Context: Almond oil is used in traditional and complementary therapies for its numerous health benefits due to high unsaturated fatty acids content.Objectives: This study investigated the composition and in vitro anticancer activity of almond oil from Northern Cyprus and compared with almond oil from Turkey.Materials and methods: Almond oil from Northern Cyprus was obtained by supercritical CO2 extraction and analyzed by GC-MS. Almond oil of Turkey was provided from Turkish pharmacies. Different concentrations of almond oils were incubated for 24 and 48 h with Colo-320 and Colo-741 cells. Cell growth and cytotoxicity were measured by MTT assays. Anticancer and antiprolifetarive activities of almond oils were investigated by immunocytochemistry using antibodies directed against to BMP-2, β-catenin, Ki-67, LGR-5 and Jagged 1.Results: Oleic acid (77.8%; 75.3%), linoleic acid (13.5%; 15.8%), palmitic acid (7.4%; 6.3%), were determined as the major compounds of almond oil from Northern Cyprus and Turkey, respectively. In the MTT assay, both almond oils were found to be active against Colo-320 and Colo-741 cells with 1:1 dilution for both 24 h and 48 h. As a result of immunohistochemical staining, while both almond oils exhibited significant antiproliferative and anticancer activity, these activities were more similar in Colo-320 cells which were treated with Northern Cyprus almond oil.Discussion and conclusion: Almond oil from Northern Cyprus and Turkey may have anticancer and antiproliferative effects on colon cancer cells through molecular signalling pathways and, thus, they could be potential novel therapeutic agents.

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Diterpenoid and norditerpenoid alkaloids were tested against Tribolium casteneum (Herbst.) in order to assess their repellent activity. Of 29 tested alkaloids, 21 compounds showed promising insect repellent activity, while eight of them were not found to be active. The alkaloids were obtained from Delphinium, Consolida and Aconitum species. The highest activity was found in hetisine, a diterpene alkaloid (59.37%) and the lowest activity in another diterpene alkaloid venulol (31.25%).

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile, Annona squamosal, Camellia sinensis (tea), and coriander (Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.

In this research, two nonlinear models, namely; adaptive neuro‐fuzzy inference system and feed‐forward neural network and a classical linear model were employed for the prediction of retention time of isoquercitrin in Coriander sativum L. using the high‐performance liquid chromatography technique. The prediction employed the use of composition of mobile phase and pH as the corresponding input parameters. The performance indices of the models were evaluated using root mean square error, determination co‐efficient, and correlation co‐efficient. The results obtained from the simple models showed that subclustering‐adaptive‐neuro fuzzy inference system gave the best results in both the training and testing phases and boosted the performance accuracy of the simple models. The overall comparison of the results showed that subclustering‐adaptive‐neuro fuzzy inference system ensemble demonstrated outstanding performance and increased the accuracy of the single models and ensemble models in the testing phase, up to 35% and 3%, respectively.

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From the aerial parts of Consolida oliveriana (DC) Schröd. a new norditerpenoid alkaloid consolidine (2) has been isolated, in addition to the known alkaloids pubescenine (1), gigactonine, and delsoline and the diterpenoid alkaloid ajaconine (4). The structure of alkaloid 2 was established on the basis of its physical and spectroscopic data including detailed NMR studies. A detailed NMR study on ajaconine (4) resulted in the revision of 11 13C chemical shift assignments.

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