Development and Validation of Stability Indicating Reverse Phase High Performance Liquid Chromatographic Method for estimation of Donepezil HCl from bulk drug

Stability of Donepezil Hydrochloride(DONE) was investigated using stability indicating Reverse phase high performance liquid chromatography (RP-HPLC) utilizing C-18 column and mobile phase containing Acetonitrile:Water (pH 3.5)  in ratio of 40:60 at flow rate of 1 ml min-1. Peaks of donepezil and degradation products were well resolved at retention times < 7 min. Stability was performed in 0.1N hydrochloric acid, 0.1N sodium hydroxide, 3 % hydrogen peroxide, neutral, photolytic and dry heat conditions. Fast hydrolysis was seen in alkaline condition as compared to oxidative and neutral conditions. Methods was validated with respect to linearity, precision, accuracy, specificity and robustness LOQ and LOD. It was also found to be stability indicating, and therefore suitable for the routine analysis of Donepezil hydrochloride in the pharmaceutical formulation

Genetic Discovery and Risk Characterization in Type 2 Diabetes across Diverse Populations

Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in th

Tight Time-Space Lower Bounds for Finding Multiple Collision Pairs and Their Applications

We consider a collision search problem (CSP), where given a parameter $C$, the goal is to find $C$ collision pairs in a random function $f:[N] \rightarrow [N]$ (where $[N] = \{0,1,\ldots,N-1\})$ using $S$ bits of memory. Algorithms for CSP have numerous cryptanalytic applications such as space-efficient attacks on double and triple encryption. The best known algorithm for CSP is parallel collision search (PCS) published by van Oorschot and Wiener, which achieves the time-space tradeoff $T^2 \cdot S = \tilde{O}(C^2 \cdot N)$ for $S = \tilde{O}(C)$. In this paper, we prove that any algorithm for CSP satisfies $T^2 \cdot S = \tilde{\Omega}(C^2 \cdot N)$ for $S = \tilde{O}(C)$, hence the best known time-space tradeoff is optimal (up to poly-logarithmic factors in $N$). On the other hand, we give strong evidence that proving similar unconditional time-space tradeoff lower bounds on CSP applications (such as breaking double and triple encryption) may be very difficult, and would imply a breakthrough in complexity theory. Hence, we propose a new restricted model of computation and prove that under this model, the best known time-space tradeoff attack on double encryption is optimal

On the distribution of the divisor function and Hecke eigenvalues

© 2016, Hebrew University of Jerusalem. We investigate the behavior of the divisor function in both short intervals and in arithmetic progressions. The latter problem was recently studied by É. Fouvry, S. Ganguly, E. Kowalski and Ph. Michel. We prove a complementary result to their main theorem. We also show that in short intervals of certain lengths the divisor function has a Gaussian limiting distribution. The analogous problems for Hecke eigenvalues are also considered

AB1047 Towards the Design of a Decision Support Tool for Precise Care for Arthritis

BackgroundDecision Support requires the ability to classify individuals into subpopulations that differ in their susceptibility to diseases or their response to a specific treatment. Preventive or therapeutic interventions can then be focused on those who will benefit, sparing expense and side effects for those who will not. Thus, it is the tailoring of medical treatment to the individual characteristics of each patient and their susceptibility to various chronic diseases.ObjectivesBig Data analytics will empower physicians at the point of care to diagnose early arthritis stages, choose treatment approaches, decide when to refer to a subspecialist, and mitigate co-morbidities.Co-morbidity refers to co-occurrence of more than one disease in a person at a time. Examples include Diabetes, Cardiovascular diseases, renal diseases, Arthritis, etc. These diseases can occur by chance or there can be complex pathological associations. These indirect causal factors are only partially understood. It has been observed that the number of hospital admissions, as well as the mortality rate of comorbid patients, is significantly high. Hence, there is a need for early detection of these diseases. The aim of this project is to develop a clinical decision support system to study the clinical and genomic factors responsible for causing these diseases. Based on these findings, educate clinicians about how certain clinical and genomic factors are responsible for causing these diseases.MethodsMost genetic variations among people is a result of single nucleotide polymorphisms (SNPs), which are differences in a single nucleotide within a stretch of DNA. SNPs can result in the production of different RNA molecules and proteins, thus altering the body's metabolism and physiology. With approximately 10 million SNPs in the human genome, “big data” analytical methods are the most efficient means for discovering which SNPs are associated with a particular disease. Candidate gene studies and genome-wide association studies (GWAS) serve a similar purpose on a much smaller scale, but are infeasible for analyzing large amounts of data.ResultsDesign and Methodology: a.From a large EMR database extract records of persons with arthritis.b.Obtain information about SNP known to be risk causing from SNPedia, dbSNP.c.Integrate clinical and genomic data to obtain a universal feature vector.d.Perform feature extraction to extract relevant attributes.e.Run data mining algorithms like simple k-means to obtain clusters of patients and study similarity between them.The application systems interconnection logic is depicted in the diagram.ConclusionsThe proposed framework will enable a decision support tool for precision medicine in treatment of persons with arthritis.AcknowledgementsThis research has been sponsored by the U.S. Arthritis Foundation.Disclosure of InterestNone declare