Non-pharmacy trade concerns the sale of medicinal products outside of pharmacies, such as limited-service pharmacies, supermarkets, petrol stations, shops open to the public, and kiosks. Access to medicinal products via non-pharmacy outlets varies across the European countries, with a general deregulation of this market area observable. Increasing the availability of medicines by allowing patients to obtain them outside of pharmacies contributes to the spread of self-medication. The aim of this article was to review the legal regulations enabling the non-pharmacy trade in OTC (over the counter) medicinal products in European countries, with particular emphasis on the analysis of active substances contained in medicines available in the non-pharmacy trade. This analysis has made it possible to distinguish three categories of countries: (1) where there is a non-pharmacy trade in OTC medicinal products, (2) where there is a limited non-pharmacy trade in OTC medicines, (3) where there is only a pharmacy trade in OTC medicinal products. In the context of these considerations, we highlight the impact of patient access to medicinal products via non-pharmacy sources on raising the prevalence of self-medication. This article identifies the advantages and risks of self-medication, emphasising the role of the pharmacist as an advisor to patients within the scope of the therapies used.

A series of 70 new 3,3'(α,ω-dioxaalkyl)bis(1-alkylimidazolium) chlorides were synthesized. They were characterized with respect to surface active properties and antimicrobial activity against the following pathogens: Staphylococcus aureus, Enterococcus faecalis, Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, Pseudomonas aeruginosa, Candida krusei, and Candida albicans. In this article, besides description of the synthesis, we characterize a set of features of these compounds, concerning their structure (described by the length of the dioxaalkan spacer and the length of the alkyl substituent in the aromatic ring) and surface active properties (critical micelle concentration, value of surface tension at critical micelle concentration, value of surface excess, molecular area of a single particle, and free energy of adsorption of molecule). Then, we present a SAR study for Staphylococcus aureus, as one of the most widespread pathogenic strains, conducted with the help of the Dominance-based Rough Set Approach (DRSA), that involves identification of relevant features and relevant combinations of features being in strong relationship with a high antimicrobial activity of the compounds. The SAR study shows, moreover, that the antimicrobial activity is dependent on the type of substituents and their position at the chloride moiety, as well as on the surface active properties of the compounds.

AbstractLung cancer is rated with the highest incidence and mortality every year compared with other forms of cancer, therefore early detection and diagnosis is essential. Artificial Neural Networks (ANNs) are “artificial intelligence” software which have been used to assess a few prognostic situations. In this study, a database containing 193 patients from Diagnostic and Monitoring of Tuberculosis and Illness of Lungs Ward in Kuyavia and Pomerania Centre of the Pulmonology (Bydgoszcz, Poland) was analysed using ANNs. Each patient was described using 48 factors (i.e. age, sex, data of patient history, results from medical examinations etc.) and, as an output value, the expected presence of lung cancer was established. All 48 features were retrospectively collected and the database was divided into a training set (n=97), testing set (n=48) and a validating set (n=48). The best prediction score of the ANN model (MLP 48-9-2) was above 0.99 of the area under a receiver operator characteristic (ROC) curve. The ANNs were able to correctly classify 47 out of 48 test cases. These data suggest that Artificial Neural Networks can be used in prognosis of lung cancer and could help the physician in diagnosis of patients with the suspicion of lung cancer.

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