Targeting Underage Drinking and Driving in Juvenile and Domestic Relations Courts: The Reinforcing Alcohol Prevention (RAP) Program

Alcohol use is a concern among our nation's least experienced drivers, and underage drinking contributes to the overall burden of traffic injury and mortality. The goal of the Reinforcing Alcohol Prevention (RAP) Program was to develop a court‐based program targeting underage drinking and driving among teens. An advisory board created the program which included changes to the drivers' licensing ceremonies and traffic dockets conducted in Juvenile and Domestic Relations Courts. Additions included 5‐minute videos of individuals in alcohol‐related crashes and a crash‐photo display. Exit evaluations garnering impressions/opinions of the program indicated 84% of teenagers felt every teen should view the presentation and 96% would be less likely to drive after drinking. The RAP Program has been well‐received by judges and participants and could be easily implemented by other jurisdictions.     

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results. The present study offers a methodology allowing to provide more valuable information for organisations engaged in the fight against counterfeiting of medicines. A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers. The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.