Design and Develop Bilayered Oral Sustained Matrix Tablets of Pioglitazone Hydrochloride and Metformin Hydrochloride

AIM AND OBJECTIVES: The aim of this investigation is to Design and Develop Bilayered oral sustained matrix tablets of Pioglitazone hydrochloride and Metformin hydrochloride. The concept of Bilayered tablet technology is utilized for stabilization of two incompatible drugs, taste masking of drugs, delivering two drugs having synergistic effects or to deliver a drug for biphasic drug release profile and for the purpose of extension of patents. A Bilayered tablet comprises of two layers among which the first layer is immediate release layer for sudden onset of action and the second layer is Sustained release layer to maintain the steady state concentrations of drug in the blood. Pioglitazone HCl is thiazolidinedione (TZD) class of drug with hypoglycemic, antihyperglycemic and antidiabetic action. Chemically Pioglitazone is (RS)-5-(4-[2-(5- ethylpyridin-2-yl) ethoxy] benzyl) thiazolidine-2, 4-dione. Pioglitazone is used for the treatment of diabetes mellitus type 2 (previously known as non-insulin-dependent diabetes mellitus, NIDDM) in monotherapy and in combination with a sulfonylurea, Metformin. Pioglitazone has also been used to treat non-alcoholic fatty liver. Pioglitazone has also been found to reduce the risk of conversion from prediabetes to diabetes mellitus type 2 by 72%. It has short biological half life of 3-5 hrs (Ramesh et al). Metformin HCl is a biguanide oral anti hyperglycemic (anti diabetic) agent. It is used as an adjunct to diet and exercise for the management of type 2(non-insulindependent diabetes mellitus) diabetes mellitus in patients whose hyperglycemia cannot be controlled by diet alone. As Metformin HCl possess short biological half life (1.5-4.5 hrs), patient should go for frequent administration usually twice or thrice a day which might be a risk to the patient. In order to overcome this Metformin HCl sustained release dosage forms are formulated (Ramesh et al). SUMMARY AND CONCLUSION: In the present investigation, Sustained release Bilayered tablets of Pioglitazone HCl and Metformin HCl were formulated by Direct Compression technique and Wet Granulation technique. Bilayered tablets comprise of IR for sudden onset of action formulated with Crospovidone and SR layer formulated with Polyethylene oxide (PEO- 303) and Carbopol 971 P inorder to sustain the drug release. Drug-excepient compatibility were studied by FT-IR spectral analysis, the results revealed that there were no interactions between drug and excepients in this investigation for the development of the Bilayered tablet formulation. The Precompressional parameters for IR, SR layer formulations ie; Angle of repose, Bulk density, Tapped density, Compressibility index, Hausner’s ratio were studied and found to be in satisfactory limits indicating that the Physical mixtures of the formulations are suitable to formulate the Bilayered tablets. Postcompressional parameters for Bilayered tablets ie; Weight variation, Hardness, Friability, Drug content, were evaluated and the results obtained were satisfactory. The in-vitro drug dissolution studies were carried out for the formulations in pH 1.2 and pH 6.8 phosphate buffer for 2hrs and 10hrs respectively and based on the in-vitro drug release profile IR layer formulation (F3) was optimized for the further development of Bilayered tablets. The formulation F8 comprising of PEO-303 and the formulation F13 comprising of CARBOPOL 971P sustained the drug release for a period of 12 hrs. Dissolution profile of formulations F8 and F13 were compared with the dissolution profile of marketed formulation and Similarity factor for the formulations F8 and F13 was found to be 51.41 and 51.21 respectively.The similarity factor (f2) was also calculated in order to compare optimized formulation (F8 and F13) with that of the reference formulations. Comparission of the profiles indicated that the formulations (F8and F13) had a profile similar to the reference formulation (f2 = 51.41and 51.21) respectively. So these two formulations were comparable with the marketed formulation. The conclusions drawn from the results include: Pioglitazone HCl and Metformin HCL and the excepients selected for this investigation were compatible and it was confirmed by FT-IR studies. Precompressional and Postcompressional parameters were found to be within the satisfactory limits and hence suitable to formulate Bilayered tablets. The order of cumulative % drug release from IR layer formulations was found to be F3>F2>F1. The IR layer formulation ie; F3 was optimized because it released the maximum amount of the drug. The results of in-vitro drug release profile of Bilayered tablets depicts that increase in polymer concentration, increases the retardation of drug release from the SR layer of a Bilayered tablet. The desired drug release rate obtained for F8 and F13 was found to be near to that of the theoretical desired drug release rate. The desired drug release rate obtained for F8 and F13 was found to be near to that of the drug release rate of Marketed formulation. The formulations F8 and F13 were suitable to sustain the drug release for a period of 12hrs, followed first order kinetics exhibited Higuchi’s model and Krosmeyer Peppas exponential coefficient ‘n’ < 0.5 indicates that the release was governed by Fickian diffusion. Hence can conclude that formulated Bilayered tablets of Pioglitazone HCl and Metformin HCl were developed successfully with IR layer comprising of Crospovidone and SR layer comprising of PEO-303 and CARBOPOL 971P as polymers by Direct Compression technique and Wet Granulation technique. From the above results it can be concluded that by using PEO-303 and CARBOPOL 971P we can successfully formulate Bilayer tablets of Pioglitazone HCl and Metformin HCl which showed sustained drug release up to 12hours

