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Objective: Method development, validation & stability indicating studies for simultaneous estimation of Anti-Hypertensive drugs, Benidepine (BEN) and Metoprolol (MET) from pharmaceutical formulation by RP-HPLC. Methods: For present work, reverse phase chromatography was selected as its suggested use for ionic and moderate to non-polar compounds. Reverse phase chromatography is simple, suitable, better regarding efficiency, stability, and reproducibility. C18 packed column, a 100 X 2.1mm. ID column of 5.0 μm particle packing, was selected for separation of BEN and MET. Different solvent systems were tried and optimized in combinations as mobile phase. BEN (4 μg/ml) and MET (50 μg/ml) in 15mM ammonium formate-Methanol (15:85 v/v) was developed as it was showing good peak shapes and a significant amount of resolution. The mobile phase was flowed at 1.2 ml/min with detection of BEN analytes at 236 nm and MET analytes at 225 nm respectively. Result: Method development was done. Specificity, linearity, accuracy, precision, robustness, limit of detection and limit of quantitation were used to accomplish validation. The method was found linear from 32.5 – 500 µg.ml-1 for both BEN and MET individually. The percentage recovery of BEN when placed for period of 12 hours was found to 100% in 0.1N/M NaOH at 60˚C and Thermal (60˚C); 12 % degradation in 0.1N/M HCl at 60˚C; Oxidation (3-6% H2O2) at room temperature whereas for MET was 100 % in 0.1N/M NaOH, 0.1N/M HCl at 60˚C, at thermal (60˚C) as well as oxidation by 3-6% H2O2 at room temperature. Conclusion: Developed analytical method for the simultaneous estimation of Benidipine (BED) and Metoprolol (MET) in both bulk and tablet formulation has obliged the ICH guidelines including, tailing factor (T), separation factors (α), theoretical plates (N), capacity factor (k’), resolution (R) and RSD (%). The validated stress degradation studies under thermal, oxidative, alkali and acid ascertained few degradation products for Benidipine whereas the Metoprolol was unaffected with forced degradation studies. Keywords: Benidipine, Metoprolol, Reverse-Phase High Performance Liquid Chromatography, Stability indicating method.

This short paper describes a web resource-the NIST CORD-19 Web Resource-for community explorations of the COVID-19 Open Research Dataset (CORD-19). The tools for exploration in the web resource make use of the NIST-developed Root- and Rule-based method, which exploits underlying linguistic structures to create terms that represent phrases in a corpus. The method allows for auto-suggesting-related terms to discover terms to refine the search of a COVID-19 heterogenous document base. The method also produces taxonomic structures in the target domain as well as providing semantic information about the relationships between terms. This term structure can serve as a basis for creating topic modeling and trend analysis tools. In this paper, we describe use of a novel search engine to demonstrate some of the capabilities above.

Diabetes mellitus has become a global threat, especially in the emerging economies. In the United States, there are about 24 million people with diabetes mellitus. Diabetes represents a trove of physiologic and sociologic data that are only superficially understood by the health care system. Artificial intelligence can address many problems posed by the prevalence of diabetes mellitus and the impact of diabetes on individual and societal health. We provide a brief overview of artificial intelligence and discuss case studies that illustrate how artificial intelligence can enhance diabetes care.

The omega-3 fatty acid eicosapentaenoic acid has an important role in human health. The Reduction of Cardiovascular Events with Icosapent Ethyl–Intervention Trial (REDUCE-IT) examined the prescription omega-3 fatty acid icosapent ethyl (IPE) in patients with established cardiovascular disease (CVD) or with diabetes plus additional CVD risk factors. The trial found a large reduction in CVD events, including significant reductions in CVD death, myocardial infarction, stroke, coronary revascularization, and hospitalization for unstable angina. These results led to the regulatory approval of IPE in a population similar to REDUCE-IT participants in the United States, Canada, United Kingdom, and the European Union. Moreover, multiple international guidelines have endorsed the use of IPE in such individuals. A secondary analysis of REDUCE-IT examined the endpoint of coronary artery revascularization. This analysis showed a significant reduction not only in coronary revascularization overall but also in elective, urgent, and emergent coronary revascularization. Additionally, IPE significantly reduced the need for both percutaneous coronary intervention and for coronary artery bypass graft surgery. Coronary imaging studies have demonstrated significant decreases in rates of plaque progression with IPE, with significant effects within 6–9 months. In parallel, experimental findings corroborate several effects of IPE that provide mechanisms that could contribute to the profound reductions in multiple types of ischemic events, including percutaneous and surgical coronary revascularization. Future trials should explore potential benefits of initiation of IPE at the time of revascularization in broader populations, potentially in conjunction with loading doses.

