ANALYITCAL METHOD DEVELOPMENT AND VALIDATION OF A REVERSED-PHASE HIGHPERFORMANCE LIQUID CHROMATOGRAPHY FOR THE DETERMINATION OF MODAFINIL IN BULK AND PHARMACEUTICAL DOSAGE FORMS

Objective: To development and validation of a reversed-phase high-performance liquid chromatography (RP-HPLC) for the determination of modafinil in bulk and pharmaceutical dosage forms.Methods: A simple, precise, rapid, and accurate RP-HPLC method was developed for the estimation of modafinil in bulk and pharmaceutical dosageforms. Xterra RP 18 (250 mm × 4.6 mm, 5 μ particle size) with a mobile phase consisting of methanol:water 70:30 V/V was used. The flow rate1.0 ml/min and the effluents were monitored at 260 nm. The retention time and recovery time was 12 minutes. The detector response was linear inthe concentration of 10-50 μg/ml. The respective linear regression equation being Y=452.1x+65237. The limit of detection and limit of quantificationwere 4.547 and 1.377 mcg, respectively. The method was validated by determining its accuracy, precision, and system suitability.Result: The objective of the present work is to develop simple, precise, and reliable HPLC method for the analysis of modafinil in bulk andpharmaceutical dosage forms. This is achieved using the most commonly employed Xterra RP 18 (250 mm × 4.6 mm, 5 μ particle size) columndetection at 260 nm. The present method was validated according to ICH guidelines.Conclusion: In this study, a simple, fast and reliable HPLC method was developed and validated for the determination of modafinil in pharmaceuticalformulations.Keywords: Modafinil, Reversed-phase high-performance liquid chromatography, Estimation, ICH guidelines, Tablets

Breaking time-reversal and translational symmetry at edges of dd-wave superconductors: microscopic theory and comparison with quasiclassical theory

We report results of a microscopic calculation of a second-order phase transition into a state breaking time-reversal and translational invariance along pair-breaking edges of $d$-wave superconductors. By solving a tight-binding model through exact diagonalization with the Bogoliubov-de~Gennes method, we find that such a state with current loops having a diameter of a few coherence lengths is energetically favorable below $T^*$ between 10%-20% of $T_{\mathrm{c}}$ of bulk superconductivity, depending on model parameters. This extends our previous studies of such a phase crystal within the quasiclassical theory of superconductivity, and shows that the instability is not qualitatively different when including a more realistic band structure and the fast oscillations on the scale of the Fermi wavelength. Effects of size quantization and Friedel oscillations are not detrimental. We also report on a comparison with quasiclassical theory with the Fermi surfaces extracted from the tight-binding models used in the microscopic calculation. There are quantitative differences in for instance the value of $T^*$ between the different models, but we can explain the predicted transition temperature within each model as due to the different spectral weights of zero-energy Andreev bound states and the resulting gain in free energy by breaking time-reversal and translational invariance below $T^*$.Comment: 15 pages and 9 figure

The Octant Module of the ATLAS Level-1 Muon to Central Trigger Processor Interface

The Muon to Central Trigger Processor Interface (MUCTPI) of the ATLAS Level-1 trigger receives data from the sector logic modules of the muon trigger at every bunch crossing and calculates the total multiplicity of muon candidates, which is then sent to the Central Trigger Processor where the final Level-1 decision is taken. The MUCTPI system consists of a 9U VME crate with a special backplane and 18 custom designed modules. We focus on the design and implementation of the octant module (MIOCT). Each of the 16 MIOCT modules processes the muon candidates from 13 sectors of one half-octant of the detector and forms the local muon candidate multiplicities for the trigger decision. It also resolves the overlaps between chambers in order to avoid double-counting of muon candidates that are detected in more than one sector. The handling of overlapping sectors is based on Look-Up-Tables (LUT) for maximum flexibility. The MIOCT also sends the information on the muon candidates over the custom backplane via the Readout Driver module to the Level-2 trigger and the DAQ systems when a Level-1 Accept is received. The design is based on state-of-the-art FPGA devices and special attention was paid to low-latency in the data transmission and processing

Common carotid segmentation in 18F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based and manual method

Background: Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with 18F-sodium fluoride (NaF) detects arterial wall micro-calcification long before macro-calcification becomes detectable by ultrasound, CT or magnetic resonance imaging. However, manual PET/CT processing is time-consuming and requires experience. We compared a convolutional neural network (CNN) approach with manual segmentation of the common carotids. Methods: Segmentation in NaF-PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients were compared for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, and\ua0SUVtotal). SUVmean was the average of SUVmeans within the VOI, SUVmax the highest SUV in all voxels in the VOI, and SUVtotal the SUVmean multiplied by the Vol of the VOI. Intra\ua0and Interobserver variability with manual segmentation was examined in 25 randomly selected scans. Results: Bias for Vol, SUVmean, SUVmax, and SUVtotal were 1.33 \ub1 2.06, −0.01 \ub1 0.05, 0.09 \ub1 0.48, and 1.18 \ub1 1.99 in the left and 1.89 \ub1 1.5, −0.07 \ub1 0.12, 0.05 \ub1 0.47, and 1.61 \ub1 1.47, respectively, in the right common carotid artery. Manual segmentation lasted typically 20 min versus 1 min with the CNN-based approach. Mean Vol deviation at repeat manual segmentation was 14% and 27% in left and right common carotids. Conclusions: CNN-based segmentation was much faster and provided SUVmean values virtually identical to manually obtained ones, suggesting CNN-based analysis as a promising substitute of slow and cumbersome manual processing