Fiber diameter plays an important role in the properties of electrospinning of nanofibers. However, one major problem is the lack of a comprehensive method that can link processing parameters to nanofibers' diameter. The objective of this study is to develope an artificial neural network (ANN) modeling and multiple regression (MLR) analysis approaches to predict the diameter of nanofibers. Processing parameters, including weight ratio, voltage, injection rate, and distance, were considered as independent variables and the nanofiber diameter as the dependent variable of the ANN model. The results of ANN modeling, especially its high accuracy (R 2 = 0.959) in comparison with MLR results (R 2 = 0.564), introduced the prediction the diameter of nanofibers model (PDNFM) as a comparative model for predicting the diameter of poly (3-caprolactone) (PCL)/gelatin (Gt) nanofibers. According to the result of sensitivity analysis of the model, the values of weight ratio, distance, injection rate, and voltage, respectively, were identified as the most significant parameters which influence PDNFM.

A number of acridone-based oxadiazoles 11a-n have been synthesized and evaluated for their anticonvulsant activity against pentylenetetrazole (PTZ)- and maximal electroshock (MES)-induced seizures in mice. Also, their neurotoxicity was evaluated by the rotarod test. Most of the compounds exhibited better anticonvulsant activity and higher safety respect to the standard drug, phenobarbital. Among the tested derivatives, compounds 11l with ED50 value of 2.08 mg/kg was the most potent compound in the PTZ test. The anticonvulsant effect of compound 11l was blocked by flumazenil, suggesting the involvement of benzodiazepine (BZD) receptors in the anticonvulsant activity of prototype compound 11l. Also, docking study of compound 11l in the BZD-binding site of GABAA receptor confirms possible binding of compound 11l with BZD receptors.

The high-performance ‎liquid chromatography-mass ‎spectrometry (LC-MS) ‎technique is widely applied ‎to ‎routine analysis in many ‎matrices. Despite the ‎enormous application of ‎LC/MS, this technique is ‎subjected to ‎drawbacks ‎called matrix...

Background Hypericum is an important genus in the family Hypericaceae, which includes 484 species. This genus has been grown in temperate regions and used for treating wounds, eczema and burns. The aim of this study was to predict the content of hypericin in Hypericum perforatum in varied ecological and phenological conditions of habitat using artificial neural network techniques [MLP (Multi-Layer Perceptron), RBF (Radial Basis Function) and SVM (Support Vector Machine)]. Results According to the results, the MLP model (R2 = 0.87) had an advantage over RBF (R2 = 0.8) and SVM (R2 = 0.54) models and it was relatively accurate in predicting hypericin content in H. perforatum based on the ecological conditions of site including soil types, its characteristics and plant phenological stages of habitat. The results of sensitivity analysis revealed that phenological stages, hill aspects, total nitrogen, altitude and organic carbon are the most influential factors that have an integral effect on the content of hypericin. Conclusions The designed graphical user interface will help pharmacognosist, manufacturers and producers of medicinal plants and so on to run the MLP model on new data to easily discover the content of hypericin in H. perforatum by entering ecological conditions of site, soil characteristics and plant phenological stages.

No abstract