Chemical synthesis of the organoarsenical antibiotic arsinothricin

We report two routes of chemical synthesis of arsinothricin (AST), the novel organoarsenical antibiotic. One is by condensation of the 2-chloroethyl(methyl)arsinic acid with acetamidomalonate, and the second involves reduction of the N-acetyl protected derivative of hydroxyarsinothricin (AST-OH) and subsequent methylation of a trivalent arsenic intermediate with methyl iodide. The enzyme AST N-acetyltransferase (ArsN1) was utilized to purify l-AST from racemic AST. This chemical synthesis provides a source of this novel antibiotic for future drug development

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence

Background: The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed.Purpose: To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems.Materials and Methods: The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted kappa values, and classification accuracy.Results: A total of 105 patients (mean age, 62 years +/- 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years +/- 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted k values of 0.60 +/- 0.01 for CO-RADS scores and 0.54 +/- 0.01 for CT severity scores.Conclusion: With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. (C) RSNA, 2020Cardiovascular Aspects of Radiolog

Solvency Analysis of Non Banking Financial Companies in Tamilnadu

International audienceIn recent times, non-banking financial companies (NBFCs) have emerged as substantial contributors to the Indian economic growth by supplementing the efforts of bank and other development financial institutions. NBFCs play a key role in the direction of savings and investments. On the basis of their legal status and their principal activities, the NBFCs have been classified as Loan Company, Hire purchase finance company, Equipment leasing company, Investment Company and Residuary non-banking company. The scope of the NBFCs is fast growing with the multiplication of financial services. Some of the NBFCs are also engaging in underwriting through subsidiary units, and by offering allied financial services including stock broking, investment banking, asset management and portfolio management. In this paper discussed to appraise the solvency of selected NBFCs. Solvency is a vital indicator of economic performance of an economic system. In fact, it is a mechanism for improving the material quality of life. Solvency is fundamental to progress throughout the world. It is at the heart of economic growth and development, improvements in standards of living and quality of life. In this paper mainly focused on branch productivity and employee productivity of selected NBFCs

Composite intelligent à base de fibre de carbone et matrice époxy pour les pales d’éoliennes offshores. Modélisations numérique et analytique en multi-échelles

