Evaluation of anti-biofilm activity of acidic amino acids and synergy with ciprofloxacin on Staphylococcus aureus biofilms

Acidic amino acids, aspartic acid (Asp) and glutamic acid (Glu) can enhance the solubility of many poorly soluble drugs including ciprofloxacin (Cip). One of the mechanisms of resistance within a biofilm is retardation of drug diffusion due to poor penetration across the matrix. To overcome this challenge, this work set to investigate novel counter ion approach with acidic amino acids, which we hypothesised will disrupt the biofilm matrix as well as simultaneously improve drug effectiveness. The anti-biofilm activity of D-Asp and D-Glu was studied on Staphylococcus aureus biofilms. Synergistic effect of combining D-amino acids with Cip was also investigated as a strategy to overcome anti-microbial resistance in these biofilms. Interestingly at equimolar combinations, D-Asp and D-Glu were able to significantly disperse (at 20 mM and 40 mM) established biofilms and inhibit (at 10 mM, 20 mM and 40 mM) new biofilm formation in the absence of an antibiotic. Moreover, our study confirmed L-amino acids also exhibit anti-biofilm activity. The synergistic effect of acidic amino acids with Cip was observed at lower concentration ranges (<40 mM amino acids and <90.54 µM, respectively), which resulted in 96.89% (inhibition) and 97.60% (dispersal) reduction in CFU with exposure to 40 mM amino acids. Confocal imaging indicated that the amino acids disrupt the honeycomb-like extracellular DNA (eDNA) meshwork whilst also preventing its formation

Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study

[EN] Patients suffering from heart failure and left bundle branch block show electrical ventricular dyssynchrony causing an abnormal blood pumping. Cardiac resynchronization therapy (CRT) is recommended for these patients. Patients with positive therapy response normally present QRS shortening and an increased left ventricle (LV) ejection fraction. However, around one third do not respond favorably. Therefore, optimal location of pacing leads, timing delays between leads and/or choosing related biomarkers is crucial to achieve the best possible degree of ventricular synchrony during CRT application. In this study, computational modeling is used to predict the optimal location and delay of pacing leads to improve CRT response. We use a 3D electrophysiological computational model of the heart and torso to get insight into the changes in the activation patterns obtained when the heart is paced from different regions and for different atrioventricular and interventricular delays. The model represents a heart with left bundle branch block and heart failure, and allows a detailed and accurate analysis of the electrical changes observed simultaneously in the myocardium and in the QRS complex computed in the precordial leads. Computational simulations were performed using a modified version of the O'Hara et al. action potential model, the most recent mathematical model developed for human ventricular electrophysiology. The optimal location for the pacing leads was determined by QRS maximal reduction. Additionally, the influence of Purkinje system on CRT response was assessed and correlation analysis between several parameters of the QRS was made. Simulation results showed that the right ventricle (RV) upper septum near the outflow tract is an alternative location to the RV apical lead. Furthermore, LV endocardial pacing provided better results as compared to epicardial stimulation. Finally, the time to reach the 90% of the QRS area was a good predictor of the instant at which 90% of the ventricular tissue was activated. Thus, the time to reach the 90% of the QRS area is suggested as an additional index to assess CRT effectiveness to improve biventricular synchrony.This work was supported by the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT) of Ecuador CIBAE-023-2014, the Plan Estatal de Investigación Científica y Técnica y de Innovación 2013 2016 from the Ministerio de Economía, Industria y Competitividad of Spain and Fondo Europeo de Desarrollo Regional (FEDER) DPI2016-75799-R (AEI/FEDER, UE), and by Dirección General de Política Científica de la Generalitat Valenciana (PROMETEU 2016/088).Carpio-Garay, EF.; Gómez García, JF.; Sebastian, R.; López-Pérez, AD.; Castellanos, E.; Almendral, J.; Ferrero De Loma-Osorio, JM.... (2019). Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study. Frontiers in Physiology. 10:1-17. https://doi.org/10.3389/fphys.2019.00074S11710Abraham, W. T., Fisher, W. G., Smith, A. L., Delurgio, D. B., Leon, A. R., Loh, E., … Messenger, J. (2002). Cardiac Resynchronization in Chronic Heart Failure. New England Journal of Medicine, 346(24), 1845-1853. doi:10.1056/nejmoa013168Abraham, W. T., Gras, D., Yu, C. M., Guzzo, L., & Gupta, M. S. (2010). Rationale and design of a randomized clinical trial to assess the safety and efficacy of frequent optimization of cardiac resynchronization therapy: The Frequent Optimization Study Using the QuickOpt Method (FREEDOM) trial. American Heart Journal, 159(6), 944-948.e1. doi:10.1016/j.ahj.2010.02.034Ai, X., & Pogwizd, S. M. (2005). Connexin 43 Downregulation and Dephosphorylation in Nonischemic Heart Failure Is Associated With Enhanced Colocalized Protein Phosphatase Type 2A. Circulation Research, 96(1), 54-63. doi:10.1161/01.res.0000152325.07495.5aAkar, F. G., Nass, R. D., Hahn, S., Cingolani, E., Shah, M., Hesketh, G. G., … Tomaselli, G. F. (2007). Dynamic changes in conduction velocity and gap junction properties during development of pacing-induced heart failure. American Journal of Physiology-Heart and Circulatory Physiology, 293(2), H1223-H1230. doi:10.1152/ajpheart.00079.2007ARBELO, E., TOLOSANA, J. M., TRUCCO, E., PENELA, D., BORRÀS, R., DOLTRA, A., … MONT, L. (2013). Fusion-Optimized