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Background: Contrast induced nephropathy (CIN) is associated with significant morbidity and mortality after percutaneous coronary intervention (PCI). The aim of this study is to evaluate the collective probability of CIN in Indian population by developing a scoring system of several identified risk factors in patients undergoing PCI. Methods: This is a prospective single center study of 1200 consecutive patients who underwent PCI from 2008 to 2011. Patients were randomized in 3:1 ratio into development (n = 900) and validation (n = 300) groups. CIN was defined as an increase of ≥25% and/or ≥0.5 mg/dl in serum creatinine at 48 hours after PCI when compared to baseline value. Seven independent predictors of CIN were identified using logistic regression analysis - amount of contrast, diabetes with microangiopathy, hypotension, peripheral vascular disease, albuminuria, glomerular filtration rate (GFR) and anemia. A formula was then developed to identify the probability of CIN using the logistic regression equation. Results: The mean (±SD) age was 57.3 (±10.2) years. 83.6% were males. The total incidence of CIN was 9.7% in the development group. The total risk of renal replacement therapy in the study group is 1.1%. Mortality is 0.5%. The risk scoring model correlated well in the validation group (incidence of CIN was 8.7%, sensitivity 92.3%, specificity 82.1%, c statistic 0.95). Conclusion: A simple risk scoring equation can be employed to predict the probability of CIN following PCI, applying it to each individual. More vigilant preventive measures can be applied to the high risk candidates.

Fluoxetine, belonging to the class of selective serotonin uptake inhibitors, has been extensively used for the treatment of depression and other psychiatry related disorders. Fluoxetine (CAS 59333-67-4) is a substrate of polymorphic cytochrome P450 2D6 isozyme (CYP2D6) leading to the formation of norfluoxetine (CAS 83891-03-06), which is not only active but long lived than the parent in the systemic circulation. Since the parent and metabolite levels are important from a therapeutic perspective, knowledge of phenotypic distribution of the population may be an important consideration. The aim of the work was to retrospectively evaluate the pharmacokinetic data of fluoxetine and norfluoxetine from several bioavailability/bioequivalence (BA/BE) studies to identify the poor metabolizer (PM) phenotypes from the unsuspected healthy subjects across varied protocol designs, dose sizes and differing formulations. The pharmacokinetic data of fluoxetine and norfluoxetine were gathered from several BA/BE studies conducted at clinical facilities located at Bangalore and Chennai, India. The BA/BE studies involved open label, two-way randomized crossover designs with two periods and two treatments (reference and test). Blood samples were collected for at least 672 h after fluoxetine dosing and the plasma was analyzed using validated tandem liquid chromatography mass spectrometric assay to determine fluoxetine and norfluoxetine levels. Standard pharmacokinetic parameters were computed using noncompartmental methods. For the purpose of this paper, retrospective evaluation of pharmacokinetic data from only the reference formulation was considered. The AUC ratio of fluoxetine/norfluoxetine was computed. The individual fluoxetine/norfluoxetine AUC 0−∞ ratios were plotted in increasing rank order and using outlier test (‘T’procedure at 5% level of significance) the subjects were categorized as extensive metabolizer (EM) and PM phenotypes. The unequivocal confirmation of PM phenotypes was obtained by performing linear regression analysis of fluoxetine vs norfluoxetine AUC 0–∞ values. Each study was evaluated for the distribution of EM and PM phenotypes of fluoxetine. There were 144 subjects evaluable from four studies, 89.6% (129 out of 144) of which could be categorised as EMs and 10.4% (15 out of 144) as PMs of fluoxetine. The pharmacokinetic parameters were quite distinct between the two phenotypes: (1) PM phenotypes showed much higher exposure (approximately 2.3-fold increase in AUC 0−∞ ) and much slower elimination (almost 2-fold increase in elimination half-life) for fluoxetine as compared to EM phenotypes; (2) PM phenotypes showed approximately 0.5-fold lower exposure of norfluoxetine as compared to the EM counter parts; (3) There was no change (approximately 1.2-fold) in the elimination half life of norfluoxetine in EM and PM phenotypes Retrospective evaluation of fluoxetine and norfluoxetine pharmacokinetic data demonstrated existence of both PM and EM phenotypes in the Indian population. Based on the overall data (n = 144 subjects) there appeared to be 10.4% of PM phenotypes for fluoxetine and/or possibly for other polymorphic CYP2D6 substrates commonly used in this region.

