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Cannabinoids are active substances present in plants of the Cannabis genus. Both the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have approved several medicinal products containing natural cannabinoids or their synthetic derivatives for the treatment of drug-resistant epilepsy, nausea and vomiting associated with cancer chemotherapy, anorexia in AIDS patients, and the alleviation of symptoms in patients with multiple sclerosis. In fact, cannabinoids constitute a broad group of molecules with a possible therapeutic potential that could be used in the management of much more diseases than mentioned above; therefore, multiple preclinical and clinical studies on cannabinoids have been carried out in recent years. Danio rerio (zebrafish) is an animal model that has gained more attention lately due to its numerous advantages, including easy and fast reproduction, the significant similarity of the zebrafish genome to the human one, simplicity of genetic modifications, and body transparency during the early stages of development. A number of studies have confirmed the usefulness of this model in toxicological research, experiments related to the impact of early life exposure to xenobiotics, modeling various diseases, and screening tests to detect active substances with promising biological activity. The present paper focuses on the current knowledge of the endocannabinoid system in the zebrafish model, and it summarizes the results and observations from studies investigating the pharmacological effects of natural and synthetic cannabinoids that were carried out in Danio rerio. The presented data support the notion that the zebrafish model is a suitable animal model for use in cannabinoid research.

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The common issue for medical information systems are missing values. Generally, gaps are filled by statistically suggested values or rule-based methods. Another approach is to use the knowledge of information systems working under the same ontology. The medical incomplete system receives a query unable to answer, because of some unknown patient attributes. So, it has to communicate with other medical systems. The result of the collaboration is collective knowledgebase. In this paper, we propose a measure supporting choice of closest pair of systems. It determines the distance between the two systems. We use ERID algorithm to extract rules from incomplete, distributed information systems. Each constructed rule has confidence and support. They allowed to determine the distance between a pair of medical information systems. The proposed solution was verified on the basis of several “manipulated” medical information systems. Next, the solution was verified in systems with randomly selected data. The satisfying results were obtained and based on them, the proposed measure can be successfully used in medical systems to support the work of doctors and the treatment of patients.