Intervals (FOI): A New Method to Achieve the Narrowest QRS for Optimization of the AV and VV Intervals in Patients Undergoing Cardiac Resynchronization Therapy. Journal of Cardiovascular Electrophysiology, 25(3), 283-292. doi:10.1111/jce.12322Auricchio, A., Fantoni, C., Regoli, F., Carbucicchio, C., Goette, A., Geller, C., … Klein, H. (2004). Characterization of Left Ventricular Activation in Patients With Heart Failure and Left Bundle-Branch Block. Circulation, 109(9), 1133-1139. doi:10.1161/01.cir.0000118502.91105.f6Auricchio, A., Klein, H., Tockman, B., Sack, S., Stellbrink, C., Neuzner, J., … Spinelli, J. (1999). Transvenous biventricular pacing for heart failure: can the obstacles be overcome? The American Journal of Cardiology, 83(5), 136-142. doi:10.1016/s0002-9149(98)01015-7Barber, F., García-Fernández, I., Lozano, M., & Sebastian, R. (2018). Automatic estimation of Purkinje-Myocardial junction hot-spots from noisy endocardial samples: A simulation study. International Journal for Numerical Methods in Biomedical Engineering, 34(7), e2988. doi:10.1002/cnm.2988Barold, S. S., Ilercil, A., & Herweg, B. (2008). Echocardiographic optimization of the atrioventricular and interventricular intervals during cardiac resynchronization. Europace, 10(Supplement 3), iii88-iii95. doi:10.1093/europace/eun220Bertaglia, E., Migliore, F., Baritussio, A., De Simone, A., Reggiani, A., Pecora, D., … Stabile, G. (2017). Stricter criteria for left bundle branch block diagnosis do not improve response to CRT. Pacing and Clinical Electrophysiology, 40(7), 850-856. doi:10.1111/pace.13104Bertini, M., Ziacchi, M., Biffi, M., Martignani, C., Saporito, D., Valzania, C., … Boriani, G. (2008). Interventricular Delay Interval Optimization in Cardiac Resynchronization Therapy Guided by Echocardiography Versus Guided by Electrocardiographic QRS Interval Width. The American Journal of Cardiology, 102(10), 1373-1377. doi:10.1016/j.amjcard.2008.07.015BLEEKER, G. B., SCHALIJ, M. J., MOLHOEK, S. G., VERWEY, H. F., HOLMAN, E. R., BOERSMA, E., … BAX, J. J. (2004). Relationship Between QRS Duration and Left Ventricular Dyssynchrony in Patients with End-Stage Heart Failure. Journal of Cardiovascular Electrophysiology, 15(5), 544-549. doi:10.1046/j.1540-8167.2004.03604.xBonakdar, H. R., Jorat, M. V., Fazelifar, A. F., Alizadeh, A., Givtaj, N., Sameie, N., … Haghjoo, M. (2009). Prediction of response to cardiac resynchronization therapy using simple electrocardiographic and echocardiographic tools. Europace, 11(10), 1330-1337. doi:10.1093/europace/eup258Bordachar, P., Derval, N., Ploux, S., Garrigue, S., Ritter, P., Haissaguerre, M., & Jaïs, P. (2010). Left Ventricular Endocardial Stimulation for Severe Heart Failure. Journal of the American College of Cardiology, 56(10), 747-753. doi:10.1016/j.jacc.2010.04.038Boukens, B. J., Rivaud, M. R., Rentschler, S., & Coronel, R. (2014). Misinterpretation of the mouse ECG: ‘musing the waves ofMus musculus’. The Journal of Physiology, 592(21), 4613-4626. doi:10.1113/jphysiol.2014.279380Bradley, C. P., Pullan, A. J., & Hunter, P. J. (1997). Geometric modeling of the human torso using cubic hermite elements. Annals of Biomedical Engineering, 25(1), 96-111. doi:10.1007/bf027385422013 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy. (2013). European Heart Journal, 34(29), 2281-2329. doi:10.1093/eurheartj/eht150Brugada, J., Brachmann, J., Delnoy, P. P., Padeletti, L., Reynolds, D., Ritter, P., … Singh, J. P. (2014). Automatic Optimization of Cardiac Resynchronization Therapy Using SonR—Rationale and Design of the Clinical Trial of the SonRtip Lead and Automatic AV-VV Optimization Algorithm in the Paradym RF SonR CRT-D (RESPOND CRT) Trial. American Heart Journal, 167(4), 429-436. doi:10.1016/j.ahj.2013.12.007Cano, O., Osca, J., Sancho-Tello, M.-J., Sánchez, J. M., Ortiz, V., Castro, J. E., … Olagüe, J. (2010). Comparison of Effectiveness of Right Ventricular Septal Pacing Versus Right Ventricular Apical Pacing. The American Journal of Cardiology, 105(10), 1426-1432. doi:10.1016/j.amjcard.2010.01.004Cazeau, S., Leclercq, C., Lavergne, T., Walker, S., Varma, C., Linde, C., … Daubert, J.