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.     

Structural,optical and electronic characteristics of Cu and Mg-doped nano MnS sample prepared by molten salt solid state reaction

Journal of Materials Science: Materials in Electronics - Nano Mn0.95M0.05S (M ≡ Cu, Mg) samples were produced using molten salt solid state reaction method. Rietveld analysis of X-ray...     

Amination of biochar surface from watermelon peel for toxic chromium removal enhancement

Watermelon peel residues were used to produce a new biochar by dehydration method. The new biochar has undergone two methods of chemical modification and the effect of this chemical modification on its ability to adsorb Cr(VI) ions from aqueous solution has been investigated. Three biochars, Melon-B, Melon-BO-NH_2 and Melon-BO-TETA, were made from watermelon peel via dehydration with 50% sulfuric acid to give Melon-B followed by oxidation with ozone and amination using ammonium hydroxide to give Melon-BO-NH_2 or Triethylenetetramine(TETA) to give Melon-BO-TETA. The prepared biochars were characterized by BET, BJH,SEM, FT-IR, TGA, DSC and EDAX analyses. The highest removal percentage of Cr(VI) ions was 69% for Melon-B,98% for Melon-BO-NH_2 and 99% for Melon-BO-TETA biochars of 100 mg·L~(-1) Cr(VI) ions initial concentration and 1.0 g·L~(-1) adsorbents dose. The unmodified biochar(Melon-B) and modified biochars(Melon-BO-NH_2 and Melon-BO-TETA) had maximum adsorption capacities(Qm) of 72.46, 123.46, and 333.33 mg·g~(-1), respectively.The amination of biochar reduced the pore size of modified biochar, whereas the surface area was enhanced.The obtained data of isotherm models were tested using different error function equations. The Freundlich,Tempkin and Langmuir isotherm models were best fitted to the experimental data of Melon-B, Melon-BO-NH_2 and Melon-BO-TETA, respectively. The adsorption rate was primarily controlled by pseudo-second–order rate model. Conclusively, the functional groups interactions are important for adsorption mechanisms and expected to control the adsorption process. The adsorption for the Melon-B, Melon-BO-NH_2 and Melon-BO-TETA could be explained for acid–base interaction and hydrogen bonding interaction.     

3HA: Hybrid Hole Healing Algorithm in a Wireless Sensor Networks

In wireless sensor networks (WSNs), the appearance of coverage holes over a large target field is mostly possible. Those holes reduce network performance and may affect the network efficiency. Several approaches were proposed to heal coverage holes in WSNs, but they still suffer from some weaknesses. In this paper we suggest a distributed algorithm, named hybrid hole healing algorithm (3HA), to find the minimum effective patching positions to deploy additional nodes to cover the holes. A hole manager node of each hole is responsible for operating the 3HA algorithm which requires two phases. The first phase finds all candidate patching positions using a Voronoi diagram. It takes all Voronoi vertices within the hole as the initial patching positions list. The second phase reduces as much as possible this list based on integer linear programming and on a probabilistic sensor model. The 3HA algorithm repeats the above phases in rounds, until all Voronoi vertices are covered. Simulation results show that our solution offers a high coverage ratio for various forms and sizes of holes and reduces the number of additional sensors when compared to some algorithms like the Perimeter-based, the Delaunay triangulation-based, the Voronoi-based, and the Trees-based coverage hole healing methods.

     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.