2,071 research outputs found

    Surgical management of BPH in Ghana: A need to improve access to transurethral resection of the prostate

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    Background: Open prostatectomy for benign prostate hyperplasia (BPH) is widely practiced in Ghana and Africa. Some of the reasons include lack of expertise and facilities for Transurethral Resection of the Prostate (TURP) and digital rectal examination assessment of prostates as greater than 50 grams.Objectives: To assess the prostate volumes of patients for surgical management of BPH by transrectal ultrasound (TRUS) and to determine, on the basis of prostatic volume, what percentage of those who had open prostatectomy could have been managed by TURP.Design: Prospective cohort study.Setting: The Korle Bu Teaching Hospital, Accra, Ghana.Subjects: Patients for elective surgical management of BPH from March to September 2010 were studied.Results: Fifty-eight patients had surgical management of BPH. Forty-six of them (79.3%) had open prostatectomy whilst twelve (20.7%) had TURP with a mean age of 70.4 and 65.2 years respectively. The most common reason for the open prostatectomy was refractory retention of urine (76.0%) while that for TURP was lower urinary tract symptoms (58.3%). The mean prostate volume for the patients who had open prostatectomy was 64.2ml ±28.7mls (range 23.0-121.0ml) while that of the TURP group was 40.1g±16.2mls (range18.5-70.0mls). Of the open prostatectomy group, 67.4% of them had prostate volumes 75mls or less. The blood transfusion and peri-operative complication rates for the open prostatectomy and TURP groups were 13% versus 8.3% and 8.7% versus 8.3% respectively. There was no mortality.Conclusion: Access to TURP in the surgical management of BPH in Ghana is low (20.7%). With improved facilities including routine use of TRUS for assessing prostate size and availability of expertise for TURP, 67.4% of patients offered open prostatectomy presently could benefit from TURP, using prostate volumes 75mls (75g) or less as indication for TURP

    Retrocaval uterer: Two case reports

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    Retrocaval ureter also referred to as pre-ureteral vena cava is a rare congenital anomaly with the ureter pass-ing posterior to the inferior vena cava. Though it is a congenital anomaly, patients do not normally present with symptoms until the 3rd and 4th decades of life from a resulting hydronephrosis. We present the first two cases to be reported in Ghana; a 36-year-old male and a 40-year-old female both with right flank pains and associated right hydronephrosis. Diagnoses were confirmed with retrograde ureteropyelogram and both had an open surgical repair of the anomaly

    Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study

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    [EN] Introduction: Focal atrial tachycardia is commonly treated by radio frequency ablation with an acceptable long-term success. Although the location of ectopic foci tends to appear in specific hot-spots, they can be located virtually in any atrial region. Multi-electrode surface ECG systems allow acquiring dense body surface potential maps (BSPM) for non-invasive therapy planning of cardiac arrhythmia. However, the activation of the atria could be affected by fibrosis and therefore biomarkers based on BSPM need to take these effects into account. We aim to analyze the effect of fibrosis on a BSPM derived index, and its potential application to predict the location of ectopic foci in the atria. Methodology: We have developed a 3D atrial model that includes 5 distributions of patchy fibrosis in the left atrium at 5 different stages. Each stage corresponds to a different amount of fibrosis that ranges from 2 to 40%. The 25 resulting 3D models were used for simulation of Focal Atrial Tachycardia (FAT), triggered from 19 different locations described in clinical studies. BSPM were obtained for all simulations, and the body surface potential integral maps (BSPiM) were calculated to describe atrial activations. A machine learning (ML) pipeline using a supervised learning model and support vector machine was developed to learn the BSPM patterns of each of the 475 activation sequences and relate them to the origin of the FAT source. Results: Activation maps for stages with more than 15% of fibrosis were greatly affected, producing conduction blocks and delays in propagation. BSPiMs did not always cluster into non-overlapped groups since BSPiMs were highly altered by the conduction blocks. From stage 3 (15% fibrosis) the BSPiMs showed differences for ectopic beats placed around the area of the pulmonary veins. Classification results were mostly above 84% for all the configurations studied when a large enough number of electrodes were used to map the torso. However, the presence of fibrosis increases the area of the ectopic focus location and therefore decreases the utility for the electrophysiologist. Conclusions: The results indicate that the proposed ML pipeline is a promising methodology for non-invasive ectopic foci localization from BSPM signal even when fibrosis is present.This work was partially supported by Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional (FEDER) DPI2015-69125-R and TIN2014-59932-JIN (MINECO/FEDER, UE).Godoy, EJ.; Lozano, M.; García-Fernández, I.; Ferrer-Albero, A.; Macleod, R.; Saiz, J.; Sebastián, R. (2018). Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study. Frontiers in Physiology. 9:1-18. https://doi.org/10.3389/fphys.2018.00404S1189Boyle, P. M., Zahid, S., & Trayanova, N. A. (2016). Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia. EP Europace, 18(suppl_4), iv136-iv145. doi:10.1093/europace/euw358Courtemanche, M., Ramirez, R. J., & Nattel, S. (1998). Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. American Journal of Physiology-Heart and Circulatory Physiology, 275(1), H301-H321. doi:10.1152/ajpheart.1998.275.1.h301Daccarett, M., Badger, T. J., Akoum, N., Burgon, N. S., Mahnkopf, C., Vergara, G., … Marrouche, N. F. (2011). Association of Left Atrial Fibrosis Detected by Delayed-Enhancement Magnetic Resonance Imaging and the Risk of Stroke in Patients With Atrial Fibrillation. Journal of the American College of Cardiology, 57(7), 831-838. doi:10.1016/j.jacc.2010.09.049Dössel, O., Krueger, M. W., Weber, F. M., Wilhelms, M., & Seemann, G. (2012). Computational modeling of the human atrial anatomy and electrophysiology. Medical & Biological Engineering & Computing, 50(8), 773-799. doi:10.1007/s11517-012-0924-6Ferrer, A., Sebastián, R., Sánchez-Quintana, D., Rodríguez, J. F., Godoy, E. J., Martínez, L., & Saiz, J. (2015). Detailed Anatomical and Electrophysiological Models of Human Atria and Torso for the Simulation of Atrial Activation. PLOS ONE, 10(11), e0141573. doi:10.1371/journal.pone.0141573Ferrer-Albero, A., Godoy, E. J., Lozano, M., Martínez-Mateu, L., Atienza, F., Saiz, J., & Sebastian, R. (2017). Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps. PLOS ONE, 12(7), e0181263. doi:10.1371/journal.pone.0181263Geselowitz, D. B., & Miller, W. T. (1983). A bidomain model for anisotropic cardiac muscle. Annals of Biomedical Engineering, 11(3-4), 191-206. doi:10.1007/bf02363286Giffard-Roisin, S., Jackson, T., Fovargue, L., Lee, J., Delingette, H., Razavi, R., … Sermesant, M. (2017). Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping. IEEE Transactions on Biomedical Engineering, 64(9), 2206-2218. doi:10.1109/tbme.2016.2629849Go, A. S., Hylek, E. M., Phillips, K. A., Chang, Y., Henault, L. E., Selby, J. V., & Singer, D. E. (2001). Prevalence of Diagnosed Atrial Fibrillation in Adults. JAMA, 285(18), 2370. doi:10.1001/jama.285.18.2370Guillem, M. S., Climent, A. M., Rodrigo, M., Fernández-Avilés, F., Atienza, F., & Berenfeld, O. (2016). Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications. Cardiovascular Research, 109(4), 480-492. doi:10.1093/cvr/cvw011Heidenreich, E. A., Ferrero, J. M., Doblaré, M., & Rodríguez, J. F. (2010). Adaptive Macro Finite Elements for the Numerical Solution of Monodomain Equations in Cardiac Electrophysiology. Annals of Biomedical Engineering, 38(7), 2331-2345. doi:10.1007/s10439-010-9997-2HOFFMANN, E., REITHMANN, C., NIMMERMANN, P., ELSER, F., DORWARTH, U., REMP, T., & STEINBECK, G. (2002). Clinical Experience with Electroanatomic Mapping of Ectopic Atrial Tachycardia. Pacing and Clinical Electrophysiology, 25(1), 49-56. doi:10.1046/j.1460-9592.2002.00049.xJacquemet, V. (2012). An eikonal-diffusion solver and its application to the interpolation and the simulation of reentrant cardiac activations. Computer Methods and Programs in Biomedicine, 108(2), 548-558. doi:10.1016/j.cmpb.2011.05.003Jalife, J. (2010). Deja vu in the theories of atrial fibrillation dynamics. Cardiovascular Research, 89(4), 766-775. doi:10.1093/cvr/cvq364Keller, D. U. J., Weber, F. M., Seemann, G., & Dössel, O. (2010). Ranking the Influence of Tissue Conductivities on Forward-Calculated ECGs. IEEE Transactions on Biomedical Engineering, 57(7), 1568-1576. doi:10.1109/tbme.2010.2046485Kistler, P. M., Fynn, S. P., Haqqani, H., Stevenson, I. H., Vohra, J. K., Morton, J. B., … Kalman, J. M. (2005). Focal