-C. (2001). Effects of Multisite Biventricular Pacing in Patients with Heart Failure and Intraventricular Conduction Delay. New England Journal of Medicine, 344(12), 873-880. doi:10.1056/nejm200103223441202Chung, E. S., Leon, A. R., Tavazzi, L., Sun, J.-P., Nihoyannopoulos, P., Merlino, J., … Murillo, J. (2008). Results of the Predictors of Response to CRT (PROSPECT) Trial. Circulation, 117(20), 2608-2616. doi:10.1161/circulationaha.107.743120ClelandJ. G. F. DaubertJ.-C. ErdmannE. FreemantleN. GrasD. KappenbergerL. The Effect of Cardiac Resynchronization on Morbidity and Mortality in Heart Failure.2005Coppola, G., Ciaramitaro, G., Stabile, G., DOnofrio, A., Palmisano, P., Carità, P., … Corrado, E. (2016). Magnitude of QRS duration reduction after biventricular pacing identifies responders to cardiac resynchronization therapy. International Journal of Cardiology, 221, 450-455. doi:10.1016/j.ijcard.2016.06.203Coronel, R., Wilders, R., Verkerk, A. O., Wiegerinck, R. F., Benoist, D., & Bernus, O. (2013). Electrophysiological changes in heart failure and their implications for arrhythmogenesis. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1832(12), 2432-2441. doi:10.1016/j.bbadis.2013.04.002Crozier, A., Blazevic, B., Lamata, P., Plank, G., Ginks, M., Duckett, S., … Niederer, S. A. (2016). The relative role of patient physiology and device optimisation in cardiac resynchronisation therapy: A computational modelling study. Journal of Molecular and Cellular Cardiology, 96, 93-100. doi:10.1016/j.yjmcc.2015.10.026Da Costa, A., Gabriel, L., Romeyer-Bouchard, C., Géraldine, B., Gate-Martinet, A., Laurence, B., … Isaaz, K. (2013). Focus on right ventricular outflow tract septal pacing. Archives of Cardiovascular Diseases, 106(6-7), 394-403. doi:10.1016/j.acvd.2012.08.005De Pooter, J., El Haddad, M., Timmers, L., Van Heuverswyn, F., Jordaens, L., Duytschaever, M., & Stroobandt, R. (2015). Different Methods to Measure QRS Duration in CRT Patients: Impact on the Predictive Value of QRS Duration Parameters. Annals of Noninvasive Electrocardiology, 21(3), 305-315. doi:10.1111/anec.12313Derval, N., Steendijk, P., Gula, L. J., Deplagne, A., Laborderie, J., Sacher, F., … Jaïs, P. (2010). Optimizing Hemodynamics in Heart Failure Patients by Systematic Screening of Left Ventricular Pacing Sites. Journal of the American College of Cardiology, 55(6), 566-575. doi:10.1016/j.jacc.2009.08.045DeskR. WilliamsL. HealthK. Characteristics and Distribution of M Cells in Arterially.1998Dou, J., Xia, L., Deng, D., Zang, Y., Shou, G., Bustos, C., … Crozier, S. (2012). A Study of Mechanical Optimization Strategy for Cardiac Resynchronization Therapy Based on an Electromechanical Model. Computational and Mathematical Methods in Medicine, 2012, 1-13. doi:10.1155/2012/948781DURRER, D., VAN DAM, R. T., FREUD, G. E., JANSE, M. J., MEIJLER, F. L., & ARZBAECHER, R. C. (1970). Total Excitation of the Isolated Human Heart. Circulation, 41(6), 899-912. doi:10.1161/01.cir.41.6.899Dutta, S., Mincholé, A., Quinn, T. A., & Rodriguez, B. (2017). Electrophysiological properties of computational human ventricular cell action potential models under acute ischemic conditions. Progress in Biophysics and Molecular Biology, 129, 40-52. doi:10.1016/j.pbiomolbio.2017.02.007Elhakam Elzoghby, I. A., Attia, I., Azab, A. E., & Hammouda, M. (2017). Impact of Cardiac Resynchronization Therapy on Heart Failure Patients: Experience from One Center. Archives of Medicine, 09(04). doi:10.21767/1989-5216.1000232Ferrer, 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.0141573FLEVARI, P., LEFTHERIOTIS, D., FOUNTOULAKI, K., PANOU, F., RIGOPOULOS, A. G., PARASKEVAIDIS, I., & KREMASTINOS, D. T. (2009). Long-Term Nonoutflow Septal Versus Apical Right Ventricular Pacing: Relation to Left Ventricular Dyssynchrony. Pacing and Clinical Electrophysiology, 32(3), 354-362. doi:10.1111/j.1540-8159.2008.02244.xGRAS, D., GUPTA, M. S., BOULOGNE, E., GUZZO, L., & ABRAHAM, W. T. (2009). Optimization of AV and VV Delays in the Real-World CRT Patient Population: An International Survey on Current Clinical Practice. Pacing and Clinical Electrophysiology, 