Charlotte Wang

Brushing Teeth With Purified Water to Reduce Ventilator-Associated Pneumonia

Background:Oral care may decrease the development of ventilator-associated pneumonia (VAP) and improve oral hygiene. However, little evidence is available to guide the development of oral care protocols. The practical effect of toothbrushing on VAP development and oral health and hygiene improvement is inconclusive. Purpose:This study evaluated the effects in postneurosurgical, intensive care unit patients of brushing teeth twice daily with purified water on VAP rates and oral health or hygiene. Methods:This study conducted a randomized controlled pilot trial. Patients consecutively admitted to the surgical intensive care unit at a suburban hospital in 2007 were invited to participate if they met two inclusion criteria: (a) under ventilator support for at least 48 to 72 hours and (b) no current pneumonia. Upon obtaining informed consent, subjects were randomized into experimental and control groups. Both groups received usual hospital care, that is, daily oral care using cotton swabs. The experimental group additionally received a twice-daily oral care protocol of toothbrushing with purified water, elevating the head of the bed, and before-and-after hypopharyngeal suctioning. The control group also received twice-daily mock oral care (elevating the head of the bed, moisturizing the lips, and before-and-after hypopharyngeal suctioning). VAP was defined by a clinical pulmonary infection score of > 6. Oral hygiene and health was assessed after conclusion of the intervention. Results:Patients (N = 53) were predominantly male (64.2%), mean age was 60.6 years old, and most had received emergency surgery (75.5%). After 7 days of toothbrushing with purified water, cumulative VAP rates were significantly lower in the experimental (17%) than in the control (71%; p <.05) group. The experimental group also had significantly better scores for oral health (p <.05) and plaque index (p <.01). Conclusion/Implication for Practice:Findings suggest that, as an inexpensive alternative to existing protocols, toothbrushing twice daily with purified water reduces VAP and improves oral health and hygiene.

Functional trajectory 6 months posthospitalization: a cohort study of older hospitalized patients in Taiwan.

Background: Although it is well-recognized that hospitalization often precipitates functional decline in older patients, there have been few studies to examine these functional changes carefully over multiple points in time. Objective: To describe functional trajectory during and 6 months posthospitalization and to ascertain the predictors that signal different classes of functional trajectory, using latent class analysis. Methods: A cohort study was conducted on 286 older hospitalized patients who were admitted to five surgical-medical units at a tertiary medical center in Northern Taiwan. Results are reported of 241 participants who completed all four scheduled assessments during hospitalization (within 48 hr after admission and before discharge) and 3 and 6 months postdischarge. Functional trajectory was measured using the Barthel index over four time points, and decline was defined as a reduction on the Barthel index scores. Demographics, comorbidities, visual impairment, medications taken, cognitive status, nutritional status, oral health, depressive symptoms, social support, surgical diagnosis, and length of stay were assessed as the predictors of functional trajectory classes. Results: Most (74.3%) participants developed functional decline during hospitalization, and 32.0% had persistent functional impairment at 6 months posthospitalization. Three functional trajectory classes (good, moderate, and poor) were identified, and gender, age, comorbidities, cognitive status, nutritional status, oral health status, and length of stay were associated with different trajectory classes. Conclusion: Visualizing different classes of functional trajectory and studying predictors that signal such differences during and posthospitalization help practitioners understand how function changed and the possible ways to intervene.

Prevalence of geriatric conditions: A hospital-wide survey of 455 geriatric inpatients in a tertiary medical center

The aim of this study was to investigate the prevalence of common geriatric conditions in a tertiary medical center. We conducted a cross-sectional, hospital-wide survey of 455 inpatients, aged 65 and older, from 24 medical and surgical units of a 2200-bed urban academic medical center in Taiwan. Patients were screened in face-to-face interviews for 15 geriatric conditions. The prevalence of geriatric conditions was determined and compared by medical versus surgical services. Our sample of participants had a mean age of 75.3±6.1 years (±S.D.), range=65-92. The prevalence of geriatric conditions ranged from 5% (pressure ulcers) to 57% (polypharmacy; taking>5 prescriptions). The majority was visually impaired (74%) and complained of sleep disturbance during their hospital stay (58%). Prevalence rates of certain geriatric conditions differed significantly between medical and surgical units, suggesting that care should address not only common conditions but also those with higher rates on different units. Furthermore, high rates of geriatric conditions indicate strong needs for care that does not fit into traditional disease models of medicine. Care should be better targeted to address different risks for geriatric conditions of medical versus surgical geriatric inpatients in acute care settings.

