Potential Role of Herbal Medicine in Alleviating Pain and Inflammation in Osteoarthritis: a Review

Osteoarthritis (OA) is a rheumatological disorder accompanied with imbalance between anabolic and catabolic mediators which leads to the destruction of homeostasis of articular cartilage. Currently, Steroids and non-steroidal anti-inflammatory drugs are commonly used in the management of OA. Besides the various side effects of these drugs, they can just alleviate symptoms of OA. Hence, to achieve safe and efficacious drugs, the research tendency toward exploration of novel sources has been grown up. Various previous researches have focused on the use of medicinal plants in the treatment of OA. This review focuses on the most efficacious medicinal plants and drugs considering related laboratory and clinical evidences. More investigations are needed to develop therapeutic agents with disease-modifying properties to treat OA

Tunability of terahertz random lasers with temperature based on superconducting materials

We theoretically demonstrate the tunabiltiy of terahertz random lasers composed of high temperature superconductorYBCO and ruby layers as active medium. The considered system is a one-dimensional disordered medium made of ruby grain and YBCO. Finite-difference time domain method is used to calculate the emission spectrum and spatial distribution of electric field at different temperatures. Our numerical results reveal that the superconductor based random lasers exhibit large temperature tunability in the terahertz domain. The emission spectrum is significantly temperature dependent, the number of lasing modes and their intensities increase with decreasing temperature. Also, we make some discussion to explain the reason for the observed tunability and the effect of temperature variation on the spatial distribution of the electric field in the disordered active medium

Comparison of predictive models for the early diagnosis of diabetes

Objectives: This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts. Methods: We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC). Results: The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively. Conclusions: The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes

Building the Bridge between Operations and Outcomes

__Abstract__ The PhD research has two objectives: - To develop generally applicable operational models which allow developing the evidence base for health service operations in provider networks. - To contribute to the evidence base by validating the model through application to health service networks for type 2 diabetes, stroke, and hip osteoarthritis

Concentration dependent tautomerism in green [Cu(HL1)(L2)] and brown [Cu(L1)(HL2)] with H2L1 = (E)-N’-(2-hydroxy-3-methoxybenzylidene)- benzoylhydrazone and HL2 = pyridine-4-carboxylic (isonicotinic) acid

The in situ formed hydrazone Schiff base ligand (E)-N’-(2-hydroxy-3-methoxybenzylidene)-benzoylhydrazone (H2L1) reacts with copper(II) acetate in ethanol in the presence of pyridine-4-carboxylic acid (isonicotinic acid, HL2) to green-[Cu(HL1)(L2)]・H2O・C2H5OH (1) and brown-[Cu(L1)(HL2)] (2) complexes which crystallize as concomitant tautomers where either the mono-anion (HL1)- or di-anion (L1)2- of the Schiff base and simultaneously the pyridine-carboxylate (L2)- or the acid (HL2) (both through the pyridine nitrogen atom) function as ligands. The square-planar molecular copper(II) complexes differ in only a localized proton position either on the amide nitrogen of the hydrazone Schiff base in 1 or on the carboxyl group of the isonicotin ligand in 2. The proportion of the tautomeric forms in the crystalline solid-state can be controlled over a wide range from 1:2 = 95 : 5 to ~2 : 98 by increasing the solution concentration. UV/Vis spectral studies show both tautomers to be kinetically stable (inert), that is, with no apparent tautomerization, in acetonitrile solution. The UB3LYP/6-31+G* level optimized structures of the two complexes are in close agreement with experimental findings. The solid-state structures feature 1D hydrogen-bonded chain from charge-assisted O(-) … H–N and O–H … (-)N hydrogen bonding in 1 and 2, respectively. In 1 pyridine-4-carboxylate also assumes a metal-bridging action by coordinating a weakly bound carboxylate group as a fifth ligand to a Cu axial site. Neighboring chains in 1 and 2 are connected by strong π-stacking interactions involving also the five- and six-membered, presumably metalloaromatic Cu-chelate rings

Naive Bayes classier-based fire detection using smartphone sensors

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014For many years, smoke detectors have been used as the most crucial _re detection sensors.Although smoke detectors do their job very well, they are not perfect and may causefalse or late alarms. This is because they only rely on one of the _re signs which is smoke.Fire has many other signs as well such as heat and light. It also a_ects its environmentalparameters such as temperature and humidity. But typically, buildings are not equippedwith sensors capable of sensing these changes. Recently, a few smartphone manufacturershave added temperature, humidity, and barometer sensors to their products which can beused for more reliable _re detection. In this thesis, a framework composed of one or moresmartphones and a back-end server is proposed which can detect and visualize indoor_re. For this purpose, the smartphones continuously collect, preprocess, and analyzedata from their sensors to detect if _re exists in their surroundings. The back-endserver facilitates the analysis processes in smartphones and provides crisis managementinstitutions such as police, _re department, and ambulance with real-time monitoringuser interface so that they can easily grasp useful information about the _re's locationand scale. The proposed _re detection framework is a learning system which needs tobe trained by real data. Therefore, a wide range of experiments is precisely designedand performed to make sure that the system can immediately and accurately detect _rein diverse environmental conditions.Keywords: Naive Bayes Classi_er, Smartphone, Sensor, Fire Detection