Atrial Tachycardia From the Ostium of the Coronary Sinus. Journal of the American College of Cardiology, 45(9), 1488-1493. doi:10.1016/j.jacc.2005.01.042Kistler, P. M., Roberts-Thomson, K. C., Haqqani, H. M., Fynn, S. P., Singarayar, S., Vohra, J. K., … Kalman, J. M. (2006). P-Wave Morphology in Focal Atrial Tachycardia. Journal of the American College of Cardiology, 48(5), 1010-1017. doi:10.1016/j.jacc.2006.03.058Kistler, P. M., Sanders, P., Fynn, S. P., Stevenson, I. H., Hussin, A., Vohra, J. K., … Kalman, J. M. (2003). Electrophysiological and Electrocardiographic Characteristics of Focal Atrial Tachycardia Originating From the Pulmonary Veins. Circulation, 108(16), 1968-1975. doi:10.1161/01.cir.0000095269.36984.75Kistler, P. M., Sanders, P., Hussin, A., Morton, J. B., Vohra, J. K., Sparks, P. B., & Kalman, J. M. (2003). Focal atrial tachycardia arising from the mitral annulus. Journal of the American College of Cardiology, 41(12), 2212-2219. doi:10.1016/s0735-1097(03)00484-4Andrew MacCannell, K., Bazzazi, H., Chilton, L., Shibukawa, Y., Clark, R. B., & Giles, W. R. (2007). A Mathematical Model of Electrotonic Interactions between Ventricular Myocytes and Fibroblasts. Biophysical Journal, 92(11), 4121-4132. doi:10.1529/biophysj.106.101410MacLeod, R. S., Kholmovski, E., DiBella, E. V. R., Oakes, R. S., Blauer, J. E., Fish, E., … Marrouche, N. F. (2008). Integration of MRI in evaluation and ablation of atrial fibrillation. 2008 Computers in Cardiology. doi:10.1109/cic.2008.4748981Maleckar, M. M., Greenstein, J. L., Giles, W. R., & Trayanova, N. A. (2009). Electrotonic Coupling between Human Atrial Myocytes and Fibroblasts Alters Myocyte Excitability and Repolarization. Biophysical Journal, 97(8), 2179-2190. doi:10.1016/j.bpj.2009.07.054Morgan, R., Colman, M. A., Chubb, H., Seemann, G., & Aslanidi, O. V. (2016). Slow Conduction in the Border Zones of Patchy Fibrosis Stabilizes the Drivers for Atrial Fibrillation: Insights from Multi-Scale Human Atrial Modeling. Frontiers in Physiology, 7. doi:10.3389/fphys.2016.00474MORTON, J. B., SANDERS, P., DAS, A., VOHRA, J. K., SPARKS, P. B., & KALMAN, J. M. (2001). Focal Atrial Tachycardia Arising from the Tricuspid Annulus: Electrophysiologic and Electrocardiographic Characteristics. Journal of Cardiovascular Electrophysiology, 12(6), 653-659. doi:10.1046/j.1540-8167.2001.00653.xNiederer, S. A., Kerfoot, E., Benson, A. P., Bernabeu, M. O., Bernus, O., Bradley, C., … Smith, N. P. (2011). Verification of cardiac tissue electrophysiology simulators using an N -version benchmark. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1954), 4331-4351. doi:10.1098/rsta.2011.0139Oakes, R. S., Badger, T. J., Kholmovski, E. G., Akoum, N., Burgon, N. S., Fish, E. N., … Marrouche, N. F. (2009). Detection and Quantification of Left Atrial Structural Remodeling With Delayed-Enhancement Magnetic Resonance Imaging in Patients With Atrial Fibrillation. Circulation, 119(13), 1758-1767. doi:10.1161/circulationaha.108.811877Ramanathan, C., Jia, P., Ghanem, R., Calvetti, D., & Rudy, Y. (2003). Noninvasive Electrocardiographic Imaging (ECGI): Application of the Generalized Minimal Residual (GMRes) Method. Annals of Biomedical Engineering, 31(8), 981-994. doi:10.1114/1.1588655Santangeli, P., & Marchlinski, F. E. (2017). Techniques for the provocation, localization, and ablation of non–pulmonary vein triggers for atrial fibrillation. Heart Rhythm, 14(7), 1087-1096. doi:10.1016/j.hrthm.2017.02.030Santangeli, P., Zado, E. S., Hutchinson, M. D., Riley, M. P., Lin, D., Frankel, D. S., … Marchlinski, F. E. (2016). Prevalence and distribution of focal triggers in persistent and long-standing persistent atrial fibrillation. Heart Rhythm, 13(2), 374-382. doi:10.1016/j.hrthm.2015.10.023Saoudi, N. (2001). A classification of atrial flutter and regular atrial tachycardia according to electrophysiological mechanisms and anatomical bases. A Statement from a Joint Expert Group from the Working Group of Arrhythmias of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. European Heart Journal, 22(14), 1162-1182. doi:10.1053/euhj.2001.2658Shah, A. J., Hocini, M., Pascale, P., Roten, L., Komatsu, Y., … Daly, M. (2013). Body Surface Electrocardiographic Mapping for Non-invasive Identification of Arrhythmic Sources. Arrhythmia & Electrophysiology Review, 2(1), 16. doi:10.15420/aer.2013.2.1.16SippensGroenewegen, A., Natale, A., Marrouche, N. F., Bash, D., & Cheng, J. (2004). Potential role of body surface ECG mapping for localization of atrial fibrillation trigger sites. Journal of Electrocardiology, 37, 47-52. doi:10.1016/j.jelectrocard.2004.08.017Sippensgroenewegen, A., Roithinger, F. X., Peeters, H. A. ., Linnenbank, A. C., van Hemel, N. M., Steiner, P. R., & Lesh, M. D. (1998). Body surface mapping of atrial arrhythmias: Atlas of paced p wave integral maps to localize the focal origin of right atrial tachycardia. Journal of Electrocardiology, 31, 85-91. doi:10.1016/s0022-0736(98)90298-9SPACH, M. S., & BOINEAU, J. P. (1997). Microfibrosis Produces Electrical Load Variations Due to Loss of Side-to-Side Cell Connections; A Major Mechanism of Structural Heart Disease Arrhythmias. Pacing and Clinical Electrophysiology, 20(2), 397-413. doi:10.1111/j.1540-8159.1997.tb06199.xTrayanova, N. A., & Boyle, P. M. (2013). Advances in modeling ventricular arrhythmias: from mechanisms to the clinic. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 6(2), 209-224. doi:10.1002/wsbm.1256Vigmond, E., Pashaei, A., Amraoui, S., Cochet, H., & Hassaguerre, M. (2016). Percolation as a mechanism to explain atrial fractionated electrograms and reentry in a fibrosis model based on imaging data. Heart Rhythm, 13(7), 1536-1543. doi:10.1016/j.hrthm.2016.03.019Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236-244. doi:10.1080/01621459.1963.10500845Weber, F. M., Keller, D. U. J., Bauer, S., Seemann, G., Lorenz, C., & Dössel, O. (2011). Predicting Tissue Conductivity Influences on Body Surface Potentials—An Efficient Approach Based on Principal Component Analysis. IEEE Transactions on Biomedical Engineering, 58(2), 265-273. doi:10.1109/tbme.2010.2090151Zhao, J., Kharche, S., Hansen, B., Csepe, T., Wang, Y., Stiles, M., & Fedorov, V. (2015). Optimization of Catheter Ablation of Atrial Fibrillation: Insights Gained from Clinically-Derived Computer Models. International Journal of Molecular Sciences, 16(12), 10834-10854. doi:10.3390/ijms16051083