32, S236-S239. doi:10.1111/j.1540-8159.2008.02294.xGreenbaum, R. A., Ho, S. Y., Gibson, D. G., Becker, A. E., & Anderson, R. H. (1981). Left ventricular fibre architecture in man. Heart, 45(3), 248-263. doi:10.1136/hrt.45.3.248Guo, T., Li, R., Zhang, L., Luo, Z., Zhao, L., Yang, J., … Hua, B. (2015). Biventricular Pacing With Ventricular Fusion by Intrinsic Activation in Cardiac Resynchronization Therapy. International Heart Journal, 56(3), 293-297. doi:10.1536/ihj.14-260Gurev, V., Constantino, J., Rice, J. J., & Trayanova, N. A. (2010). Distribution of Electromechanical Delay in the Heart: Insights from a Three-Dimensional Electromechanical Model. Biophysical Journal, 99(3), 745-754. doi:10.1016/j.bpj.2010.05.028Heidenreich, 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/s10439-010-9997-2Huang, W., Su, L., Wu, S., Xu, L., Xiao, F., Zhou, X., & Ellenbogen, K. A. (2017). A Novel Pacing Strategy With Low and Stable Output: Pacing the Left Bundle Branch Immediately Beyond the Conduction Block. Canadian Journal of Cardiology, 33(12), 1736.e1-1736.e3. doi:10.1016/j.cjca.2017.09.013Keller, 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.2010.2046485Krum, H., Lemke, B., Birnie, D., Lee, K. L.-F., Aonuma, K., Starling, R. C., … Martin, D. (2012). A novel algorithm for individualized cardiac resynchronization therapy: Rationale and design of the adaptive cardiac resynchronization therapy trial. American Heart Journal, 163(5), 747-752.e1. doi:10.1016/j.ahj.2012.02.007Leclercq, C., Sadoul, N., Mont, L., Defaye, P., Osca, J., Mouton, E., … Fernandez-Lozano, I. (2015). Comparison of right ventricular septal pacing and right ventricular apical pacing in patients receiving cardiac resynchronization therapy defibrillators: the SEPTAL CRT Study. European Heart Journal, 37(5), 473-483. doi:10.1093/eurheartj/ehv422LEE, A. W. C., CROZIER, A., HYDE, E. R., LAMATA, P., TRUONG, M., SOHAL, M., … NIEDERER, S. (2017). Biophysical Modeling to Determine the Optimization of Left Ventricular Pacing Site and AV/VV Delays in the Acute and Chronic Phase of Cardiac Resynchronization Therapy. Journal of Cardiovascular Electrophysiology, 28(2), 208-215. doi:10.1111/jce.13134Lee, A. W. C., Costa, C. M., Strocchi, M., Rinaldi, C. A., & Niederer, S. A. (2018). Computational Modeling for Cardiac Resynchronization Therapy. Journal of Cardiovascular Translational Research, 11(2), 92-108. doi:10.1007/s12265-017-9779-4Lee, H., Park, J.-H., Seo, I., Park, S.-H., & Kim, S. (2014). Improved application of the electrophoretic tissue clearing technology, CLARITY, to intact solid organs including brain, pancreas, liver, kidney, lung, and intestine. BMC Developmental Biology, 14(1). doi:10.1186/s12861-014-0048-3Linde, C., Ellenbogen, K., & McAlister, F. A. (2012). Cardiac resynchronization therapy (CRT): Clinical trials, guidelines, and target populations. Heart Rhythm, 9(8), S3-S13. doi:10.1016/j.hrthm.2012.04.026Lopez-Perez, A., Sebastian, R., & Ferrero, J. M. (2015). Three-dimensional cardiac computational modelling: methods, features and applications. BioMedical Engineering OnLine, 14(1). doi:10.1186/s12938-015-0033-5Mafi Rad, M., Blaauw, Y., Dinh, T., Pison, L., Crijns, H. J., Prinzen, F. W., & Vernooy, K. (2014). Different regions of latest electrical activation during left bundle-branch block and right ventricular pacing in cardiac resynchronization therapy patients determined by coronary venous electro-anatomic mapping. European Journal of Heart Failure, 16(11), 1214-1222. doi:10.1002/ejhf.178Miri, R., Graf, I. M., & Dossel, O. (2009). Efficiency of Timing Delays and Electrode Positions in Optimization of Biventricular Pacing: A Simulation Study. IEEE Transactions on Biomedical Engineering, 56(11), 2573-2582. doi:10.1109/tbme.2009.2027692Miri, R., Reumann, M., Farina, D., & Dössel, O. (2009). Concurrent optimization of timing delays and electrode positioning in biventricular pacing based on a computer heart model assuming 17 left ventricular segments. Biomedizinische Technik/Biomedical Engineering, 54(2), 55-65. doi:10.1515/bmt.2009.013Miri, R., Reumann, M., Keller, D. U. J., Farina, D., & Dossel, O. (2008). Comparison of the electrophysiologically based optimization methods with different pacing parameters in patient undergoing resynchronization treatment. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2008.4649513MOLHOEK, S. G., BAX, J. J., BOERSMA, E., ERVEN, L. V., BOOTSMA, M., STEENDIJK, P., … SCHALIJ, M. J. (2004). QRS Duration and Shortening to Predict Clinical Response to Cardiac Resynchronization Therapy in Patients with End‐Stage Heart Failure. Pacing and Clinical Electrophysiology, 27(3), 308-313. doi:10.1111/j.1540-8159.2004.00433.xMora, M. T., Ferrero, J. M., Romero, L., & Trenor, B. (2017). Sensitivity analysis revealing the effect of modulating ionic mechanisms on calcium dynamics in simulated human heart failure. PLOS ONE, 12(11), e0187739. doi:10.1371/journal.pone.0187739MUTO, C., OTTAVIANO, L., CANCIELLO, M., CARRERAS, G., CALVANESE, R., ASCIONE, L., … TUCCILLO, B. (2007). Effect of Pacing the Right Ventricular Mid-Septum Tract in Patients with Permanent Atrial Fibrillation and Low Ejection Fraction. Journal of Cardiovascular Electrophysiology, 18(10), 1032-1036. doi:10.1111/j.1540-8167.2007.00914.xO’Hara, T., Virág, L., Varró, A., & Rudy, Y. (2011). Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLoS Computational Biology, 7(5), e1002061. doi:10.1371/journal.pcbi.1002061Passini, E., Mincholé, A., Coppini, R., Cerbai, E., Rodriguez, B., Severi, S., & Bueno-Orovio, A. (2016). Mechanisms of pro-arrhythmic abnormalities in ventricular repolarisation and anti-arrhythmic therapies in human hypertrophic cardiomyopathy. Journal of Molecular and Cellular Cardiology, 96, 72-81. doi:10.1016/j.yjmcc.2015.09.003Pitzalis, M. V., Iacoviello, M., Romito, R., Massari, F., Rizzon, B., Luzzi, G., … Rizzon, P. (2002). Cardiac resynchronization therapy tailored by echocardiographic evaluation of ventricular asynchrony. Journal of the American College of Cardiology, 40(9), 1615-1622. doi:10.1016/s0735-1097(02)02337-9Pluijmert, M., Bovendeerd, P. H. M., Lumens, J., Vernooy, K., Prinzen, F. W., & Delhaas, T. (2016). New insights from a computational model on the relation between pacing site and CRT response. EP Europace, 18(suppl_4), iv94-iv103. doi:10.1093/europace/euw355Potse, M., Dube, B., Richer, J., Vinet, A., & Gulrajani, R. M. (2006). A Comparison of Monodomain and Bidomain Reaction-Diffusion Models for Action Potential Propagation in the Human Heart. IEEE Transactions on Biomedical Engineering, 53(12), 2425-2435. doi:10.1109/tbme.2006.880875Potse, M., Krause, D., Bacharova, L., Krause, R., Prinzen, F. W., & Auricchio, A. (2012). Similarities and differences between electrocardiogram signs of left bundle-branch block and left-ventricular uncoupling. Europace, 14(suppl 5), v33-v39. doi:10.1093/europace/eus272Prassl, A. J., Kickinger, F., Ahammer, H., Grau, V., Schneider, J. E., Hofer, E., … Plank, G. (2009). Automatically Generated, Anatomically Accurate Meshes for Cardiac Electrophysiology Problems. IEEE Transactions on Biomedical Engineering, 56(5), 1318-1330. doi:10.1109/tbme.2009.2014243PRINZEN, F. W., & PESCHAR, M. (2002). Relation Between the Pacing Induced Sequence of Activation and Left Ventricular Pump Function in Animals. Pacing and Clinical Electrophysiology, 25(4), 484-498. doi:10.1046/j.1460-9592.2002.00484.xVan Deursen, C., van Geldorp, I. E., Rademakers, L. M., van Hunnik, A., Kuiper, M., Klersy, C., … Prinzen, F. W. (2009). Left Ventricular Endocardial Pacing Improves Resynchronization Therapy in Canine Left Bundle-Branch Hearts. Circulation: Arrhythmia and Electrophysiology, 2(5), 580-587. doi:10.1161/circep.108.846022Prinzen, F. W., Vernooy, K., Lumens, J., & Auricchio, A. (2017). Physiology of Cardiac Pacing and Resynchronization. Clinical Cardiac Pacing, Defibrillation and Resynchronization Therapy, 213-248. doi:10.1016/b978-0-323-37804-8.00007-9Rickard, J., Karim, M., Baranowski, B., Cantillon, D., Spragg, D., Tang, W. H. W., … Varma, N. (2017). Effect of PR interval prolongation on long-term outcomes in patients with left bundle branch block vs non–left bundle branch block morphologies undergoing cardiac resynchronization therapy. Heart Rhythm, 14(10), 1523-1528. doi:10.1016/j.hrthm.2017.05.028Romero, D., Sebastian, R., Bijnens, B. H., Zimmerman, V., Boyle, P. M., Vigmond, E. J., & Frangi, A. F. (2010). Effects of the Purkinje

