Studies on Buddleja asiatica antibacterial, antifungal, antispasmodic and Ca++ antagonist activities

Crude extract of Buddleja asiatica Lour and its fractions, chloroform (F1), ethyl acetate (F2) and nbutanol (F3) were evaluated for antibacterial, antifungal, antispasmodic and Ca++ antagonist activities. The antibacterial activity was performed against 11 types of bacteria. The crude extract and fractions F2 and F3 exhibited significant activity, while F1 showed low activity in killing the Shigella flexenari, Sternostoma boydi and Escherichia coli. In the rest bacteria, the crude extract and all the fractions (F1 to F3) revealed minimum to nil inhibitory effect. The fungicidal activity of the crude extract and all the fractions (F1 to F3) was also performed against six different fungi. The crude extract and fractions F1 and F3 displayed significant activity, while fraction F2 showed moderate activity against Fusarium solani. In the case of Microsporum canis, the crude extract and fraction F3 showed high activity but in the other four fungi, the inhibition area exhibited optimum to nil activity in crude extract and all the fractions (F1 to F3). In isolated rabbit jejunum preparations, B. asiatica crude extract caused concentration-dependent (0.03 to 1.0 mg/ml) relaxation of spontaneous and high K+ (80 mM)-induced contractions. The results indicate the antibacterial, antifungal, antispasmodic and Ca++ antagonist potential of B. asiatica Lour

Comparison of Histoacryl® plus Lipiodol® versus Histoacryl® plus vitamin D3 in the management of isolated fundal varices: A retrospective comparative study

Objective: According to recent guidelines Histoacryl® (N-butyl-2 -Cyanoacrylate) injection is the first line therapy for the endoscopic obliteration of gastric varices. Lipiodol is commonly used to facilitate injection Histoacryl® but it is expensive. In this study we compare Lipiodol with Vitamin D3 injection as priming agents for Histoacryl injection in terms of efficacy and safety in the management of isolated fundal varices. Methods: This is a retrospective comparative study conducted at Gastroenterology Unit, Lady Reading hospital Peshawar. Patients’ information was collected from March, 2012 to January, 2020 from medical records and statistically analyzed in terms of fundal varices obliteration, re-bleeding, mortality, and adverse events related to treatment. Results: From March, 2012 to January, 2020, 171 patients met the criteria. 7 cases lost follow up, and all the cases in both groups were treated successfully. There were no adverse events related to procedure in either group. Twenty six patients developed upper GI re-bleeding, which did not differ significantly betweenthe twogroups. There was also no difference between the groups in terms of treatment failure, complications, varices obliteration, and mortality. Conclusion: Vitamin D3 is as safe and effective as Lipoidol when used as priming agent for Histoacryl injection for obliteration of isolated fundal varices and can be used as a cheaper alternative to Lipoidol

Cloudlet computing : recent advances, taxonomy, and challenges

A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail. © 2013 IEEE

Effect of electrolyte (NaCl) and temperature on the mechanism of cetyl trimethylammonium bromide micelles

In the last few decades, surfactants and electrolyte interaction has gained considerable attention of researchers due to their industrial and domestic applications. In this work, the effects of electrolyte (NaCl) on the critical micelle concentration (CMC) of the cationic surfactant cetyltrymethyl ammonium bromide (CTAB) at different temperatures were investigated through different techniques such as conductometry, surface tensiometer and viscosimeter. The results showed that the values of CMC of CTAB decreased with the increase in temperature as well as with the addition of NaCl. The value of CMC for pure CTAB was calculated 0.98M at 303K, which was observed to decrease as temperature increased and got value of 0.95M at 318K. Moreover the addition of electrolyte NaCl into the surfactant lead to lowering of the CMC and obtained value of 0.90M at 3M of NaCl, indicating significant electrostatic interactions between surfactant and electrolyte. Moreover the degree of ionization(α) calculated for pure cationic surfactant CTAB was 0.219, which tends to increase with the addition of electrolyte, while that of counter ion binding values (β) was observed to decrease from 0.780 to 0.201. Furthermore, the conductivity of charged micelle of surfactant and free ions of electrolyte contributed to electric conductivity of aqueous micellar solution of surfactant. The results can be helpful to develop better understanding about interaction between electrolyte and surfactant