Background Domestic solar PV installations in India are yet to become a valuable proposition for both the prosumers and utility because of the deficiencies in the formulation of the policy parameters. This paper presents a comprehensive analysis of the consumer-centric business model for rooftop solar PV installations in India. We explore areas where potential policy interventions may be introduced to improve collective stakeholder benefits and incentivize more domestic consumers to install rooftop solar panels in their premises. We propose a policy framework that seeks optimal Feed-in Tariff (FiT) rates, PV capacities and Average Billing Rates (ABRs) towards maximizing stakeholder benefits. The stakeholders considered are the consumers/prosumers and the utility. Results Case studies with three residential prosumers of different demand and generation profiles (extracted from data provided by Indian utilities) are presented. A multi-objective problem is formulated with the FiT, generation capacity (as a function of demand) and ABR as decision variables, exploring the various welfare trade-offs. The pareto-optimal front is identified for prosumer and utility benefits and suitable points with reasonable tradeoff are selected based on sensitivity analysis of the impact of the decision variables on collective welfare. Conclusions The paper provides a workflow to fix tariff, FiT and local PV capacities in active residential distribution systems. The suitability of prevailing tariff and FiT rates of three Indian utilities namely, MSEDCL, TATA POWER (Delhi) and TANGEDCO are studied, and their impact on prosumer savings and utility profits is brought out. The paper recommends optimal installation capacities for prosumers based on their load demand so as to encourage the adoption of roof-top solar without affecting collective benefits. This provides policymakers and prosumers an effective decision-making tool.

Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs.

Sclerotinia sclerotiorum causing white mold disease, Fusarium solani and Rhizoctonia solani causing root rot/wilt complex and Sclerotium rolfsii causing collar rot are economically important soil-borne pathogens of various crops having wide host range. Eight different botanical extracts were evaluated for their antifungal effects against the four above-mentioned fungi under laboratory conditions using food poison technique. Five percent crude extracts of garlic clove (Allium sativum), rhizome of bojo (Acorus calamus), leaves of castor (Ricinus communis), asuro (Justicia adhatoda), sambucus (Sambucus hookeri), neem (Azadirachta indica), bougainvillea (Bougainvillea glabra), and dried fruits of timur/Sichuan pepper (Zanthoxylum armatum) were included in the experiment. Three PDA plates for each treatment were inoculated with each pathogen and incubated at room temperature (24-25°C). Efficacy of the treatments on inhibition of radial growth of the pathogen colony was assessed 96 hours (4 days) after inoculation and compared with control without botanical extract. Significant inhibitory effect on radial growth of S. sclerotiorum was found with garlic (70% inhibition) followed by sambucus (22%) and asuro (12%). It was also observed that asuro treated plates had least sclerotia formation of the pathogen followed by sambucus. Bojo was found effective in inhibiting the radial growth of R. solani by 14% and S. rolfsii by 54% whereas, castor extract was found effective against F. solani with 21% inhibition in colony growth. Hence, these botanicals could have the potential in managing the diseases incited by the test pathogens, however, firstly their efficacy in the field conditions needs to be verified.

In this paper we analyze elements of the Unified Modeling Language (UML), specifically the class diagram, and propose an simplified alternative language based on a branch of mathematics called category theory (CT). We argue that modeling in CT provides a richer and more precise formal structure than UML, while still supporting many of the intuitive and diagrammatic features which engineers appreciate in UML. Deep ties connect CT to formal logic, physics and computer science, helping to connect our CT-based models to simulation and implementation. We proceed by walking through the constructs of the class diagram and showing how the same information can be captured using CT constructions. As we do, we see that some UML constructs are redundant while others smuggle in assumptions which ought to be made explicit. Thus we argue that the use of CT in information modeling can make our models less ambiguous, more precise and more formal.