Migratory flows and international travel are triggering an increase in imported cases of schistosomiasis in non-endemic countries, with serology being the recommended method for screening migrants. The present study aims to evaluate, for the first time, the LAMP technique on patients’ urine samples for the diagnosis of imported schistosomiasis in a non-endemic area in comparison to a commercial immunochromatographic test and microscopic examination of feces and urine. A prospective observational study was conducted in sub-Saharan migrants attending at the Tropical Medicine Unit, Almería, Spain. For schistosomiasis diagnosis, serum samples were tested using an immunochromatographic test (Schistosoma ICT IgG-IgM). Stool and urine samples were examined by microcopy. Urine samples were evaluated by combining three LAMP assays for the specific detection of Schistosoma mansoni and S. haematobium, and an assay for the genus Schistosoma. To evaluate the diagnostic accuracy of Schistosoma-LAMP in comparison to microscopy and Schistosoma ICT IgG-IgM test, a Latent Class Analysis (LCA) was performed. A total of 115 patients were included; 21 patients (18.3%) were diagnosed with schistosomiasis confirmed by microscopy, with S. haematobium being the most frequent species identified (18/115; 15.7%). The Schistosoma ICT IgG-IgM test resulted 100% positive and Schistosoma-LAMP was 61.9% positive, reaching as high as 72.2% for S. haematobium. The sensitivity and specificity estimated by LCA, respectively, were: 92% and 76% for Schistosoma ICT IgG-IgM, 68% and 44% for Schistosoma-LAMP and, 46% and 97% for microscopy. The Schistosoma-LAMP technique presented a higher sensitivity than microscopy for the diagnosis of imported urinary schistosomiasis, which could improve the diagnosis both in referral centers and in centers with limited experience or scarce resources and infrastructure.

Strawberries (garden strawberries) belong to the most valuable berry crops and are an excellent dietary low-calorie product. This favorite berry is grown everywhere in Azerbaijan. The main objective of this study was a comparative study of 4 imported English strawberry cultivars Cupid, Fenella, Christine and Flamenco by productivity and its components, and on this basis, the selection of the most productive and adaptive. The study was conducted in a pilot production area (0.62 ha) of the Research Institute of Fruit and Tea Growing of the Ministry of Agriculture of the Azerbaijan Republic, which is located in the lowland part of the Guba district at an altitude of 400 m above sea level. Strawberry seedlings were planted on this site in the fall of 2020. The site was divided into paired rows repeated every 80 cm, and in each such pair the distance between the rows was 30 cm. The distance between the plants in each row was 15 cm. Mulching and drip irrigation were used as the main elements of the technology. When evaluating varieties by productivity in June 2022, when it reached its maximum level, the Fenella cultivar stood out. The productivity of this cultivar was 0.81 kg per bush or 40.2 tons per hectare. The same cultivar was distinguished by its large fruit (length 4.07 ± 0.42 cm, width 3.61 ± 0.55 cm, weight 20.96 ± 6.21 g), which indicates the prospect of its cultivation in the region.

Malaria is rare in Europe, but it is a medical emergency and programs for its control should ensure access to early diagnosis and prompt treatment within 24-48 h of the symptom’s onset. The consistent number of imported malaria cases and the risk of reappearance of autochthonous cases stimulated laboratories in non-endemic countries to evaluate diagnostic methods/algorithms: microscopy remains the gold standard, but with limitations. RDTs have greatly expanded the ability to diagnose malaria for rapid results due to simplicity and low cost, but they lack sensitivity and specificity. PCR-based assays provide more relevant information but need well-trained technicians. According to WHO global Technical Strategy for Malaria 2016-2030, the future direction for a rapid diagnosis is the development of Point-Of-Care Testing, which would be helpful for prompt/accurate treatment and for preventing the diseases spread. Despite the limitations of current diagnostic methods, they play important roles in dealing with the global malaria situation, including decreasing its incidence. Recently evidence suggested that Artificial Intelligence could be utilized for assisting pathologists in malaria diagnosis. Future studies need to be conducted to see whether this system can be used for routine malaria diagnostic procedures.