Venlafaxine (VEN), a well accepted anti-depressant, is metabolized through the cytochrome P 450 (CYP) 2D6 isozyme to form O-desmethyvenlafaxine (ODV). Due to the involvement of CYP2D6, the formation of ODV is influenced by genetic polymorphism. We used standard tools of assessment to explore the phenotypic distribution in a retrospective manner using the pharmacokinetic (PK) data of VEN and ODV obtained from several bioavailability/bioequivalence (BA/BE) studies in healthy subjects using the reference formulation.Four single oral dose, open-label, randomized crossover BA/BE studies of VEN (doses: 37.5-150 mg) were performed in 141 healthy subjects. Plasma samples were collected over a period 72 h post VEN administration. The samples were analyzed for VEN and ODV using a validated LC/MS/MS assay with a limit of quantification 2.073 ng/mL for VEN and 3.973 ng/mL for ODV. PK parameters (C(max), T(max), AUC (0-t), AUC(0-infinity), t(1/2)) were computed using the noncompartmental approach. AUC metabolic ratios of VEN/ODV and ODV/VEN were computed for all subjects and were subjected to normality test procedures to tease out phenotypic distribution.ODV/VEN and VEN/ODV AUC metabolic ratios were evaluated for standard normal distribution and outliers to determine phenotypic distribution. Use of the VEN/ODV AUC metabolic ratio, arranged in a rank order, resulted in a distribution that distinguished poor metabolizers (PM) and extensive metabolizers (EM). The application of the ODV/VEN AUC metabolic ratio showed a unique distribution that distinguished ultra metabolizers (UM) and extensive metabolizers. By using both metabolic ratios, 141 healthy subjects were classified as follows: PMs = 18, EMs = 118, UMs = 5. Regardless of the formulation or dose size used, the plasma concentration-time profiles for both VEN and ODV were distinct amongst the three phenotypes identified in this work.The use of VEN/ODV and ODV/VEN AUC metabolic ratios suggested quantitative differences. The data support the use of ODV/VEN but not VEN/ODV metabolic ratio for the identification of UM phenotypes of VEN. The derived metabolic ratios of ODV/VEN from this work were in line with other studies that used both phenotypic and genotypic correlation strategies for VEN.

Fluoxetine, belonging to the class of selective serotonin uptake inhibitors, has been extensively used for the treatment of depression and other psychiatry related disorders. Fluoxetine (CAS 59333-67-4) is a substrate of polymorphic cytochrome P450 2D6 isozyme (CYP2D6) leading to the formation of norfluoxetine (CAS 83891-03-06), which is not only active but long lived than the parent in the systemic circulation. Since the parent and metabolite levels are important from a therapeutic perspective, knowledge of phenotypic distribution of the population may be an important consideration.The aim of the work was to retrospectively evaluate the pharmacokinetic data of fluoxetine and norfluoxetine from several bioavailability/bioequivalence (BA/BE) studies to identify the poor metabolizer (PM) phenotypes from the unsuspected healthy subjects across varied protocol designs, dose sizes and differing formulations.The pharmacokinetic data of fluoxetine and norfluoxetine were gathered from several BA/BE studies conducted at clinical facilities located at Bangalore and Chennai, India. The BA/BE studies involved open label, two-way randomized crossover designs with two periods and two treatments (reference and test). Blood samples were collected for at least 672 h after fluoxetine dosing and the plasma was analyzed using validated tandem liquid chromatography mass spectrometric assay to determine fluoxetine and norfluoxetine levels. Standard pharmacokinetic parameters were computed using noncompartmental methods. For the purpose of this paper, retrospective evaluation of pharmnacokinetic data from only the reference formulation was considered. The AUC ratio of fluoxetine/norfluoxetine was computed. The individual fluoxetine/norfluoxetine AUC(0-infinity), ratios were plotted in increasing rank order and using outlier test ('T' procedure at 5% level of significance) the subjects were categorized as extensive metabolizer (EM) and PM phenotypes. The unequivocal confirmation of PM phenotypes was obtained by performing linear regression analysis of fluoxetine vs norfluoxetine AUC(0-infinity) values.Each study was evaluated for the distribution of EM and PM phenotypes of fluoxetine. There were 144 subjects evaluable from four studies, 89.6% (129 out of 144) of which could be categorised as EMs and 10.4% (15 out of 144) as PMs of fluoxetine. The pharmacokinetic parameters were quite distinct between the two phenotypes: (1) PM phenotypes showed much higher exposure (approximately 2.3-fold increase in AUC(0-infinity) and much slower elimination (almost 2-fold increase in elimination half-life) for fluoxetine as compared to EM phenotypes; (2) PM phenotypes showed approximately 0.5-fold lower exposure of norfluoxetine as compared to the EM counter parts; (3) There was no change (approximately 1.2-fold) in the elimination half life of norfluoxetine in EM and PM phenotypes.Retrospective evaluation of fluoxetine and norfluoxetine pharmacokinetic data demonstrated existence of both PM and EM phenotypes in the Indian population. Based on the overall data (n=144 subjects) there appeared to be 10.4% of PM phenotypes for fluoxetine and/or possibly for other polymorphic CYP2D6 substrates commonly used in this region.

This paper summarizes the efforts carried out over the last three years for the prediction of high-speed sealift (HSSL) ships. Semi-planing monohulls, catamarans, trimarans, and small waterplane area twin hull (SWATH) concepts are some of the hull forms that were considered. Propulsor options for these configurations varied from traditional propellers mounted on shafts and struts to waterjet propulsion systems. Computational tools are needed to predict the hydrodynamics of these multihull configurations for their proper design and analysis in many areas including resistance and powering, maneuvering and seakeeping. The focus of the present effort is to use an Unsteady Reynolds averaged Navier-Stokes (URANS) solver, CFDShip-Iowa, to predict the flow fields. Many of these predictions are very computationally intensive and thus require high performance computing resources. This paper will discuss the progress made in the overall effort along with some of the validations performed.

