Bee Bread Ameliorates Vascular Inflammation and Impaired Vasorelaxation in Obesity-Induced Vascular Damage Rat Model: The Role of eNOS/NO/cGMP-Signaling Pathway

Obesity and hyperlipidemia are major risk factors for developing vascular diseases. Bee bread (BB) has been reported to exhibit some biological actions, including anti-obesity and anti-hyperlipidemic. This study aims to investigate whether bee bread can ameliorate vascular inflammation and impaired vasorelaxation activity through eNOS/NO/cGMP pathway in obese rats. Forty male Sprague-Dawley rats were randomly divided into four groups (n = 10/group), namely: control (normal group), obese rats (OB group), obese rats treated with bee bread (0.5 g/kg/day, OB/BB group) and obese rats treated with orlistat (10 mg/kg/day, OB/OR group). The latter three groups were given a high-fat diet (HFD) for 6 weeks to induced obesity before being administered with their respective treatments for another 6 weeks. After 12 weeks of the total experimental period, rats in the OB group demonstrated significantly higher Lee obesity index, lipid profile (total cholesterol, triglyceride, low-density lipoprotein), aortic proinflammatory markers (tumor necrosis factor-α, nuclear factor-κβ), aortic structural damage and impairment in vasorelaxation response to acetylcholine (ACh). Bee bread significantly ameliorated the obesity-induced vascular damage manifested by improvements in the lipid profile, aortic inflammatory markers, and the impaired vasorelaxation activity by significantly enhancing nitric oxide release, promoting endothelial nitric oxide synthase (eNOS) and cyclic guanosine monophosphate (cGMP) immunoexpression. These findings suggest that the administration of bee bread ameliorates the impaired vasorelaxation response to ACh by improving eNOS/NO/cGMP-signaling pathway in obese rats, suggesting its vascular therapeutic role.     

Temperature- and pH-sensitive core–shell nanogels as efficient carriers of doxorubicin with potential application in lung cancer treatment

Temperature- and pH-sensitive core–shell nanogels were prepared by one-pot soapless emulsion polymerization of N-isopropylacrylamide and 2-methacryloyloxy benzoic acid with the aid of a crosslinker (core) using poly(ethylene glycol) methyl ether methacrylate as stabilizer (shell). The size of nanogels depended on the crosslinker used, being considerable smaller (around 100?nm) with the use of the acid-labile crosslinker 9-divinyl-2,4,8,10-tetraoxaspiro[5.5]-undecane (DVA). Doxorubicine (DOX) was loaded in nanogels with good efficiency. The empty nanogels were biocompatible for a lung cancer cell line (NCI-H1437), while the DOX-loaded, DVA-crosslinked nanogels resulted with efficient cytotoxicity for that cell line.     

Editorial: Security and Privacy Protection for Mobile Applications and Platforms

Mobile Networks and Applications -     

A computational literature review of football performance analysis through probabilistic topic modeling

This research aims to illustrate the potential use of concepts, techniques, and mining process tools to improve the systematic review process. Thus, a review was performed on two online databases (Scopus and ISI Web of Science) from 2012 to 2019. A total of 9649 studies were identified, which were analyzed using probabilistic topic modeling procedures within a machine learning approach. The Latent Dirichlet Allocation method, chosen for modeling, required the following stages: 1) data cleansing, and 2) data modeling into topics for coherence and perplexity analysis. All research was conducted according to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses in a fully computerized way. The computational literature review is an integral part of a broader literature review process. The results presented met three criteria: (1) literature review for a research area, (2) analysis and classification of journals, and (3) analysis and classification of academic and individual research teams. The contribution of the article is to demonstrate how the publication network is formed in this particular field of research, and how the content of abstracts can be automatically analyzed to provide a set of research topics for quick understanding and application in future projects.

     

Reactions of microcrystalline fullerene C60 with amino and aza macrocyclic ligands under solvent-free conditions

We evaluated the reactivity under solvent-free conditions of 2-aminomethyl-15-crown-5 (AM15C5), 2-aminomethyl-18-crown-6 (AM18C6), 1,4,8,12-tetraazacyclopentadecane (TACPD) and rac-5,5,7,12,12,14-hexamethyl-1,4,8,11-tetraazacyclotetradecane (tet b) with microcrystalline fullerene C₆₀. The reactions of nucleophilic addition were carried out at temperatures of about 160°C for 5 h in the case of crown ethers and TACPD, and at 180–190°C for 24 h in the case of tet b. Characterization of the products obtained was performed by using Fourier-transform infrared (FTIR) and Raman spectroscopy, laser desorption/ionization time-of-flight (LDI-TOF) mass spectroscopy, scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). To provide an additional insight into the reactivity of macrocyclic compounds with C₆₀ molecules, we employed theoretical calculations in the frame of density functional theory (DFT). According to the results of SEM imaging, macrocyclic reagents are generally unable to deeply penetrate into the bulk of fullerene microcrystals (with a possible exception of TACPD). According to TGA measurements, the reaction efficiency is very low for tet b, whereas the average content of organic fraction in C₆₀-AM15C5 and C₆₀-AM18C6 reaches about 29%, and in C₆₀-TACPD, almost 40%. According to LDI-TOF mass spectral analysis, the products of TACPD, tet b and AM15C5 reactions with fullerene have oligomeric or polymeric structure. By using DFT theoretical calculations, the latter observation was explained by enhanced reactivity of secondary amino groups toward nucleophilic addition onto fullerene cage.     

A novel transfer learning approach for the classification of histological images of colorectal cancer

Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification of eight types of tissues found in CRC histopathological evaluation. We apply Transfer Learning from architectures of Convolutional Neural Networks (CNNs). We modify the structures of CNNs to extract features from the images and input them to well-known machine learning methods: Naive Bayes, Multilayer Perceptron, k-Nearest Neighbors, Random Forest, and Support Vector Machine (SVM). We evaluated 108 extractor–classifier combinations. The one that achieved the best results is DenseNet169 with SVM (RBF), reaching an Accuracy of 92.083% and F1-Score of 92.117%. Therefore, our approach is capable of distinguishing tissues found in CRC histopathological evaluation.