Helicobacter pylori: A Review of Current Diagnostic and Management Strategies.

As one of the most prevalent infections globally, Helicobacter pylori (H. pylori) continues to present diagnostic and therapeutic challenges for clinicians worldwide. Diagnostically, the "test-and-treat" strategy is the recommended approach for healthcare practitioners when managing this potentially curable disease. The choice of testing method should be based on several factors including patient age, presenting symptoms, and medication use, as well as test reliability, availability, and cost. With rising antibiotic resistance, particularly of macrolides, care must be taken to ensure that therapy is selected based on regional resistance patterns and prior antibiotic exposure. In the USA, macrolide antibiotic resistance rates in some areas have reached or exceeded a generally accepted threshold, such that clarithromycin triple therapy may no longer be an appropriate first-line empiric treatment. Instead, bismuth quadruple therapy should be considered, while levofloxacin-based or alternative macrolide-containing therapies are also options. Once treated, it is essential to test for eradication as untreated H. pylori is associated with serious complications including peptic ulcer disease, mucosa-associated lymphoid tissue lymphoma, and gastric cancer. This review article aims to consolidate current knowledge of H. pylori infection with a particular emphasis on diagnostic and treatment strategies.

Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway.

While rural freeways generally have lower crash rates, interactions between driver behavior, traffic and geometric characteristics, and adverse weather conditions may increase the crash risk along some freeway sections. This paper examines the safety effects of roadway geometrics on crash occurrence along a freeway section that features mountainous terrain and adverse weather. Starting from preliminary exploration using Poisson models, Bayesian hierarchical models with spatial and random effects were developed to efficiently model the crash frequencies on road segments on the 20-mile freeway section of study. Crash data for 6 years (2000-2005), roadway geometry, traffic characteristics and weather information in addition to the effect of steep slopes and adverse weather of snow and dry seasons, were used in the investigation. Estimation of the model coefficients indicates that roadway geometry is significantly associated with crash risk; segments with steep downgrades were found to drastically increase the crash risk. Moreover, this crash risk could be significantly increased during snow season compared to dry season as a confounding effect between grades and pavement condition. Moreover, sites with higher degree of curvature, wider medians and an increase of the number of lanes appear to be associated with lower crash rate. Finally, a Bayesian ranking technique was implemented to rank the hazard levels of the roadway segments; the results confirmed that segments with steep downgrades are more crash prone along the study section.