Indapamide is an effective and safe antihypertensive medication showing a beneficial effect in combination with other antihypertensive agents regarding morbidity and mortality. A comparative study was performed under fasting and fed conditions to investigate the effect of food and selected single nucleotide polymorphisms in the uridine diphosphate glucuronyl transferase (UGT2B7) gene on the pharmacokinetics and pharmacodynamics behavior of indapamide 1.5 mg sustained release. Forty-nine healthy volunteers aged 18–55 years were randomized into two groups; 25 volunteers were administered indapamide under fasting conditions and 24 under fed conditions. Genotyping of the UGT2B7 rs7438135 and rs11740316 was done before commencing the study using predesigned TaqMan assays. Results showed that food independently decreased the value of indapamide’ Tmax by 5.5 h and increased the value of Cmax by 8.7 ng/mL. On the other hand, all genetic variants of both UGT2B7 SNPs had no significant impact on the values of Tmax, Cmax, and AUC0–t; however, it was found that rs11740316 variant AG was correlated with a 2.8 h lower MRTinf. Finally, BMI positively correlated with longer MRTinf. It was concluded that none of rs7438135, rs11740316, or food had a significant impact on the pharmacodynamic properties. Food had a modest impact on indapamide Cmax and Tmax values, while there were unremarkable differences in safety and efficacy.

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The MapReduce programming model introduced by Google is one of the most successful efforts to cope with the growth of demand for processing large amount of data in large-scale clusters. Although MapReduce programming paradigm has never been easier or more scalable, distributed platforms have changed drastically in recent years. These days, most of the data centers and clusters are equipped with new processing elements such as Multi-Core CPUs, SIMD accelerators particularly, and FPGAs. Unfortunately, current MapReduce frameworks are incapable in harnessing the computational power of these available nodes. In this paper, we propose a new design philosophy to implement MapReduce frameworks in order to comply with above-mentioned multi-level parallelism that exists in modern data centers. We designed a novel architecture to leverage all types of SIMD architectures in distributed platforms. Experiments and evaluations show our novel implementation not only complies with the characteristics of MapReduce applications but also outperforms Hadoop in terms of speedup and throughput.