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at √s = 7 TeV with the ATLAS detector

This paper presents a measurement of the cross-section for high transverse momentum W and Z bosons produced in pp collisions and decaying to all-hadronic final states. The data used in the analysis were recorded by the ATLAS detector at the CERN Large Hadron Collider at a centre-of-mass energy of √s = 7 TeV;{\rm Te}{\rm V}$and correspond to an integrated luminosity of$4.6\;{\rm f}{{{\rm b}}^{-1}}$. The measurement is performed by reconstructing the boosted W or Z bosons in single jets. The reconstructed jet mass is used to identify the W and Z bosons, and a jet substructure method based on energy cluster information in the jet centre-of-mass frame is used to suppress the large multi-jet background. The cross-section for events with a hadronically decaying W or Z boson, with transverse momentum${{p}_{{\rm T}}}\gt 320\;{\rm Ge}{\rm V}$and pseudorapidity$|\eta |\lt 1.9$, is measured to be${{\sigma }_{W+Z}}=8.5\pm 1.7$ pb and is compared to next-to-leading-order calculations. The selected events are further used to study jet grooming techniques

Search for pair-produced long-lived neutral particles decaying to jets in the ATLAS hadronic calorimeter in ppcollisions at √s=8TeV

The ATLAS detector at the Large Hadron Collider at CERN is used to search for the decay of a scalar boson to a pair of long-lived particles, neutral under the Standard Model gauge group, in 20.3fb−1of data collected in proton–proton collisions at √s=8TeV. This search is sensitive to long-lived particles that decay to Standard Model particles producing jets at the outer edge of the ATLAS electromagnetic calorimeter or inside the hadronic calorimeter. No significant excess of events is observed. Limits are reported on the product of the scalar boson production cross section times branching ratio into long-lived neutral particles as a function of the proper lifetime of the particles. Limits are reported for boson masses from 100 GeVto 900 GeV, and a long-lived neutral particle mass from 10 GeVto 150 GeV

Search for direct pair production of the top squark in all-hadronic final states in proton-proton collisions at s√=8 TeV with the ATLAS detector

The results of a search for direct pair production of the scalar partner to the top quark using an integrated luminosity of 20.1fb−1 of proton–proton collision data at √s = 8 TeV recorded with the ATLAS detector at the LHC are reported. The top squark is assumed to decay via t˜→tχ˜01 or t˜→ bχ˜±1 →bW(∗)χ˜01 , where χ˜01 (χ˜±1 ) denotes the lightest neutralino (chargino) in supersymmetric models. The search targets a fully-hadronic final state in events with four or more jets and large missing transverse momentum. No significant excess over the Standard Model background prediction is observed, and exclusion limits are reported in terms of the top squark and neutralino masses and as a function of the branching fraction of t˜ → tχ˜01 . For a branching fraction of 100%, top squark masses in the range 270–645 GeV are excluded for χ˜01 masses below 30 GeV. For a branching fraction of 50% to either t˜ → tχ˜01 or t˜ → bχ˜±1 , and assuming the χ˜±1 mass to be twice the χ˜01 mass, top squark masses in the range 250–550 GeV are excluded for χ˜01 masses below 60 GeV

