The Effects of Crude and Pure Green Tea Polyphenols on Bacteria Biofilm Formation and Endospore Germination

Over the past couple of decades humans have been combating the problem of multi-drug resistant microorganisms. Multi-drug resistant strains o f microorganisms have been causing not only problems for medical settings, but the world as well. Because of the continuous use of antibiotics within our time and age, there has been a huge increase in antibiotic resistant bacteria. In addition to the rise in multi-drug resistant bacteria over time, endospore cells have been causing increased levels of food-bourne illnesses. Thus, there is a need for novel antimicrobial agents from nature derived products as an alternative way to treat and control these microorganisms. This specific study has been focused on the effects of green tea polyphenols (GTP) and a modified derivative, lipophilic tea polyphenol (LTP), Epigallocatechin Gallate (EGCG), and the modified derivative, Epigallocatechin Gallate - Stearate (EGCG-S) as antimicrobial agents against bio film formation and endospore germination. The selected bacteria for this study were the gram negative bacteria Pseudomonas aeruginosa and Pseudomonas fluorescens for the bio film study and the endospore forming bacteria Bacillus cereus, Bacillus megaterium, and Bacillus subtil is for the endospore study. For the bio film formation study, air-liquid interface assays, crystal violet assays, and Congo red assays were used to analyze the ability of the tested bacteria to form biofilm when treated with tea polyphenols. The results indicated that each of the tea polyphenols used in this research have strong inhibitory effects and LTP and EGCG-S had the strongest effects on inhibiting biofilm formation. Concentrations o f 50pg/mL-100pg/mL were enough to inhibit biofilm formation from both bacteria. The fluorescent and scanning electron microscopic observations suggested that tea polyphenols affect the cell surface integrity, which was shown by damaged bacteria cells when treated. For the endospore germination study, colony-forming unit (CFU) studies and time kill studies indicated that concentrations of LTP and EGCG-S at 1% and 5% treated for 15 minutes and up to 2 hours were both able to inhibit endospore germination up to 100%. Transmission electron microscope images suggested that the inhibitory effect of EGCG-S might be due to its ability to damage the endospore coat and cause agglutination of endospores. These results suggest the potential beneficial effects of using each tea polyphenol to work as a nature derived compound to inhibit biofilm formation and endospore germination. It can be concluded from these results that each of the polyphenols, GTP, LTP, EGCG, and EGCG-S, can be used as potential antimicrobial agents

An Invariant Dual-beam Snowflake Antenna for Future 5G Communications

A broadband snowflake antenna for future 5G and millimeter-wave communications is presented. The proposed antenna has a size of 8 × 5 mm 2 . The antenna consists of a central hexagon surrounded by a series of symmetrically placed smaller hexagons around it, resulting in broadband characteristics. The impedance bandwidth of the proposed antenna ranges from 25.284-29.252 GHz. The antenna has a gain of 3.12 dBi at 28 GHz and is more than 98% efficient. A distinct feature of the proposed antenna is its dual-beam radiation pattern. The two beams remain fixed at ±50° even if the frequency is varied with in its operating band. The proposed antenna is modelled on thin Rogers substrate which makes it very useful for future 5G smart phones

Lecturers’ Perspectives on using KeLiP as an E-Learning Tool

The aim of this paper is to investigate the use of an e-learning tool called KeLiP among lecturers in one of the public university in Malaysia, University Sultan Zainal Abidin (UniSZA). Specifically, this study is intended to achieve three main objectives: to examine the opinions of lecturers towards KeLiP, to investigate willingness to use KeLiP and to examine the challenges using KeLip. A total of 250 lecturers responded to the survey. Interestingly, the experiences on internet and computers as well as exposure on the use of KeLiP among lecturers were vast. The results on opinions revealed that KeLiP is cost effective and easy to understand. The results also indicate that the main challenge faced in using KeLip was related to technical problems and lecturers were willing to use KeLiP if appropriate support is given

ON PERFORMANCE OF QUANTUM LOCKING FOR WHEEL-LESS TRANSPORTATION

Transportation has continuously been a vital side of human civilization reflective the extent of general economic and technological advancement of a given society. The recent technological advancements in power electronics are pushing for Maglev train using Quantum locking. For levitation the most important factor is temperature which is affecting the efficiency of the train. In this work we have compared different prototypes of Maglev trains including Japanese, German and Chinese technologies. We also did mathematical modeling to relate the magnetic force required for a certain weight of the train to levitate.  We suggest that Japanese maglev is better than Germany and china maglev

Evaluating tools to support a new practical classification of diabetes: excellent control may represent misdiagnosis and omission from disease registers is associated with worse control.

To conduct a service evaluation of usability and utility on-line clinical audit tools developed as part of a UK Classification of Diabetes project to improve the categorisation and ultimately management of diabetes

Our Experience with Tension-Free Urethral Reconstruction with Urethral Plate Dorsal Slit Technique

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A study on the effects of environment on curing characteristics of thixotropic and room temperature cured epoxy-based adhesives using DMTA

This study investigated the thermal properties of three room temperature curing adhesives containing nano-particles which were thixotropic and shear thinning which allowed injection into overhead holes when exposed to different environmental conditions. Viscosity and shear stress of the adhesives were measured as a function of shear rate. The thermal behaviour of the adhesives were measured using dynamic mechanical thermal analysis following exposure to different temperatures and humidities which included temperatures of 20°C. 30°C and 50°C, relative humidities of 65% RH, 75% RH and 95% RH, soaked in water at 20°C and placed in the oven at 50°C. The dynamic thermal properties reported include storage and loss modulus, the loss tangent and the glass transition temperature (Tg). For nano- and micro-particles filled adhesives, the Tg increased with the temperature increase, even though the adhesives were subjected to high humidity and this was due to further cross-linking. The results showed that room temperature cured epoxies were only partially cured at room temperature

An Intelligent Healthcare system for detecting diabetes using machine learning algorithms

The human disease prediction is specifically a struggling piece of work for an accurate and on time treatment. Around the world, diabetes is a hazardous disease. It affects the various essential organs of the human body, for example, nerves, retinas, and eventually heart. By using models of machine learning algorithms, we can recommend and predict diabetes on various healthcare datasets more accurately with the assistance of an intelligent healthcare recommendation system. Not long ago, for the prediction of diabetes, numerous models and methods of machine learning have been introduced. But despite that, enormous multi-featured healthcare datasets cannot be handled by those systems appropriately. By using Machine Learning, an intelligent healthcare recommendation system is introduced for the prediction of diabetes. Ultimately, the model of machine learning is trained to predict this disease along with K-Fold Cross validation testing.  The evaluation of this intelligent and smart recommendation system is depending on datasets of diabetes and its execution is differentiated from the latest development of previous literatures. Our system accomplished 99.0% of efficiency with the shortest time of 12 Milliseconds, which is highly analyzed by the previous existing models of machine learning. Consequently, this recommendation system is superior for the prediction of diabetes than the previous ones. This system enhances the performance of automatic diagnosis of this disease. Code is available at (https://github.com/RaoHassanKaleem/Diebetes-Detection-using-Machine-Learning-Algorithms). &nbsp