    Assessing the importance of car meanings and attitudes in consumer evaluations of electric vehicles

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    This paper reports findings from a research study which assesses the importance of attitudinal constructs related to general car attitudes and the meanings attached to car ownership over evaluations of electric vehicles (EVs). The data are assessed using principal component analysis to evaluate the structure of the underlying attitudinal constructs. The identified constructs are then entered into a hierarchical regression analysis which uses either positive or negative evaluations of the instrumental capabilities of EVs as the dependent variable. Results show that attitudinal constructs offer additional predictive power over socioeconomic characteristics and that the symbolic and emotive meanings of car ownership are as, if not more, effective in explaining the assessment of EV instrumental capability as compared to issues of cost and environmental concern. Additionally, the more important an individual considers their car to be in their everyday life, the more negative their evaluations are of EVs whilst individuals who claim to be knowledgeable about cars in general and EVs in particular have a lower propensity for negative EV attitudes. However, positive and negative EV attitudes are related to different attitudinal constructs suggesting that it is possible for someone to hold both negative and positive assessments at the same time

    The use of measured genotype information in the analysis of quantitative phenotypes in man

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    Improved laboratory methods allow one to investigate the contribution of measured allelic variability at a locus physiologically involved in determining the expression of a quantitative trait. We present statistical methods that incorporate measured genotype information into the analysis of a quantitative phenotype that allows one simultaneously to detect and estimate the effects of a measured single locus and residual polygenic effects. Likelihoods are presented for the joint distribution of the quantitative phenotype and a measured genotype that are appropriate when the data are collected as a sample of unrelated individuals or as a sample of nuclear families. Application of this method to the analysis of serum cholesterol levels and the concentration of the group specific component (Gc) are presented. The analysis of the contribution of the common Gc polymorphism to the determination of quantitative variability in Gc using smaples of related and unrelated individuals presents, for the first time, the simultaneous estimation of the frequencies and the effects of the genotypes at a measured locus, and the contribution of residual unmeasured polygenes to phenotypic variability.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65935/1/j.1469-1809.1986.tb01037.x.pd

    Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

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    The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum
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