Thermal characterization of solid lipid nanoparticles containing praziquantel

Solid lipid nanoparticles (SLNs), loaded and unloaded with praziquantel (PRZ-load SLN and PRZ-unload SLN) were prepared by two different procedures: (a) oil-in-water hot microemulsion method, obtaining at 70 °C an optically transparent blend composed of surfactant, co-surfactant, and water; and (b) oil-in-water microemulsion method, dissolving the lipid in an immiscible organic solvent, emulsified in water containing surfactants and co-surfactant, and then evaporated under reduced pressure at 50 °C. The mean diameter, polydispersity index (PdI), and zeta potential were 187 to 665 nm, 0.300 to 0.655, and −25 to −28 mV respectively, depending on the preparation method. The components, binary mixture, SLNs loaded and unloaded with PRZ, and physical mixture were evaluated by differential scanning calorimetry (DSC) and thermogravimetry (TG). The non-isothermal isoconversional Flynn-Wall–Ozawa method was used to determine the kinetic parameters associated with the thermal decomposition of the samples. The experimental data indicated a linear relationship between the apparent activation energy E and the pre-exponential factor A, also called the kinetic compensation effect (KCE), allowing us to determine the stability with respect to the preparation method. Loading with PRZ increased the thermal stability of the SLNs.     

Validation of analytical procedures using HPLC‐ELSD to determine six sesquiterpene lactones in Eremanthus species

Eremanthus species display sesquiterpene lactones with therapeutic potential. We are proposing the development of a new analytical method that has been completely validated to qualify Eremanthus species and its main lactones in raw material using HPLC‐ELSD. For the sample preparation, 10.0 mg of powdered Eremanthus leaves was extracted with a 5 mL MeOH/H₂O (9:1 v/v) solution containing scopoletin at 140 µg/mL as the internal standard. For the separation of eight compounds, six of which were lactones, one internal (IS) and one secondary standard were performed utilizing monolithic columns with a nonlinear gradient. The selectivity, stability, precision and matrix effects parameters showed values of RSD of <10%. The six lactones and scopoletin (IS) were recovered with a proportion between 74 and 90% with accuracy represented by error at ?25.41%. The linear dynamic range was obtained between 10.0 and 310.0 µg/mL for all compounds with r² > 0.9987. The limits of detection and quantitation ranged from 2.00 to 6.79 µg/mL and from 6.00 to 20.40 µg/mL, respectively. Assessing the robustness study, this method can be used in inter‐laboratory studies. Using the HPLC‐ELSD method, six sesquiterpene lactones including 4β,5‐dihydro‐2′,3′‐dihydroxy‐15‐deoxy‐goyazensolide, goyazensolide, 4β,5‐dihydro‐2′,3′‐epoxy‐15‐deoxy‐goyazensolide, centratherin, 4β,5‐dihydro‐15‐deoxy‐goyazensolide and lychnofolide, were detected and quantified from distinct Eremanthus species, which were collected in different regions. Copyright © 2015 John Wiley & Sons, Ltd.     

Spondias tuberosa (Anacardiaceae) leaves: profiling phenolic compounds by HPLC‐DAD and LC–MS/MS and in vivo anti‐inflammatory activity

Spondias tuberosa is a medicinal plant used by several local communities in northeast Brazil to treat infections, digestive disorders and inflammatory conditions. The study aimed to identify and quantify the major phenolic in hydroethanolic extract of leaves from S. tuberosa and to evaluate its anti‐inflammatory potential. The chemical profile of extract was analyzed by HPLC‐DAD and HPLC–MS. The in vivo anti‐inflammatory activity was investigated in carrageenan‐induced hind paw edema and peritonitis models in mice. Identified and quantified through HPLC‐DAD or HPLC‐MS analyses of S. tuberosa extract were the following compounds: chlorogenic acid, caffeic acid, rutin and isoquercitrin. The inflammatory response to carrageenan was significantly reduced in both models by S. tuberosa extract. In hind paw edema, the edematogenic response was reduced by up to 63.6% and the myeloperoxidase activity was completely inhibited. In the peritonitis model, the total cell migration into the peritoneal cavity was reduced by up to 65%. The results obtained give evidence of the anti‐inflammatory action of S. tuberosa and suggest the potential therapeutic benefit of this plant on inflammatory conditions. The chlorogenic acid, caffeic acid, rutin and isoquercitrin identified and quantified in S. tuberosa leaves enable us to suggest that these compounds could be used as chemical markers for quality control of derivative products from this species. Copyright © 2016 John Wiley & Sons, Ltd.     

A generalized eigenvalues classifier with embedded feature selection

Supervised classification is one of the most used methods in machine learning. In case of data characterized by a large number of features, a critical issue is to deal with redundant or irrelevant information. To this extent, an effective algorithm needs to identify a suitable subset of features, as small as possible, for the classification. In this work we present ReGEC_L1, a classifier with embedded feature selection based on the Regularized Generalized Eigenvalue Classifier (ReGEC) and equipped with a L1-norm regularization term. We detail the mathematical formulation and the numerical algorithm. Numerical results, obtained on some de facto standard benchmark data sets, show that the approach we propose produces a remarkable selection of the features, without losing accuracy in the classification. In that respect, our algorithm seems to compare favorably with the SVM_L1 method. A MATLAB implementation of ReGEC_L1 is available at http://www.na.icar.cnr.it/~mariog/regec_l1.html.