Time filter

Source Type

Sebastopol, CA, United States

Briggs F.B.S.,University of California at Berkeley | Acuna B.,Kaiser Permanente | Shen L.,Kaiser Permanente | Ramsay P.,University of California at Berkeley | And 10 more authors.
Epidemiology | Year: 2014

BACKGROUND: Tobacco smoke is an established risk factor for multiple sclerosis (MS). We hypothesized that variation in genes involved in metabolism of tobacco smoke constituents may modify MS risk in smokers. METHODS: A three-stage gene-environment investigation was conducted for NAT1, NAT2, and GSTP1 variants. The discovery analysis was conducted among 1588 white MS cases and controls from the Kaiser Permanente Northern California Region HealthPlan (Kaiser). The replication analysis was carried out in 988 white MS cases and controls from Sweden. RESULTS: Tobacco smoke exposure at the age of 20 years was associated with greater MS risk in both data sets (in Kaiser, odds ratio [OR] = 1.51 [95% confidence interval (CI) = 1.17-1.93]; in Sweden, OR = 1.35 [1.04-1.74]). A total of 42 NAT1 variants showed evidence for interaction with tobacco smoke exposure (Pcorrected < 0.05). Genotypes for 41 NAT1 single nucleotide polymorphisms (SNPs) were studied in the replication data set. A variant (rs7388368C>A) within a dense transcription factor-binding region showed evidence for interaction (Kaiser, OR for interaction = 1.75 [95% CI = 1.19-2.56]; Sweden, OR = 1.62 [1.05-2.49]). Tobacco smoke exposure was associated with MS risk among rs7388368A carriers only; homozygote individuals had the highest risk (A/A, OR = 5.17 [95% CI = 2.17-12.33]). CONCLUSIONS: We conducted a three-stage analysis using two population-based case-control datasets that consisted of a discovery population, a replication population, and a pooled analysis. NAT1 emerged as a genetic effect modifier of tobacco smoke exposure in MS susceptibility. Copyright © 2014 by Lippincott Williams & Wilkins.

Winkler C.,Fairfield University | Funk M.,Yale University | Schindler D.M.,Palm Drive Hospital | Hemsey J.Z.,University of California at San Francisco | And 2 more authors.
Heart and Lung: Journal of Acute and Critical Care | Year: 2013

Objectives: In patients with acute coronary syndrome (ACS), we sought to: 1) describe arrhythmias during hospitalization, 2) explore the association between arrhythmias and patient outcomes, and 3) explore predictors of the occurrence of arrhythmias. Methods: In a prospective sub-study of the IMMEDIATE AIM study, we analyzed electrocardiographic (ECG) data from 278 patients with ACS. On emergency department admission, a Holter recorder was attached for continuous 12-lead ECG monitoring. Results: Approximately 22% of patients had more than 50 premature ventricular contractions (PVCs) per hour. Non-sustained ventricular tachycardia (VT) occurred in 15% of patients. Very few patients (≤1%) had a malignant arrhythmia (sustained VT, asystole, torsade de pointes, or ventricular fibrillation). Only more than 50PVCs/hour independently predicted an increased length of stay ( p<.0001). No arrhythmias predicted mortality. Age greater than 65 years and a final diagnosis of acute myocardial infarction independently predicted more than 50PVCs per hour ( p=.0004). Conclusions: Patients with ACS seem to have fewer serious arrhythmias today, which may have implications for the appropriate use of continuous ECG monitoring. © 2013 Elsevier Inc.

Ghorbanian P.,Villanova University | Devilbiss D.M.,NexStep Biomarkers LLC | Hess T.,Palm Drive Hospital | Bernstein A.,Palm Drive Hospital | And 2 more authors.
Medical and Biological Engineering and Computing | Year: 2015

We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer’s disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer’s disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4–8 Hz ($$\theta$$θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8–12 Hz ($$\alpha$$α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12–30 Hz ($$\beta$$β) followed by lower skewness of the wavelet scales corresponding to 2–4 Hz (upper $$\delta$$δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device. © 2015, International Federation for Medical and Biological Engineering.

Ghorbanian P.,Villanova University | Devilbiss D.M.,NexStep Biomarkers | Simon A.J.,Portable On demand Diagnostics Inc. | Bernstein A.,Palm Drive Hospital | And 2 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2012

In this study, electroencephalogram (EEG) signals obtained by a single-electrode device from 24 subjects - 10 with Alzheimer's disease (AD) and 14 age-matched Controls (CN) - were analyzed using Discrete Wavelet Transform (DWT). The focus of the study is to determine the discriminating EEG features of AD patients while subjected to cognitive and auditory tasks, since AD is characterized by progressive impairments in cognition and memory. At each recording block, DWT extracts EEG features corresponding to major brain frequency bands. T-test and Kruskal-Wallis methods were used to determine the statistically significant features of EEG signals from AD patients compared to Controls. A decision tree algorithm was then used to identify the dominant features for AD patients. It was determined that the mean value of the low-δ (1 - 2 Hz) frequency band during the Paced Auditory Serial Addition Test with 2.0 (s) interval and the mean value of the δ frequency band (12 - 30 Hz) during 6 Hz auditory stimulation have higher mean values in AD patients than Controls. Due to artifacts, the less reliable low-δ features were removed and it was determined that the mean value of β frequency band during 6 Hz auditory stimulation followed by the standard deviation of θ (4 - 8 Hz) frequency band of one card learning cognitive task are higher for AD patients compared to Controls and thus the most dominant discriminating features of the disease. © 2012 IEEE.

Ghorbanian P.,Villanova University | Devilbiss D.M.,NexStep Biomarkers | Verma A.,Brain Computer Interface, LLC | Bernstein A.,Palm Drive Hospital | And 3 more authors.
Annals of Biomedical Engineering | Year: 2013

Alzheimer's disease (AD) is associated with deficits in a number of cognitive processes and executive functions. Moreover, abnormalities in the electroencephalogram (EEG) power spectrum develop with the progression of AD. These features have been traditionally characterized with montage recordings and conventional spectral analysis during resting eyes-closed and resting eyes-open (EO) conditions. In this study, we introduce a single lead dry electrode EEG device which was employed on AD and control subjects during resting and activated battery of cognitive and sensory tasks such as Paced Auditory Serial Addition Test (PASAT) and auditory stimulations. EEG signals were recorded over the left prefrontal cortex (Fp1) from each subject. EEG signals were decomposed into sub-bands approximately corresponding to the major brain frequency bands using several different discrete wavelet transforms and developed statistical features for each band. Decision tree algorithms along with univariate and multivariate statistical analysis were used to identify the most predictive features across resting and active states, separately and collectively. During resting state recordings, we found that the AD patients exhibited elevated D4 (~4-8 Hz) mean power in EO state as their most distinctive feature. During the active states, however, the majority of AD patients exhibited larger minimum D3 (~8-12 Hz) values during auditory stimulation (18 Hz) combined with increased kurtosis of D5 (~2-4 Hz) during PASAT with 2 s interval. When analyzed using EEG recording data across all tasks, the most predictive AD patient features were a combination of the first two feature sets. However, the dominant discriminating feature for the majority of AD patients were still the same features as the active state analysis. The results from this small sample size pilot study indicate that although EEG recordings during resting conditions are able to differentiate AD from control subjects, EEG activity recorded during active engagement in cognitive and auditory tasks provide important distinct features, some of which may be among the most predictive discriminating features. © 2013 Biomedical Engineering Society.

Discover hidden collaborations