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.     

Pathway to terrorist behaviors: The role of childhood experiences,personality traits,and ideological motivations in a sample of Iraqi prisoners

Radicalization to terrorism is a multifaceted process with no single theory or approach to explain it. Although research has focused on understanding the process, there is still a dearth of studies that examine an empirically driven pathway to terrorism behavior. This study examines a cross-sectional sample of incarcerated men convicted of terrorism in Iraq (N = 160). A questionnaire-guided interview included adverse childhood experiences (ACEs), conduct disorder (CD), antisocial personality disorder (ASPD), religious and political ideology, views about causes of terrorism, and the severity of terrorist acts. Path analysis was employed to examine the relationships between these factors and to identify the model with the best fit. After adjusting for age, employment, and location, results indicated that ACEs positively impacted CD, ASPD, religious guidance, and terrorism attitudes. ASPD positively affected political commitment and terrorism attitudes, but inversely affected current religious commitment. Political commitment inversely influenced terrorism attitudes. Religious commitment positively influenced the prioritization of religion in life, which subsequently impacted terrorism attitudes and behavior severity. Additionally, attitudes toward terrorism directly affected the severity of terrorism behavior. All paths in the final model were statistically significant at p < 0.05. Although these findings may be limited in generalizability due to the unique sample, results support the complex and interdependent nature of childhood and adult experiences on the development of both terrorism attitudes and the severity of terrorism behavior.