GaN-Based Ultraviolet Light Emitting Diodes With Ex Situ Sputtered AlN Nucleation Layer

We demonstrated the electro-optical characteristics of gallium nitride (GaN)-based ultraviolet (UV) light emitting diodes (LEDs) with sputtered aluminum nitride (AlN) nucleation layer. The introduction of the ex situ sputtered AlN nucleation layer improved the crystal quality of the GaN and the n-AlGaN layer of the GaN-based UV LEDs. Hence, the 20-mA output power of UV LEDs with ex situ AlN nucleation layers is higher than that of UV LEDs with GaN nucleation layers. In addition, the enhanced power output of UV LEDs with ex situ AlN nucleation could reach around 52% in magnitude at peak emission wavelengths of 370 nm compared with power outputs of UV LEDs with GaN nucleation layers. Furthermore, UV LEDs with ex situ AlN nucleation show improved reliability. The UV LEDs with ex situ AlN nucleation layer revealed a power output drop of around 9% within 168 hours , which is less than the around 14% power drop of UV LEDs with GaN nucleation layer.

Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies

The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers.

Modeling the Association Between Clusters of SNPs and Disease Responses

The aim of the paper is to discuss the association between SNP genotype data and a disease. For genetic association studies, the statistical analyses with multiple markers have been shown to be more powerful, efficient, and biologically meaningful than single marker association tests. As the number of genetic markers considered is typically large, here we cluster them and then study the association between groups of markers and disease. We propose a two-step procedure: first a Bayesian nonparametric cluster estimate under normalized generalized gamma process mixture models is introduced, so that we are able to incorporate the information from a large-scale SNP data with a much smaller number of explanatory variables. Then, thanks to the introduction of a genetic score, we study the association between the relevant disease response and groups of markers using a logit model. Inference is obtained via an MCMC truncation method recently introduced in the literature. We also provide a review of the state of art of Bayesian nonparametric cluster models and algorithms for the class of mixtures adopted here. Finally, the model is applied to genome-wide association study of Crohn’s disease in a case-control setting.

Motivating the motivators: Lessons learned from the design and evaluation of a social persuasion system

This paper presents the lessons learned in designing and evaluating a social persuasion system. This social persuasion system, called the Playful Bottle, consists of a mobile phone attached to an everyday drinking mug, and motivates office workers to drink healthy quantities of water. This study discusses the results of a 10-week quantitative user study and qualitative focus group interviews. We describe how users interacted with one other through the systems care-giving and care-receiving interface and how the systems social effect influenced drinking behaviors. Based on our findings, we offer lessons learned on how to design an effective social persuasion system. The important lessons leaned in our finding: Motivate the motivator, reduce pressure and lessen the feeling of deprivation, and combine positive with negative reinforcements.

An electronic voting mechanism for fighting bribery and coercion

This paper proposes an electronic voting scheme that can be implemented on the current Internet without any secure channel. Under the long-term private key assumption, this scheme not only satisfies most important security requirements proposed before, such as fairness, eligibility, uniqueness, accuracy, anonymity and so on, but also prevents bribery and coercion. Furthermore, the scheme offers voters mobility and convenience so they can securely and easily cast their vote from any location and on any device using a stable Internet connection, which has a potential for raising voter turnout rates and facilitating the voting process.

Bayesian nonparametric clustering and association studies for candidate SNP observations

Clustering is often considered as the first step in the analysis when dealing with an enormous amount of Single Nucleotide Polymorphism (SNP) genotype data. The lack of biological information could affect the outcome of such procedure. Even if a clustering procedure has been selected and performed, the impact of its uncertainty on the subsequent association analysis is rarely assessed. In this research we propose first a model to cluster SNPs data, then we assess the association between the cluster and a disease. In particular, we adopt a Dirichlet process mixture model with the advantages, with respect to the usual clustering methods, that the number of clusters needs not to be known and fixed in advance and the variation in the assignment of SNPs to clusters can be accounted. In addition, once a clustering of SNPs is obtained, we design an individualized genetic score quantifying the SNP composition in each cluster for every subject, so that we can set up a generalized linear model for association analysis able to incorporate the information from a large-scale SNP dataset, and yet with a much smaller number of explanatory variables. The inference on cluster allocation, the strength of association of each cluster (the collective effect on SNPs in the same cluster), and the susceptibility of each SNP are based on posterior samples from Markov chain Monte Carlo methods and the Binder loss information. We exemplify this Bayesian nonparametric strategy in a genome-wide association study of Crohns disease in a case-control setting. A Bayesian nonparametric model to cluster SNPs data is proposed.A efficient Gibbs sampler is designed and a model based genetic score is computed.Association between groups of SNPs and a disease is assessed via the genetic score.The method is applied to a Crohns disease study.

Detect Rare Events via MICE Algorithm with Optimal Threshold

Class imbalanced classifications are important issues in machine learning since class imbalanced problems usually happen in real applications such as intrusion detection, medical diagnostic/monitoring, oil-spill detection, and credit card fraud detection. It is hard to identify rare events correctly if a learning algorithm is just established based on optimal accuracy, as all samples will be classified into the major group. Many algorithms were proposed to deal with class imbalance problems. In this paper, we focus on MICE algorithm proposed by [15] and improve the algorithm by choosing the optimal threshold based on the posterior probabilities. In addition, we illustrate the reason why the logistic transformation works in MICE. The empirical results show that choosing the optimal threshold vis posterior probabilities can improve the performance of the MICE algorithm.