A novel driver emotion recognition system based on deep ensemble classification

Driver emotion classification is an important topic that can raise awareness of driving habits because many drivers are overconfident and unaware of their bad driving habits. Drivers will acquire insight into their poor driving behaviors and be better able to avoid future accidents if their behavior is automatically identified. In this paper, we use different models such as convolutional neural networks, recurrent neural networks, and multi-layer perceptron classification models to construct an ensemble convolutional neural network-based enhanced driver facial expression recognition model. First, the faces of the drivers are discovered using the faster region-based convolutional neural network (R-CNN) model, which can recognize faces in real-time and offline video reliably and effectively. The feature-fusing technique is utilized to integrate the features extracted from three CNN models, and the fused features are then used to train the suggested ensemble classification model. To increase the accuracy and efficiency of face detection, a new convolutional neural network block (InceptionV3) replaces the improved Faster R-CNN feature-learning block. To evaluate the proposed face detection and driver facial expression recognition (DFER) datasets, we achieved an accuracy of 98.01%, 99.53%, 99.27%, 96.81%, and 99.90% on the JAFFE, CK+, FER-2013, AffectNet, and custom-developed datasets, respectively. The custom-developed dataset has been recorded as the best among all under the simulation environment

Green Production and Structural Evaluation of Maize Starch–Fatty Acid Complexes Through High Speed Homogenization

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. The current study describes the production of maize starch–fatty acid complexes through high speed homogenization, a novel field of research, without heat or any chemical treatment. The starch–fatty acid complexes were produced with three different fatty acids, i.e. stearic acid (T1), palmitic acid (T2) and lauric acid (T3). The complexes were analyzed through various techniques. The results reveal that the complexing index (CI), swelling power (SP) and solubility (S) for T1 were significantly higher compared to T2 and T3. In X-ray diffraction (XRD) studies, relatively lower crystalline (V-type pattern) structures were obtained for the samples T1–T3, where T2 showed the highest crystallinity amongst all. Fourier transformed infrared (FTIR) spectra showed characteristic bands i.e., OH, C=O, C–O and long-chain CH2 functionalities thus confirming the overall incorporation of acids into glycoside moieties. The Scanning electron microscopy (SEM) analysis showed sub-crystalline matrix structures with fewer or no spherulites indicating the overall incorporation of acids in starch. The samples showed relatively low thermal stability in the thermal gravimetric analysis (TGA) in the range of 180 to 280 °C. These results suggest that high speed homogenization had the potential for the development of green and biocompatible maize starch–fatty acid complexes

A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning models for the detection and localization of the tumor in MRI. A novel two-tier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second tire. Furthermore, in this paper, we introduce a well-annotated dataset comprised of tumor and normal images. The experimental results demonstrate the effectiveness of the proposed framework by achieving 97% accuracy using GoogLeNet on the proposed dataset for classification and 83% for localization tasks after fine-tuning the pre-trained you only look once (YOLO) v3 model

Synthesis of new 2-{2,3-dihydro-1,4-benzodioxin-6- yl[(4-methylphenyl) sulfonyl]amino}-N-(un/substituted-phenyl) acetamides as α-glucosidase and acetylcholinesterase inhibitors and their in silico study

The aim of the present research work was to investigate the enzyme inhibitory potential of some new sulfonamides having benzodioxane and acetamide moieties. The synthesis was started by the reaction of N-2,3-dihydrobenzo[1,4]-dioxin-6-amine (1) with 4-methylbenzenesulfonyl chloride (2) in the presence of 10% aqueous Na2CO3 to yield N-(2,3-dihydrobenzo[1,4]-dioxin-6-yl)-4-methylbenzenesulfonamide (3), which was then reacted with 2-bromo-N-(un/substituted-phenyl)acetamides (6a-l) in DMF and lithium hydride as a base to afford various 2-{2,3-dihydro-1,4-benzodioxin-6-yl[(4-methylphenyl)sulfonyl] amino}-N-(un/substituted-phenyl)acetamides (7a-l). All the synthesized compounds were characterized by their IR and 1 H-NMR spectral data along with CHN analysis data. The enzyme inhibitory activities of these compounds were tested against -glucosidase and acetylcholinesterase (AChE). Most of the compounds exhibited substantial inhibitory activity against yeast -glucosidase and weak against AChE. The in silico molecular docking results were also consistent with in vitro enzyme inhibition data