Smart structures have been developed as to monitor structures that have to operate in demanding industrial applications with includes harsh environments (Aeronautics and aerospace, Civil engineering, nuclear and chemical power plants…), too. Current study is focused on the suggestion of new smart composite materials that can be successfully used for wind blade structures in offshore energy generation farms. Indeed, to bring expectable energy-generation performances, new generation wind blades have to exceed 100m length, which is a hardly achievable target given that actual constitutive composite materials are based on glass-fibers, that are notably known to be very heavy and lacking stiffness. Therefore, the switch to carbon fibers (lighter and stiffer) becomes mandatory. In this thesis, we propose the implementation of a smart composite material that is based on carbon fibers and epoxy matrix (here called parent material). Fiber Optic Sensors (FOS) and Quantum-Resistive Sensors (QRS) will be used for detection of over-strained areas all over the structure. This choice is expected to enable for accurate documentation and instant sending of critical information to engineers. To achieve this goal of development of a new smart material for a critical application in offshore wind generation, we have chosen to illustrate it in a research document that is grouping several aspects, summarized in 5 chapters. The thesis is conducted using numerical and analytical modelings. The document is not having the ambition to be exhaustive. It is intended to present a pragmatic research that emphasize how areas of mechanical weakness can be diagnosed, what are the solutions that can be suggested and how we can support them, what are the issues pertaining to the use of embedded sensors and some experimental results that give appraisal of current performance status and what could be future trends.Les structures intelligentes fondées sur des matériaux composites ont été développées pour surveiller les structures qui doivent fonctionner dans des applications industrielles exigeantes, dans des environnements difficiles comme c’est le cas de l’aéronautique, de l’aérospatiale, du génie civil, des centrales nucléaires et chimiques ...). L'étude actuelle est axée sur la suggestion d’un nouveau matériau composite intelligent qui peut être utilisé avec succès dans les pâles d’éoliennes offshore de nouvelle génération. En effet, pour accentuer leur rendement, les pales de nouvelle génération doivent dépasser une longueur de 100m, ce qui représente actuellement une cible hors d’atteinte étant donné que les matériaux composites constitutifs sont fondés sur des fibres de verre, notamment connues pour être lourdes et dépourvues de rigidité significative. Par conséquent, le passage aux fibres de carbone (plus légères et 3 fois plus rigides) devient obligatoire. Dans cette thèse, nous proposons la mise en place d'un matériau composite intelligent à base de fibres de carbone et de matrice époxy (ici appeler matériau parent). Les capteurs à fibre optique (FOS) et les capteurs à résistance quantique (QRS) seront utilisés pour la détection de déformation dans toute la structure. Ce choix devrait permettre une documentation précise et un envoi instantané d'informations critiques aux ingénieurs. Pour atteindre cet objectif de développement d'un nouveau matériau intelligent pour une application critique dans la production d’énergie éolienne offshore, nous avons choisi de proposer un document de recherche regroupant plusieurs aspects du sujet, résumés en 5 chapitres. La thèse est fondée sur des modélisations numériques et analytiques. Le document n'a pas l'ambition d'être exhaustif. Il est destiné à présenter une recherche pragmatique qui met l'accent sur la façon dont les domaines de faiblesse mécanique peuvent être diagnostiqués, quelles sont les solutions qui peuvent être suggérées et comment nous pouvons les soutenir, quelles sont les questions relatives à l'utilisation de capteurs intégrés et les résultats expérimentaux qui permettent l'évaluation du statut actuel de la performance du matériau et les moyens d’en améliorer les performances

H-DOCTOR: Honeypot based firewall tuning for attack prevention

A honeypot is a well-known entrapment technique used by network and Internet of Things (IoT) security professionals to lure intruders. Unlike traditional security measures, they can capture information in real time from the attacker about how they are attacking. A network firewall protects Internet servers from unwanted and malicious traffic. Detecting ransomware with existing security systems such as IDPS (Intrusion Detection and Protection System) and AV (Antivirus) is difficult and time-consuming. In this paper, a novel hybrid Honeynet deployed in Docker for detecting attacker behavior with Tuning Of fiRewall (H-DOCTOR) has been proposed. The proposed H-DOCTOR technique comprises both low interaction and high interaction honeypot to attract the malicious attacker and to analyze the behavioral patterns. This is a form of bait, designed to detect or block attacks, or to divert an attacker's attention away from the legitimate services and tune the firewall. The proposed H-DOCTOR method identify ransomware activity, attack trends, and timely decision-making through the use of an effective rule and tunes the firewall. The proposed H-DOCTOR framework is compared with existing methods such as HyInt,IDS and honeypot-based IDS. The proposed system achieves higher accuracy of 86% and the existing system such as HyInt,IDS and honeypot-based IDS achieves 73.25%, 76.75% and 81.25%