Search for R-parity-violating supersymmetry in events with four or more leptons in sqrt(s) =7 TeV pp collisions with the ATLAS detector

A search for new phenomena in final states with four or more leptons (electrons or muons) is presented. The analysis is based on 4.7 fb−1 of $\sqrt{s}=7\;\mathrm{TeV}$ proton-proton collisions delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in two signal regions: one that requires moderate values of missing transverse momentum and another that requires large effective mass. The results are interpreted in a simplified model of R-parity-violating supersymmetry in which a 95% CL exclusion region is set for charged wino masses up to 540 GeV. In an R-parity-violating MSUGRA/CMSSM model, values of m 1/2 up to 820 GeV are excluded for 10 < tan β < 40

Search for high-mass resonances decaying to dilepton final states in pp collisions at s√=7 TeV with the ATLAS detector

The ATLAS detector at the Large Hadron Collider is used to search for high-mass resonances decaying to an electron-positron pair or a muon-antimuon pair. The search is sensitive to heavy neutral Z′ gauge bosons, Randall-Sundrum gravitons, Z * bosons, techni-mesons, Kaluza-Klein Z/γ bosons, and bosons predicted by Torsion models. Results are presented based on an analysis of pp collisions at a center-of-mass energy of 7 TeV corresponding to an integrated luminosity of 4.9 fb−1 in the e + e − channel and 5.0 fb−1 in the μ + μ −channel. A Z ′ boson with Standard Model-like couplings is excluded at 95 % confidence level for masses below 2.22 TeV. A Randall-Sundrum graviton with coupling k/MPl=0.1 is excluded at 95 % confidence level for masses below 2.16 TeV. Limits on the other models are also presented, including Technicolor and Minimal Z′ Models

The impact of the initial COVID-19 outbreak on young adults’ mental health: a longitudinal study of risk and resilience factors

Few studies assessing the effects of COVID-19 on mental health include prospective markers of risk and resilience necessary to understand and mitigate the combined impacts of the pandemic, lockdowns, and other societal responses. This population-based study of young adults includes individuals from the Neuroscience in Psychiatry Network (n = 2403) recruited from English primary care services and schools in 2012–2013 when aged 14–24. Participants were followed up three times thereafter, most recently during the initial outbreak of the COVID-19 outbreak when they were aged between 19 and 34. Repeated measures of psychological distress (K6) and mental wellbeing (SWEMWBS) were supplemented at the latest assessment by clinical measures of depression (PHQ-9) and anxiety (GAD-7). A total of 1000 participants, 42% of the original cohort, returned to take part in the COVID-19 follow-up; 737 completed all four assessments [mean age (SD), 25.6 (3.2) years; 65.4% female; 79.1% White]. Our findings show that the pandemic led to pronounced deviations from existing mental health-related trajectories compared to expected levels over approximately seven years. About three-in-ten young adults reported clinically significant depression (28.8%) or anxiety (27.6%) under current NHS guidelines; two-in-ten met clinical cut-offs for both. About 9% reported levels of psychological distress likely to be associated with serious functional impairments that substantially interfere with major life activities; an increase by 3% compared to pre-pandemic levels. Deviations from personal trajectories were not necessarily restricted to conventional risk factors; however, individuals with pre-existing health conditions suffered disproportionately during the initial outbreak of the COVID-19 pandemic. Resilience factors known to support mental health, particularly in response to adverse events, were at best mildly protective of individual psychological responses to the pandemic. Our findings underline the importance of monitoring the long-term effects of the ongoing pandemic on young adults’ mental health, an age group at particular risk for the emergence of psychopathologies. Our findings further suggest that maintaining access to mental health care services during future waves, or potential new pandemics, is particularly crucial for those with pre-existing health conditions. Even though resilience factors known to support mental health were only mildly protective during the initial outbreak of the COVID-19 pandemic, it remains to be seen whether these factors facilitate mental health in the long term