree of sequence selectivity. Cyclooctyne-derivatized pyrrole-imidazole polyamides were immobilized on azide-modified glass substrates using microcontact printing and a strain-promoted azide-alkyne cycloaddition (SPAAC) reaction. These polyamide-immobilized substrates selectively detected a seven-base-pair binding site incorporated within a double-stranded oligodeoxyribonucleotide sequence even in the presence of an excess of a sequence with a single-base-pair mismatc

A Generic Language for Application-Specific Flow Sampling

Flow records gathered by routers provide valuable coarse-granularity traffic information for several measurement-related network applications. However, due to high volumes of traffic, flow records need to be sampled before they are gathered. Current techniques for producing sampled flow records are either focused on selecting flows from which statistical estimates of traffic volume can be inferred, or have simplistic models for applications. Such sampled flow records are not suitable for many applications with more specific needs, such as ones that make decisions across flows. As a first step towards tailoring the sampling algorithm to an application’s needs, we design a generic language in which any particular application can express the classes of traffic of its interest. Our evaluation investigates the expressive power of our language, and whether flow records have sufficient information to enable sampling of records of relevance to applications. We use templates written in our custom language to instrument sampling tailored to three different applications—BLINC, Snort, and Bro. Our study, based on month-long datasets gathered at two different network locations, shows that by learning local traffic characteristics we can sample relevant flow records near-optimally with low false negatives in diverse applications

ATMEN: A triggered network measurement infrastructure

Web performance measurements and availability tests have been carried out using a variety of infrastructures over the last several years. Disruptions in the Internet can lead to Web sites being unavailable or increase user-perceived latency. The unavailability could be due to DNS, failures in segments of the physical network cutting off thousands of users, or attacks. Prompt reactions to network-wide events can be facilitated by local or remote measurement and monitoring. Better yet, a distributed set of intercommunicating measurement and monitoring entities that react to events dynamically could go a long way to handle disruptions. We have designed and built ATMEN, a triggered measurement infrastructure to communicate and coordinate across various administrative entities. ATMEN nodes can trigger new measurements, query ongoing passive measurements or stored historical measurements on remote nodes, and coordinate the responses to make local decisions. ATMEN reduces wasted measurements by judiciously reusing measurements along three axes: spatial, temporal, and application. We describe the use of ATMEN for key Web applications such as performance based ranking of popular Web sites and availability of DNS servers on which most Web transactions are dependent. The evaluation of ATMEN is done using multiple network monitoring entities called Gigascopes installed across the USA, measurement data of a popular network application involving millions of users distributed across the Internet, and scores of clients to aid in gathering measurement information upon demand. Our results show that such a system can be built in a scalable fashion. 1

Evidence of firms' perceptions toward Electronic Payment Systems (EPS) in Malaysia

This quantitative study examines the expectation of 122 firms in Malaysia regarding electronic payment systems (EPS) and the extent to which banks are meeting these expectations and thus facilitating e-commerce growth in this country. This paper presents survey data on the firms‟ perceptions toward EPS provided by commercial banks in Malaysia. The survey data was analyzed using quadrant, gap, factor, regression analyses, and other statistical tools. The results indicate that the extent of use of electronic cheques, smart cards, and electronic cash is rather sluggish, but that the banks‟ service performance was found to be satisfactory. Study results also indicate that the most important factor influencing the firms‟ perceptions is flexibility of the payment system, followed by functionality, data management, privacy, and security of the EPS. Functionality, privacy, and security were important factors in predicting the level of EPS use

Near ground path gain measurements at 433/868/915/2400 MHz in indoor corridor for wireless sensor networks

Near to ground radio frequency (RF) propagation path gain (PG) measurements at short distances at antenna height of 50 cm from the ground/floor were made in typical narrow and wide straight indoor corridors at 433/868/915/2400 MHz in a modern multi-storied building. The measurement was performed utilizing RF equipment and comparisons were made with Matlab simulations of ray tracing technique, free space model and ITU-R model along with Full-3D ray tracing model of Wireless Insite (WI) software. Measured PG values showed good agreement with WI in all cases. Path loss exponent (PE) values ranging from 1.22 to 2.13 were observed from the measured data. The research work reported in this paper is predominately geared towards characterizing radio link for wireless sensor networks in typical indoor corridor environments

Prediction of COVID 19 using marching learning techniques

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include support vector machine, naive Bayes, random Forest, GNB using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that GNB prediction model has the highest accuracy of 98% compared to other existing ML techniques

Long-term outcomes following left main bifurcation stenting in Indian population—Analysis based on SYNTAX I and II scores

Background: Syntax 1 and recently Syntax 2 (SS2) scores are validated risk prediction models in coronary disease. Objectives: To find out the long term outcomes following stenting for unprotected left main bifurcation disease (LMD) and to validate and compare the performance of the SYNTAX scores 1 and 2 (SS1 and SS2 PCI) for predicting major adverse cardiac events (MACE) in Indian population. Methods: Single-center, retrospective, observational study involving patients who underwent percutaneous coronary intervention (PCI) with at least one stent implanted for the LMD. Discrimination and calibration models were assessed by ROC curve and the Hosmer-Lemeshow test. Results: Data of 103 patients were analyzed. The mean SS1 and SS2 scores were 27.9 and 30.7 and MACE was 16.5% at 4 years. The target lesion revascularization (TLR) rate at 4 years was 11(10.7%). There were 4 deaths (3.8%). The mean left ventricular ejection fraction (LVEF) was the only variable in SS2, which predicted cardiac events. ROC curve analysis showed both models to be accurate in predicting TLR and mortality following LM PCI. SS2 score showed a better risk prediction than SSI with AUC for TLR (SSI 0.560 and SS2PCI 0.625) and AUC for mortality (SS1 0.674 and SS2PCI 0.833). Hosmer-Lemeshow test validated the accuracy of both the risk models in predicting the events. Conclusions: Both risk models were applicable for Indian patients. The SS2 score was a better predictor for mortality and TLR. In the SS2 score, the LVEF was the most useful predictor of events after LM PCI. Keywords: Left main coronary, Bifurcation stenting, Syntax score, Outcome