High Speed Sea Lift (HSSL) is an important area of interest for the US Navy. Computational tools are needed to predict the hydrodynamics of these multihull configurations for their proper design and analysis in many areas including resistance and powering, motions and habitability, loads in service and maneuverability. To address these issues requires a hierarchy of computational and experimental capabilities. Described here are efforts underway to use Unsteady Reynolds Averaged Navier-Stokes (URANS) solvers to answer some of these needs. Specifically, computations are demonstrated with CFDSHIP-Iowa to predict relevant HSSL catamaran and trimaran configurations under a variety of operating conditions in both calm water and with waves. Many of these predictions, particularly those representing behavior in various sea states, are very computationally intensive and thus require high performance computing resources. The predictions demonstrated here are paving the way for a computational capability to aid in the design for a new generation of naval ships.

vortex and turbulence interactions. First identified and studied using a surface-piercing foil mounted on the floor of a hydraulic flume. This building block geometry facilitates identification of the salient features; since, the foil profile was designed for limited separation for the deep, no-wave, and twodimensional (2D) condition. Tests were conducted for Froude number ( gc Uc / = Fr ) range 0. 20.48 with average Reynolds number ( ν / Re c U c = ) = 7x10 (where Uc=carriage speed, c=chord length, and ν=kinematic viscosity). Wave profile photographs and needlepoint, dye injection flow visualization was used to determine the length and depth of the separation region. Separation was defined by a region of highly disturbed free-surface flow, which initiated just beyond the wave trough and extended to the foil trailing edge for all but the highest Fr ranging from separation starting point x/c=. 7 at small Fr=. 2, to maximum x/c=. 42 at medium Fr=. 25, and x/c=. 6 at Fr=. 37. Beyond the separation starting point, the wave profile was nearly constant. The depth of separation was defined by reversed axial flow and upward cross flow, which was observed close to the foil surface in a wedge shaped region gradually expanding from the separation starting point to a maximum depth near the foil trailing edge with magnitude similar to the wave height. Stratford’s laminar separation criterion showed good agreement with separation starting point data. A general-purpose unsteady Reynolds-averaged Navier-Stokes (URANS) research code CFDSHIPIOWA developed for ship hydrodynamics application is extended for detached eddy simulation (DES) capability. CFDSHIP-IOWA uses surface-tracking free-surface model, k-ω turbulence model, and high performance computing. Both 2 and 3 order upwind biased scheme for spatial derivatives were applied for URANS while 3 order upwind biased scheme used for DES. DES extensions are based on the blended k-ω model by modifying the length scale in the k equation and validated with surface piercing NACA 0024 benchmark, including IIHR towing-tank EFD data and concurrent URANS. Domain and grid convergence studies were conducted for 2 order RANS. 3 order RANS was also studied on a coarse grid and a 3 order DES was conducted on both coarse and medium grids. Statistical analysis of the results, including time history, running mean, and FFT of total drag and side forces, mean and RMS of wave elevations and pressure on foil surface, and unsteady 3D separation flow pattern are presented. Results show fairly good agreement EFD validation data for mean, RMS, and FFT frequencies for wave elevations and surface pressure; however, many modeling and numerical issues remain. Seemingly credible flow features of unsteady wave-induced separation have been simulated for the first time, which will be used to guide future PIV measurements. More recently, [2-4] used a similar geometry (surface-piercing NACA 0024) for complementary towing-tank (3x3x100m) experimental fluid dynamics (EFD) and steady RANS computational fluid dynamics (CFD). Wave profiles, mean far field wave elevations, and mean and RMS near field wave elevations and surface-pressure measurements were made for c=1.2m and deep draft d=1.5m foil and Fr=0.19, 0.37, and 0.55 and Re=(0.822, 1.52, 2.26) x 10. EFD results similar to[1], except differences foil geometry and restricted water and foil bottom effects. Steady RANS solutions show good agreement wave profiles and surface pressure, but only fair agreement wave elevations due to poor resolution short waves for low Fr and separation region for medium Fr. RANS solutions also provide details of separated flow pattern, but data not yet available for validation

Turbulent structures of the unsteady free-surface wave-induced separation around a surface-piercing NACA0024 are studied using a detached-eddy simulation with a single-phase level set method. Quantitative verification and validation show good agreement with experimental data. Instantaneous and mean flows show an owl-type separation with the same instability frequencies identified by unsteady Reynolds-averaged Navier–Stokes. Anisotropy invariant maps show that turbulence is anisotropic in the middle of the separation region and is at a two-component state near the foil surface. The turbulent kinetic energy and its budget have similarities to that of a separated turbulent boundary layer near the toe and a backward-facing step flow in the recirculation region, but with large three-dimensional and free-surface effects. The free-surface damps velocity and pressure fluctuations and moves the peaks of turbulence quantities from the high-speed to the low-speed side of the free shear layer.