A NOVEL VALIDATED UHPLC METHOD FOR ESTIMATION OF ASSAY AND ITS RELATED SUBSTANCES OF TRICHOSTATIN-A

Objective: The main objective of the research work is to develop and validate a rapid UHPLC method for the estimation of assay and its related substances of Trichostatin A (TSA) in pharmaceutical samples. Methods: The UHPLC method developed for chromatographic separation between TSA and its related compounds on Poroshell 120 SB C18(50×4.6) mm; 2.7 µm RRLC column using Agilent RRLC (UHPLC) system with linear gradient elution. Results: The developed UHPLC method has shown excellent chromatographic separation between TSA and its related compounds within 12 min run time, during validation experiments, specificity study revealed that the peak threshold was more than the peak purity and no purity flag was observed. Repeatability, intra, and inter-day precision results were well within the tolerable limits. Limits of detection concentrations were found between 0.075 to 0.077 ppm and the limit of quantitation is between 0.252 to 0.258 ppm for related compounds and TSA. The related substances method recoveries were found between 80 and 120 % and assay method recovery was found between 98.0 to 102.0%. Conclusion: The developed method capability was proven for the assay of TSA and its related compounds in pharmaceutical samples and the method shows eco-friendlier than routine, conventional HPLC methods in terms of analysis time, cost and HPLC effluent waste

Application of Analytic Hierarchy Process in Engineering Education

Analytic Hierarchy Process (AHP) provides a mathematical technique to formulate a problem as a hierarchical structure and believes in an amalgamation of quantitative and qualitative criteria. It is this uniqueness of AHP that makes it one of the important inclusive systems, considered to make decisions with multiple criteria. This paper focuses on conducting Analytic Hierarchy Process, based on the data collected from several Engineering colleges in the state of Telangana. This paper aims to understand the reasons for removing the staple Engineering streams such as Mechanical engineering, Production engineering, Electronics and Instrumentation engineering and introducing new and contemporary streams such as Artificial Intelligence and Data Science, Artificial Intelligence and Machine Learning and Internet of Things. The World Economic Forum’s latest “Future of Jobs” report highlights the impact of ‘double disruption’ of Automation, followed by COVID-19. The report indicates that while 85 million jobs will be displaced, 47% of core skills will change by 2025. The topic thus is of immense value since it looks closely at the paradigm shift mentioned above and its further consequences. The result of the present study would be helpful to indicate the exact rankings of the programming and non-programming branches in the engineering field and thus would be instrumental in gauging learners’ inclination towards studying specific branches. This paper aims to analyze the growing demand of programming branches over traditional, non-programming branches.

A novel validated eco-friendly RP-UHPLC method for assay and related substances in Meropenem

1148-1157A simple, rapid, sensitive, specific, eco-friendly and stability-indicating linear gradient liquid chromatographic method (RP-UHPLC) for simultaneous estimation of assay and its related compounds in Meropenem API samples is developed and validated. Chromatographic separation was achieved on Zorbax Eclipse plus C18, (100 x 4.6) mm, 3.5 µm RRLC short column and 10 mM potassium dihydrogen orthophosphate is used as buffer, buffer solution used as eluent A and buffer and acetonitrile combination 30: 70 v/v ratio used as eluent B and Agilent RRLC (UHPLC) system is used for analysis. The mobile phase flow rate was 1.0 ml/min, and the eluted compounds have been monitored at 220 nm for related substance method and 290 nm for assay method. Excellent resolution is obtained between Meropenem and its related compounds which were eluted within 10 min. The correlation co-efficient(r) is > 0.995 for both the methods from linearity data and percentage of recovery is 98.0 to 102.0 and 80.0 to 120.0 % for assay method and for related substance method, respectively. Sensitivity of the method is found to be less than 0.316 µg/ml. Peak homogeneity data for Meropenem in the chromatograms from the stressed samples are obtained by using photodiode array detector demonstrated the specificity of the method for analysis of Meropenem in presence of the degradation compounds. The performance of the method is validated according to the present ICH guidelines for specificity, limit of detection, limit of quantification, linearity, accuracy, precision, and robustness

A Novel Validated Eco-friendly RP-UHPLC Method for Assay and Related Substances in Meropenem

A simple, rapid, sensitive, specific, eco-friendly and stability-indicating linear gradient liquid chromatographic method (RP-UHPLC) for simultaneous estimation of assay and its related compounds in Meropenem API samples is developed and validated. Chromatographic separation was achieved on Zorbax Eclipse plus C18, (100 x 4.6) mm, 3.5 µm RRLC short column and 10 mM potassium dihydrogen orthophosphate is used as buffer, buffer solution used as eluent A and buffer and acetonitrile combination 30: 70 v/v ratio used as eluent B and Agilent RRLC (UHPLC) system is used for analysis. The mobile phase flow rate was 1.0 ml/min, and the eluted compounds were monitored at 220 nm for related substance method and 290 nm for assay method. Excellent resolution was obtained between Meropenem and its related compounds which were eluted within 10 min. The correlation co-efficient(r) is > 0.995 for both the methods from linearity data and percentage of recovery is 98.0 to 102.0 and 80.0 to 120.0 % for assay method and for related substance method respectively. Sensitivity of the method is found to be less than 0.316 µg/ml. Peak homogeneity data for Meropenem in the chromatograms from the stressed samples were obtained by using photodiode array detector demonstrated the specificity of the method for analysis of Meropenem in presence of the degradation compounds. The performance of the method was validated according to the present ICH guidelines for specificity, limit of detection, limit of quantification, linearity, accuracy, precision, and robustness

Machine learning for the Zwicky transient facility

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages