Survivors of intensive care with type 2 diabetes and the effect of shared care follow-up clinics: study protocol for the SWEET-AS randomised controlled feasibility study

Published online: 13 October 2016Background: Many patients who survive the intensive care unit (ICU) experience long-term complications such as peripheral neuropathy and nephropathy which represent a major source of morbidity and affect quality of life adversely. Similar pathophysiological processes occur frequently in ambulant patients with diabetes mellitus who have never been critically ill. Some 25 % of all adult ICU patients have diabetes, and it is plausible that ICU survivors with co-existing diabetes are at heightened risk of sequelae from their critical illness. ICU follow-up clinics are being progressively implemented based on the concept that interventions provided in these clinics will alleviate the burdens of survivorship. However, there is only limited information about their outcomes. The few existing studies have utilised the expertise of healthcare professionals primarily trained in intensive care and evaluated heterogenous cohorts. A shared care model with an intensivist- and diabetologist-led clinic for ICU survivors with type 2 diabetes represents a novel targeted approach that has not been evaluated previously. Prior to undertaking any definitive study, it is essential to establish the feasibility of this intervention. Methods: This will be a prospective, randomised, parallel, open-label feasibility study. Eligible patients will be approached before ICU discharge and randomised to the intervention (attending a shared care follow-up clinic 1 month after hospital discharge) or standard care. At each clinic visit, patients will be assessed independently by both an intensivist and a diabetologist who will provide screening and targeted interventions. Six months after discharge, all patients will be assessed by blinded assessors for glycated haemoglobin, peripheral neuropathy, cardiovascular autonomic neuropathy, nephropathy, quality of life, frailty, employment and healthcare utilisation. The primary outcome of this study will be the recruitment and retention at 6 months of all eligible patients. Discussion: This study will provide preliminary data about the potential effects of critical illness on chronic glucose metabolism, the prevalence of microvascular complications, and the impact on healthcare utilisation and quality of life in intensive care survivors with type 2 diabetes. If feasibility is established and point estimates are indicative of benefit, funding will be sought for a larger, multi-centre study. Trial registration: ANZCTR ACTRN12616000206426Yasmine Ali Abdelhamid, Liza Phillips, Michael Horowitz and Adam Dean

Cisplatin, gemcitabine, and treosulfan in relapsed stage IV cutaneous malignant melanoma patients

To evaluate the efficacy of cisplatin, gemcitabine, and treosulfan (CGT) in 91 patients with pretreated relapsed AJCC stage IV cutaneous malignant melanoma. Patients in relapse after first-, second-, or third-line therapy received 40 mg m−2 intravenous (i.v.) cisplatin, 1000 mg m−2 i.v. gemcitabine, and 2500 mg m−2 i.v. treosulfan on days 1 and 8. Cisplatin, gemcitabine, and treosulfan therapy was repeated every 5 weeks until progression of disease occurred. A maximum of 11 CGT cycles (mean, two cycles) was administered per patient. Four patients (4%) showed a partial response; 15 (17%) patients had stable disease; and 72 (79%) patients progressed upon first re-evaluation. Overall survival of all 91 patients was 6 months (2-year survival rate, 7%). Patients with partial remission or stable disease exhibited a median overall survival of 11 months (2-year survival rate, 36%), while patients with disease progression upon first re-evaluation had a median overall survival of 5 months (2-year survival rate, 0%). Treatment with CGT was efficient in one-fifth of the pretreated relapsed stage IV melanoma patients achieving disease stabilisation or partial remission with prolonged but limited survival

Antiretroviral Therapy Outcomes in HIV-Infected Children after Adjusting Protease Inhibitor Dosing during Tuberculosis Treatment

Modification of ritonavir-boosted lopinavir (LPV/r)-based antiretroviral therapy is required for HIV-infected children co-treated for tuberculosis (TB). We aimed to determine virologic and toxicity outcomes among TB/HIV co-treated children with the following modifications to their antiretroviral therapy (ART): (1) super-boosted LPV/r, (2) double-dose LPV/r or (3) ritonavir.A medical record review was conducted at two clinical sites in Johannesburg, South Africa. The records of children 6-24 months of age initiating LPV/r-based therapy were reviewed. Children co-treated for TB were categorized based on the modifications made to their ART regimen and were compared to children of the same age at each site not treated for TB. Included are 526 children, 294 (56%) co-treated for TB. All co-treated children had more severe HIV disease, including lower CD4 percents and worse growth indicators, than comparisons. Children in the super-boosted group (n = 156) were as likely to be virally suppressed (<400 copies/ml) at 6 months as comparisons (69.2% vs. 74.8%, p = 0.36). Children in the double-dose (n = 47) and ritonavir groups (n = 91) were significantly less likely to be virally suppressed at 6 months (53.1% and 49.3%) than comparisons (74.8% and 82.1%; p = 0.02 and p<0.0001, respectively). At 12 months only children in the ritonavir group still had lower rates of virological suppression relative to comparisons (63.9% vs 83.3% p<0.05). Grade 1 or greater ALT elevations were more common in the super-boosted (75%) than double-dose (54.6%) or ritonavir (33.9%) groups (p = 0.09 and p<0.0001) but grade 3/4 elevations were observed in 3 (13.6%) of the super-boosted, 7 (15.9%) of the double-dose and 5 (8.9%) of the ritonavir group (p = 0.81 and p = 0.29).Good short-term virologic outcomes were achieved in children co-treated for TB and HIV who received super-boosted LPV/r. Treatment limiting toxicity was rare. Strategies for increased dosing of LPV/r with TB treatment warrant further investigation

ApoB100/LDLR-/- Hypercholesterolaemic Mice as a Model for Mild Cognitive Impairment and Neuronal Damage

Recent clinical findings support the notion that the progressive deterioration of cholesterol homeostasis is a central player in Alzheimer's disease (AD). Epidemiological studies suggest that high midlife plasma total cholesterol levels are associated with an increased risk of AD. This paper reports the plasma cholesterol concentrations, cognitive performance, locomotor activity and neuropathological signs in a murine model (transgenic mice expressing apoB100 but knockout for the LDL receptor [LDLR]) of human familial hypercholesterolaemia (FH). From birth, these animals have markedly elevated LDL-cholesterol and apolipoprotein B100 (apoB100) levels. These transgenic mice were confirmed to have higher plasma cholesterol concentrations than wild-type mice, an effect potentiated by aging. Further, 3-month-old transgenic mice showed cholesterol (total and fractions) concentrations considerably higher than those of 18-month-old wild-type mice. The hypercholesterolaemia of the transgenic mice was associated with a clear locomotor deficit (as determined by rotarod, grip strength and open field testing) and impairment of the episodic-like memory (determined by the integrated memory test). This decline in locomotor activity and cognitive status was associated with neuritic dystrophy and/or the disorganization of the neuronal microtubule network, plus an increase in astrogliosis and lipid peroxidation in the brain regions associated with AD, such as the motor and lateral entorhinal cortex, the amygdaloid basal nucleus, and the hippocampus. Aortic atherosclerotic lesions were positively correlated with age, although potentiated by the transgenic genotype, while cerebral β-amyloidosis was positively correlated with genetic background rather than with age. These findings confirm hypercholesterolaemia as a key biomarker for monitoring mild cognitive impairment, and shows these transgenic mice can be used as a model for cognitive and psycho-motor decline

Patterns of genetic diversity in southern and southeastern Araucaria angustifolia (Bert.) O. Kuntze relict populations

Habitat fragmentation and a decrease in population size may lead to a loss in population genetic diversity. For the first time, the reduction in genetic diversity in the northernmost limit of natural occurence (southeastern Brazil) of Araucaria angustifolia in comparison with populations in the main area of the species continuous natural distribution (southern Brazil), was tested. The 673 AFLPs markers revealed a high level of genetic diversity for the species (Ht = 0.27), despite anthropogenic influence throughout the last century, and a decrease of H in isolated populations of southeastern Brazil (H = 0.16), thereby indicating the tendency for higher genetic diversity in remnant populations of continuous forests in southern Brazil, when compared to natural isolated populations in the southeastern region. A strong differentiation among southern and southeastern populations was detected (AMOVA variance ranged from 10%-15%). From Bayesian analysis, it is suggested that the nine populations tested form five “genetic clusters” (K = 5). Five of these populations, located in the northernmost limit of distribution of the species, represent three “genetic clusters”. These results are in agreement with the pattern of geographic distribution of the studied populations

Multiphysics and Thermodynamic Formulations for Equilibrium and Non-equilibrium Interactions: Non-linear Finite Elements Applied to Multi-coupled Active Materials

[EN] Combining several theories this paper presents a general multiphysics framework applied to the study of coupled and active materials, considering mechanical, electric, magnetic and thermal fields. The framework is based on thermodynamic equilibrium and non-equilibrium interactions, both linked by a two-temperature model. The multi-coupled governing equations are obtained from energy, momentum and entropy balances; the total energy is the sum of thermal, mechanical and electromagnetic parts. The momentum balance considers mechanical plus electromagnetic balances; for the latter the Abraham rep- resentation using the Maxwell stress tensor is formulated. This tensor is manipulated to automatically fulfill the angular momentum balance. The entropy balance is for- mulated using the classical Gibbs equation for equilibrium interactions and non-equilibrium thermodynamics. For the non-linear finite element formulations, this equation requires the transformation of thermoelectric coupling and conductivities into tensorial form. The two-way thermoe- lastic Biot term introduces damping: thermomechanical, pyromagnetic and pyroelectric converse electromagnetic dynamic interactions. Ponderomotrix and electromagnetic forces are also considered. The governing equations are converted into a variational formulation with the resulting four-field, multi-coupled formalism implemented and val- idated with two custom-made finite elements in the research code FEAP. Standard first-order isoparametric eight-node elements with seven degrees of freedom (dof) per node (three displacements, voltage and magnetic scalar potentials plus two temperatures) are used. Non-linearities and dynamics are solved with Newton-Raphson and New- mark-b algorithms, respectively. Results of thermoelectric, thermoelastic, thermomagnetic, piezoelectric, piezomag- netic, pyroelectric, pyromagnetic and galvanomagnetic interactions are presented, including non-linear depen- dency on temperature and some second-order interactions.This research was partially supported by grants CSD2008-00037 Canfranc Underground Physics, Polytechnic University of Valencia under programs PAID 02-11-1828 and 05-10-2674. The first author used the grant Generalitat Valenciana BEST/2014/232 for the completion of this work.Pérez-Aparicio, JL.; Palma, R.; Taylor, R. (2016). Multiphysics and Thermodynamic Formulations for Equilibrium and Non-equilibrium Interactions: Non-linear Finite Elements Applied to Multi-coupled Active Materials. Archives of Computational Methods in Engineering. 23:535-583. https://doi.org/10.1007/s11831-015-9149-9S53558323Abraham M (1910) Sull’elettrodinamica di Minkowski. Rend Circ Mat 30:33–46Allik H, Hughes TJR (1970) Finite elment method for piezoelectric vibration. Int J Numer Methods Eng 2:151–157Antonova EE, Looman DC (2005) Finite elements for thermoelectric device analysis in ANSYS. In: International conference on thermoelectricsAtulasimha J, Flatau AB (2011) A review of magnetostrictive iron–gallium alloys. Smart Mater Struct 20:1–15Ballato A (1995) Piezoelectricity: old effect, new thrusts. IEEE Trans Ultrason Ferroelectr Freq Control 42(5):916–926Baoyuan S, Jiantong W, Jun Z, Min Q (2003) A new model describing physical effects in crystals: the diagrammatic and analytic methods for macro-phenomenological theory. J Mater Process Technol 139:444–447Bargmann S, Steinmann P (2005) Finite element approaches to non-classical heat conduction in solids. Comput Model Eng Sci 9(2):133–150Bargmann S, Steinmann P (2006) Theoretical and computational aspects of non-classical thermoelasticity. Comput Methods Appl Mech Eng 196:516–527Bargmann S, Steinmann P (2008) Modeling and simulation of first and second sound in solids. Int J Solids Struct 45:6067–6073Barnett SM (2010) Resolution of the Abraham–Minkowski dilemma. Phys Rev Lett 104:070401Benbouzid MH, Meunier G, Meunier G (1995) Dynamic modelling of giant magnetostriction in Terfenol-D rods by the finite element method. IEEE Trans Magn 31(3):1821–1824Benbouzid MH, Reyne G, Meunier G (1993) Nonlinear finite element modelling of giant magnetostriction. IEEE Trans Magn 29(6):2467–2469Benbouzid MH, Reyne G, Meunier G (1995) Finite elment modelling of magnetostrictive devices: investigations for the design of the magnetic circuit. IEEE Trans Magn 31(3):1813–1816Besbes M, Ren Z, Razek A (1996) Finite element analysis of magneto-mechanical coupled phenomena in magnetostrictive materials. IEEE Trans Magn 32(3):1058–1061Biot MA (1956) Thermoelasticity and irreversible thermodynamics. J Appl Phys 27(3):240–253Bisio G, Cartesegna M, Rubatto G (2001) Thermodynamic analysis of elastic systems. Energy Convers Manag 42:799–812Blun SL (1974) Materials for radiation detection. National Academy of Sciences, WashingtonBonet J, Wood RD (1997) Nonlinear continuum mechanics for finite element analysis. Cambridge University Press, CambridgeBorovik-Romanov AS (1960) Piezomagnetism in the antiferromagnetic fluorides of cobalt and manganese. Sov Phys 11:786Bowyer P (2005) The momentum of light in media: the Abraham–Minkowski controversy. http://bit.ly/1M7wyATBrauer JR, Ruehl JJ, MacNeal BE, Hirtenfelder F (1995) Finite element analysis of Hall effect and magnetoresistance. IEEE Trans Electron Devices 42(2):328–333Bustamante R, Dorfmann A, Ogden RW (2009) On electric body forces and Maxwell stresses in nonlinearly electroelastic solids. Int J Eng Sci 47:1131–1141Callen HB (1948) The application of Onsager’s reciprocal relations to thermoelectric, thermomagnetic, and galvanomagnetic effects. Phys Rev 73(11):1349–1358Callen HB (1985) Thermodynamics and an introduction to thermostatistics. Wiley, New YorkCarter JP, Booker JR (1989) Finite element analysis of coupled thermoelasticity. Comput Struct 31(1):73–80Cattaneo C (1938) Sulla conduzione del calore. Atti Semin Mat Fis Univ Modena 3:83–1013Chaplik AV (2000) Some exact solutions for the classical Hall effect in an inhomogeneous magnetic field. JETP Lett 72:503Chen PJ, Gurtin ME (1968) On a theory of heat conduction involving two temperatures. J Z Angew Math Phys ZAMP 19(4):614–627Chu LJ, Haus HA, Penfield P (1966) The force density in polarizable and magnetizable fluids. In: Proceedings of the IEEEClin Th, Turenne S, Vasilevskiy D, Masut RA (2009) Numerical simulation of the thermomechanical behavior of extruded bismuth telluride alloy module. J Electron Mater 38(7):994–1001Coleman BD (1964) Thermodynamics of materials with memory. Arch Ration Mech Anal 17:1–46de Groot SR (1961) Non-equilibrium themodynamics of systems in an electromagnetic field. J Nucl Energy C Plasma Phys 2:188–194de Groot SR, Mazur P (1984) Non-equilibrium thermodynamics. Dover, MineolaDebye P (1913) On the theory of anomalous dispersion in the region of long-wave electromagnetic radiation. Verh dtsch phys Ges 15:777–793del Castillo LF, García-Colín LS (1986) Thermodynamic basis for dielectric relaxation in complex materials. Phys Rev B 33(7):4944–4951Delves RT (1964) Figure of merit for Ettingshausen cooling. Br J Appl Phys 15:105–106Dorf RC (1997) The electrical engineering handbook. CRC Press, UKEarle R, Richards JFC (1956) Theophrastus: on stones. Ohio State University, ColumbusEbling D, Jaegle M, Bartel M, Jacquot A, Bottner H (2009) Multiphysics simulation of thermoelectric systems for comparison with experimental device performance. J Electron Mater 38(7):1456–1461El-Karamany AS, Ezzat MA (2011) On the two-temperature Green–Naghdi thermoelasticity theories. J Therm Stress 34:1207–1226Eringen AC (1980) Mechanics of continua. Robert E Krieger, MalabarEringen AC, Maugin GA (1990) Electrodynamics of continua I. Springer, New YorkErsoy Y (1984) A new nonlinear constitutive theory for conducting magnetothermoelastic solids. Int J Eng Sci 22(6):683–705Ersoy Y (1986) A new nonlinear constitutive theory of electric and heat conductions for magnetoelastothermo-electrical anisotropic solids. Int J Eng Sci 24(6):867–882Ferrari A, Mittica A (2013) Thermodynamic formulation of the constitutive equations for solids and fluids. Energy Convers Manag 66:77–86Galushko D, Ermakov N, Karpovski M, Palevski A, Ishay JS, Bergman DJ (2005) Electrical, thermoelectric and thermophysical properties of hornet cuticle. Semicond Sci Technol 20:286–289Gao JL, Du QG, Zhang XD, Jiang XQ (2011) Thermal stress analysis and structure parameter selection for a Bi2Te3-based thermoelectric module. J Electron Mater 40(5):884–888Gaudenzi P, Bathe KJ (1995) An iterative finite element procedure for the analysis of piezoelectric continua. J Intell Mater Syst Struct 6:266–273Gavela D, Pérez-Aparicio JL (1998) Peltier pellet analysis with a coupled, non-linear 3D finite element model. In: 4th European workshop on thermoelectricsGoudreau GL, Taylor RL (1972) Evaluation of numerical integration methods in elastodynamics. Comput Methods Appl Mech Eng 2:69–97Griffiths DJ (1999) Introduction to electrodynamics. Prentice-Hall Inc, Upper Saddle RiverGros L, Reyne G, Body C, Meunier G (1998) Strong coupling magneto mechanical methods applied to model heavy magnetostrictive actuators. IEEE Trans Magn 34(5):3150–3153Gurtin ME, Williams WO (1966) On the Clausius–Duhem inequality. J Z Angew Math Phys ZAMP 17(5):626–633Hamader VM, Patil TA, Chovan SH (1987) Free vibration response of two-dimensional magneto-electro-elastic laminated plates. Build Mater Sci 9:249–253Hausler C, Milde G, Balke H, Bahr HA, Gerlach G (2001) 3-D modeling of pyroelectric sensor arrays part I: multiphysics finite-element simulation. IEEE Sens J 8(12):2080–2087He Y (2004) Heat capacity, thermal conductivity and thermal expansion of barium titanate-based ceramics. Thermochimica 419:135–141Hernández-Lemus E, Orgaz E (2002) Hysteresis in nonequilibrium steady states: the role of dissipative couplings. Rev Mex Fís 48:38–45Hinds EA (2009) Momentum exchange between light and a single atom: Abraham or Minkowski? Phys Rev Lett 102:050403Hirsinger L, Billardon R (1995) Magneto-elastic finite element analysis including magnetic forces and magnetostriction effects. IEEE Trans Magn 31(3):1877–1880Huang MJ, Chou PK, Lin MC (2008) An investigation of the thermal stresses induced in a thin-film thermoelectric cooler. J Therm Stress 31:438–454IEEE Standards Board (1988) IEEE standard on piezoelectricity. ANSI/IEEE Std 176-1987. doi: 10.1109/IEEESTD.1988.79638IEEE Standards Board (1991) IEEE standard on magnetostrictive materials: piezomagnetic nomenclature. IEEE Std 319-1990. doi: 10.1109/IEEESTD.1991.101048Ioffe Institute (2013) INSb—indium antimonide. Ioffe Institute. www.ioffe.rssi.ru/SVA/NSM/Semicond/InSb/index.htmlJackson JD (1962) Classical electrodynamics. Wiley, New YorkJaegle M (2008) Multiphysics simulation of thermoelectric systems—modeling of Peltier—cooling and thermoelectric generation. In: Proceedings of the COMSOLJaegle M, Bartel M, Ebling D, Jacquot A, Bottner H (2008) Multiphysics simulation of thermoelectric systems. In: European conference on thermoelectrics ECT2008Jiménez JL, Campos I (1996) Advanced electromagnetism: foundations, theory and applications, chapter The balance equations of energy and momentum in classical electrodynamics. World Scientific Publishing, SingaporeJohnstone S (2008) Is there potential for use of the Hall effect in analytical science? Analyst 133:293–296Jou D, Lebon G (1996) Extended irreversible thermodynamics. Springer, BerlinKaltenbacher M, Kaltenbacher B, Hegewald T, Lerch R (2010) Finite element formulation for ferroelectric hysteresis of piezoelectric materials. J Intell Mater Syst Struct 21:773–785Kaltenbacher M, Meiler M, Ertl M (2009) Physical modeling and numerical computation of magnetostriction. Int J Comput Math Electr Electron Eng 28(4):819–832Kamlah M, Bohle U (2001) Finite element analysis of piezoceramic components taking into account ferroelectric hysteresis behavior. Int J Solids Struct 38:605–633Kannan KS, Dasgupta A (1997) A nonlinear Galerkin finite-element theory for modeling magnetostrictive smart structures. Smart Mater Struct 6:341–350Kiang J, Tong L (2010) Nonlinear magneto-mechanical finite element analysis of Ni–Mn–Ga single crystals. Smart Mater Struct 19:1–17Kinsler P, Favaro A, McCall MW (2009) Four Poynting theorems. Eur J Phys 30:983–993Klinckel S, Linnemann K (2008) A phenomenological constitutive model for magnetostrictive materials and ferroelectric ceramics. Proc Appl Math Mech 8:10507–10508Kosmeier D (2013) Hornets: Gentle Giants! Wikipedia: the free encyclopedia. www.hornissenschutz.de/hornets.htmLahmer T (2008) Forward and inverse problems in piezoelectricity. PhD thesis, Universität Erlangen-NürnbergLandau LD, Lifshitz EM (1982) Mechanics. Butterworth-Heinemann, OxfordLandau LD, Lifshitz EM (1984) Electrodynamics of continuous media. Pergamon Press, OxfordLandis CM (2002) A new finite-element formulation for electromechanical boundary value problems. Int J Numer Methods Eng 55:613–628Díaz Lantada A (2011) Handbook of active materials for medical devices: advances and applications. CRC Press, Boca RatonLebon G, Jou D, Casas-Vázquez J (2008) Understanding non-equilibrium thermodynamics. Springer, BerlinLinnemann K, Klinkel S (2006) A constitutive model for magnetostrictive materials—theory and finite element implementation. Proc Appl Math Mech 6:393–394Linnemann K, Klinkel S, Wagner W (2009) A constitutive model for magnetostrictive and piezoelectric materials. Int J Solids Struct 46:1149–1166Llebot JE, Jou D, Casas-Vázquez J (1983) A thermodynamic approach to heat and electric conduction in solids. Physica 121(A):552–562Lu X, Hanagud V (2004) Extended irreversible thermodynamics modeling for self-heating and dissipation in piezoelectric ceramics. IEEE Trans Ultrason Ferroelectr Freq Control 51(12):1582–1592Lubarda VA (2004) On thermodynamic potentials in linear thermoelasticity. Int J Solids Struct 41:7377–7398Mansuripur M (2012) Trouble with the lorentz law of force: incompatibility with special relativity and momentum conservation. Phys Rev Lett 108:193901Maruszewski B, Lebon G (1986) An extended irreversible thermodynamic description of electrothermoelastic semiconductors. Int J Eng Sci 24(4):583–593McMeeking RM, Landis CM (2005) Electrostatic forces and stored energy for deformable dielectric materials. J Appl Mech 72:581–590McMeeking RM, Landis CM, Jimenez MA (2007) A principle of virtual work for combined electrostatic and mechanical loading of materials. Int J Non Linear Mech 42:831–838MELCOR (2000) Thermoelectric handbook. Melcor, a unit of Laird Technologies. http://www.lairdtech.comMinkowski H (1908) Nachr. ges. wiss. Gottingen 53Naranjo B, Gimzewski JK, Putterman S (2005) Observation of nuclear fusion driven by a pyroelectric crystal. Nature 28(434):1115–1117Nédélec JC (1980) Mixed finite elements in $${R}^3$$ R 3 . Numer Math 35:314–345Nettleton RE, Sobolev SL (1995) Applications of extended thermodynamics to chemical, rheological, and transport processes: a special survey part I. approaches and scalar rate processes. J Non-Equilib Thermodyn 20:205–229Nettleton RE, Sobolev SL (1995) Applications of extended thermodynamics to chemical, rheological, and transport processes: a special survey part II. vector transport processes, shear relaxation and rheology. J Non-Equilib Thermodyn 20:297–331Nettleton RE, Sobolev SL (1996) Applications of extended thermodynamics to chemical, rheological, and transport processes: a special survey part III. wave phenomena. J Non-Equilib Thermodyn 21:1–16Newmark N (1959) A method of computation for structural dynamics. ASCE J Eng Mech 85:67–94Newnham RE (2005) Properties of materials: anisotropy, symmetry, structure. Oxford University Press, OxfordNour AE, Abd-Alla N, Maugin GA (1990) Nonlinear equations for thermoelastic magnetizable conductors. Int J Eng Sci 27(7):589–603Nowacki A (1962) International series of monographs in aeronautics and astronautics. Pergamon Press, OxfordOkumura H, Hasegawa Y, Nakamura H, Yamaguchi S (1999) A computational model of thermoelectric and thermomagnetic semiconductors. In: 18th international conference on thermoelectricsOkumura H, Yamaguchi S, Nakamura H, Ikeda K, Sawada K (1998) Numerical computation of thermoelectric and thermomagnetic effects. In: 17th international conference on thermoelectricsOliver X, Agelet C (2000) Continuum mechanics for engineers. Edicions UPC, Barcelona. http://hdl.handle.net/2099.3/36197Shankar K, Kondaiah P, Ganesan N (2013) Pyroelectric and pyromagnetic effects on multiphase magneto-electro-elastic cylindrical shells for axisymmetric temperature. Smart Mater Struct 22(2):025007Palma R, Pérez-Aparicio JL, Bravo R (2013) Study of hysteretic thermoelectric behavior in photovoltaic materials using the finite element method, extended thermodynamics and inverse problems. Energy Convers Manag 65:557–563Palma R, Pérez-Aparicio JL, Taylor RL (2012) Non-linear finite element formulation applied to thermoelectric materials under hyperbolic heat conduction model. Comput Method Appl Mech Eng 213–216:93–103Palma R, Rus G, Gallego R (2009) Probabilistic inverse problem and system uncertainties for damage detection in piezoelectrics. Mech Mater 41:1000–1016Pérez-Aparicio JL, Gavela D (1998) 3D, non-linear coupled, finite element model of thermoelectricity. In: 4th European workshop on thermoelectricsPérez-Aparicio JL, Palma R, Taylor RL (2012) Finite element analysis and material sensitivity of Peltier thermoelectric cells coolers. Int J Heat Mass Transf 55:1363–1374Pérez-Aparicio JL, Sosa H (2004) A continuum three-dimensional, fully coupled, dynamic, non-linear finite element formulation for magnetostrictive materials. Smart Mater Struct 13:493–502Perez-Aparicio JL, Sosa H, Palma R (2007) Numerical investigations of field-defect interactions in piezoelectric ceramics. Int J Solids Struct 44:4892–4908Pérez-Aparicio JL, Taylor RL, Gavela D (2007) Finite element analysis of nonlinear fully coupled thermoelectric materials. Comput Mech 40:35–45Qi H, Fang D, Yao Z (1997) FEM analysis of electro-mechanical coupling effect of piezoelectric materials. Comput Mater Sci 8:283–290Pérez-Aparicio JL, Palma R, Abouali-Sánchez S (2014) Complete finite element method analysis of galvanomagnetic and thermomagnetic effects. Appl Therm Eng (submitted)Perez-Aparicio JL, Palma R, Moreno-Navarro P (2014) Elasto-thermoelectric non-linear, fully coupled, and dynamic finite element analysis of pulsed thermoelectrics. Appl Therm Eng (submitted)Ramírez F, Heyliger PR, Pan E (2006) Free vibration response of two-dimensional magneto-electro-elastic laminated plates. J Sound Vib 292:626–644Reitz JR, Milford FJ (1960) Foundations of electromagnetic theory. Addison-Wesley, BostonReng Z, Ionescu B, Besbes M, Razek A (1995) Calculation of mechanical deformation of magnetic materials in electromagnetic devices. IEEE Trans Magn 31(3):1873–1876Restuccia L (2010) On a thermodynamic theory for magnetic relaxation phenomena due to n microscopic phenomena described by n internal variables. J Non-Equilib Thermodyn 35:379–413Restuccia L, Kluitenberg GA (1988) On generalizations of the Debye equation for dielectric relaxation. Phys A 154:157–182Restuccia L, Kluitenberg GA (1992) On the heat dissipation function for dielectric relaxation phenomena in anisotropic media. Int J Eng Sci 30(3):305–315Riffat SB, Ma X (2003) Thermoelectrics: a review of present and potential applications. Appl Therm Eng 23:913–935Rinaldi C, Brenner H (2002) Body versus surface forces in continuum mechanics: is the Maxwell stress tensor a physically objective Cauchy stress? Phys Rev E 65:036615Rowe DM (ed) (1995) CRC handbook of thermoelectrics. CRC Press, UKRus G, Palma R, Pérez-Aparicio JL (2009) Optimal measurement setup for damage detection in piezoelectric plates. Int J Eng Sci 47:554–572Rus G, Palma R, Pérez-Aparicio JL (2012) Experimental design of dynamic model-based damage identification in piezoelectric ceramics. Mech Syst Signal Process 26:268–293Sadiku MNO (2001) Numerical techniques in electromagnetics. CRC Press LLC, Boca RatonSemenov AS, Kessler H, Liskowsky A, Balke H (2006) On a vector potential formulation for 3D electromechanical finite element analysis. Commun Numer Methods Eng 22:357–375Serra E, Bonaldi M (2008) A finite element formulation for thermoelastic damping analysis. Int J Numer Methods Eng 78(6):671–691Several. Wikipedia. Wikipedia: The Free Encyclopedia, SeveralSoh AK, Liu JX (2005) On the constitutive equations of magnetoelectroelastic solids. J Intell Mater Syst Struct 16:597–602Stefanescu DM (2011) Handbook of force transducers: principles and components. Springer, BerlinTamma KK, Namburu RR (1992) An effective finite element modeling/analysis approach for dynamic thermoelasticity due to second sound effects. Comput Mech 9:73–84Tang T, Yu W (2009) Micromechanical modeling of the multiphysical behavior of smart materials using the variational asymptotic method. Smart Mater Struct 18:1–14Taylor RL (2010) FEAP a finite element analysis program: user manual. University of California, Berkeley. http://www.ce.berkeley.edu/feapThurston RN (1994) Warren p. Mason (1900–1986) physicist, engineer, inventor, author, teacher. IEEE Trans Ultrason Ferroelectr Freq Control 41(4):425–434Tian X, Shen Y, Chen C, He T (2006) A direct finite element method study of generalized thermoelastic problems. Int J Solids Struct 43:2050–2063Tinder RF (2008) Tensor properties of solids: phenomenological development of the tensor properties of crystals. Morgan and Claypool, San RafaelTruesdell C (1968) Thermodynamics for beginners, in irreversible aspects of continuum mechanics. Springer, BerlinTzou HS, Ye R (1996) Pyroelectric and thermal strain effects of piezoelectric (PVDF and PZT) devices. Mech Syst Signal Process 10(4):459–469Walser R (1972) Application of pyromagnetic phenomena to radiation detection

Proteomic characterization of HIV-modulated membrane receptors, kinases and signaling proteins involved in novel angiogenic pathways

<p>Abstract</p> <p>Background</p> <p>Kaposi's sarcoma (KS), hemangioma, and other angioproliferative diseases are highly prevalent in HIV-infected individuals. While KS is etiologically linked to the human herpesvirus-8 (HHV8) infection, HIV-patients without HHV-8 and those infected with unrelated viruses also develop angiopathies. Further, HIV-Tat can activate protein-tyrosine-kinase (PTK-activity) of the vascular endothelial growth factor receptor involved in stimulating angiogenic processes. However, Tat by itself or HHV8-genes alone cannot induce angiogenesis <it>in vivo </it>unless specific proteins/enzymes are produced synchronously by different cell-types. We therefore tested a hypothesis that <it>chronic </it>HIV-<it>replication in non-endothelial cells </it>may produce novel factors that provoke angiogenic pathways.</p> <p>Methods</p> <p>Genome-wide proteins from HIV-infected and uninfected T-lymphocytes were tested by subtractive proteomics analyses at various stages of virus and cell growth <it>in vitro </it>over a period of two years. Several thousand differentially regulated proteins were identified by mass spectrometry (MS) and >200 proteins were confirmed in multiple gels. Each protein was scrutinized extensively by protein-interaction-pathways, bioinformatics, and statistical analyses.</p> <p>Results</p> <p>By functional categorization, 31 proteins were identified to be associated with various signaling events involved in angiogenesis. 88% proteins were located in the plasma membrane or extracellular matrix and >90% were found to be essential for regeneration, neovascularization and angiogenic processes during embryonic development.</p> <p>Conclusion</p> <p>Chronic HIV-infection of T-cells produces membrane receptor-PTKs, serine-threonine kinases, growth factors, adhesion molecules and many diffusible signaling proteins that have not been previously reported in HIV-infected cells. Each protein has been associated with endothelial cell-growth, morphogenesis, sprouting, microvessel-formation and other biological processes involved in angiogenesis (p = 10^-4to 10^-12). Bioinformatics analyses suggest that overproduction of PTKs and other kinases in HIV-infected cells has <it>suppressed </it>VEGF/VEGFR-PTK expression and promoted <it>VEGFR-independent </it>pathways. This unique mechanism is similar to that observed in neovascularization and angiogenesis during embryogenesis. Validation of clinically relevant proteins by gene-silencing and translational studies <it>in vivo </it>would identify specific targets that can be used for early diagnosis of angiogenic disorders and future development of inhibitors of angiopathies. This is the first comprehensive study to demonstrate that HIV-infection alone, without any co-infection or treatment, can induce numerous "embryonic" proteins and kinases capable of generating novel <it>VEGF-independent </it>angiogenic pathways.</p

Meta-analysis of genetic association with diagnosed Alzheimer’s disease identifies novel risk loci and implicates Abeta, Tau, immunity and lipid processing

Introduction Late-onset Alzheimer’s disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly 1 , and risk is partially driven by genetics 2 . Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS) 3–8 . To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci ( IQCK , ACE , ADAM10 , and ADAMTS1 ). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants ( P = 1.32 × 10 −7 ) indicating that additional rare variants remain to be identified.ADGC. The National Institutes of Health, National Institute on Aging (NIH-NIA) supported this work through the following grants: ADGC, U01 AG032984, RC2 AG036528; Samples from the National Cell Repository for Alzheimer’s Disease (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible; Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689-01); NACC, U01 AG016976; NIA LOAD (Columbia University), U24 AG026395, U24 AG026390, R01AG041797; Banner Sun Health Research Institute P30 AG019610; Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, R01 AG048927, R01AG33193, R01 AG009029; Columbia University, P50 AG008702, R37 AG015473, R01 AG037212, R01 AG028786; Duke University, P30 AG028377, AG05128; Emory University, AG025688; Group Health Research Institute, UO1 AG006781, UO1 HG004610, UO1 HG006375, U01 HG008657; Indiana University, P30 AG10133, R01 AG009956, RC2 AG036650; Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574, R01 AG032990, KL2 RR024151; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, 1R01AG035137; North Carolina A&T University, P20 MD000546, R01 AG28786-01A1; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG030146, R01 AG01101, RC2 AG036650, R01 AG22018; TGen, R01 NS059873; University of Alabama at Birmingham, P50 AG016582; University of Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383, AG05144; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653, AG041718, AG07562, AG02365; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136, R01 AG042437; University of Wisconsin, P50 AG033514; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991, P01 AG026276. The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS grant # NS39764, NIMH MH60451 and by Glaxo Smith Kline. Support was also from the Alzheimer’s Association (LAF, IIRG-08-89720; MP-V, IIRG-05-14147), the US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program, and BrightFocus Foundation (MP-V, A2111048). P.S.G.-H. is supported by Wellcome Trust, Howard Hughes Medical Institute, and the Canadian Institute of Health Research. Genotyping of the TGEN2 cohort was supported by Kronos Science. The TGen series was also funded by NIA grant AG041232 to AJM and MJH, The Banner Alzheimer’s Foundation, The Johnnie B. Byrd Sr. Alzheimer’s Institute, the Medical Research Council, and the state of Arizona and also includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council, local NHS trusts and Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council),South West Dementia Brain Bank (funding via numerous sources including the Higher Education Funding Council for England (HEFCE), Alzheimer’s Research Trust (ART), BRACE as well as North Bristol NHS Trust Research and Innovation Department and DeNDRoN), The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona. ADNI data collection and sharing was funded by the National Institutes of Health Grant U01 AG024904 and Department of Defense award number W81XWH-12-2-0012. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members. EADI. This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer’s disease) including funding from MEL (Metropole européenne de Lille), ERDF (European Regional Development Fund) and Conseil Régional Nord Pas de Calais. This work was supported by INSERM, the National Foundation for Alzheimer’s disease and related disorders, the Institut Pasteur de Lille and the Centre National de Génotypage, the JPND PERADES, GENMED, and the FP7 AgedBrainSysBio. The Three-City Study was performed as part of collaboration between the Institut National de la Santé et de la Recherche Médicale (Inserm), the Victor Segalen Bordeaux II University and Sanofi- Synthélabo. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C Study was also funded by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la Santé, MGEN, Institut de la Longévité, Agence Française de Sécurité Sanitaire des Produits de Santé, the Aquitaine and Bourgogne Regional Councils, Agence Nationale de la Recherche, ANR supported the COGINUT and COVADIS projects. Fondation de France and the joint French Ministry of Research/INSERM “Cohortes et collections de données biologiques” programme. Lille Génopôle received an unconditional grant from Eisai. The Three-city biological bank was developed and maintained by the laboratory for genomic analysis LAG-BRC - Institut Pasteur de Lille. This work was further supported by the CoSTREAM project (http://www.costream.eu/) and funding from the European Union's Horizon 2020 research and innovation program under grant agreement 667375. Belgium samples: Research at the Antwerp site is funded in part by the Belgian Science Policy Office Interuniversity Attraction Poles program, the Belgian Alzheimer Research Foundation, the Flemish government-initiated Flanders Impulse Program on Networks for Dementia Research (VIND) and the Methusalem excellence program, the Research Foundation Flanders (FWO), and the University of Antwerp Research Fund, Belgium. The Antwerp site authors thank the personnel of the VIB Neuromics Support Facility, the Biobank of the Institute Born-Bunge and neurology departments at the contributing hospitals. The authors acknowledge the members of the BELNEU consortium for their contributions to the clinical and pathological characterization of Belgium patients and the personnel of the Diagnostic Service Facility for the genetic testing. Finish sample collection: Financial support for this project was provided by Academy of Finland (grant number 307866), Sigrid Jusélius Foundation and the Strategic Neuroscience Funding of the University of Eastern Finland. Swedish sample collection: Financially supported in part by the Swedish Brain Power network, the Marianne and Marcus Wallenberg Foundation, the Swedish Research Council (521-2010-3134, 2015-02926), the King Gustaf V and Queen Victoria’s Foundation of Freemasons, the Regional Agreement on Medical Training and Clinical Research (ALF) between Stockholm County Council and the Karolinska Institutet, the Swedish Brain Foundation and the Swedish Alzheimer Foundation”. CHARGE. Infrastructure for the CHARGE Consortium is supported in part by National Heart, Lung, and Blood Institute grant HL105756 (Psaty) and RC2HL102419 (Boerwinkle) and the neurology working group by grants from the National Institute on Aging, R01 AG033193, U01 AG049505 and U01AG52409. Rotterdam (RS). This study was funded by the Netherlands Organisation for Health Research and Development (ZonMW) as part of the Joint Programming for Neurological Disease (JPND)as part of the PERADES Program (Defining Genetic Polygenic, and Environmental Risk for Alzheimer’s disease using multiple powerful cohorts, focused Epigenetics and Stem cell metabolomics), Project number 733051021. This work was funded also by the European Union Innovative Medicine Initiative (IMI) programme under grant agreement No. 115975 as part of the Alzheimer’s Disease Apolipoprotein Pathology for Treatment Elucidation and Development (ADAPTED, https://www.imi-adapted.eu);and the European Union’s Horizon 2020 research and innovation programme as part of the Common mechanisms and pathways in Stroke and Alzheimer’s disease CoSTREAM project (www.costream.eu, grant agreement No. 667375). The current study is supported by the Deltaplan Dementie and Memorabel supported by ZonMW (Project number 733050814) and Alzheimer Nederland. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS-I, RS-II, RS-III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organization of Scientific Research NWO Investments (Project number 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project number 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters, MSc, and Carolina Medina-Gomez, MSc, for their help in creating the GWAS database, and Karol Estrada, PhD, Yurii Aulchenko, PhD, and Carolina Medina-Gomez, MSc, for the creation and analysis of imputed data. AGES. The AGES study has been funded by NIA contracts N01-AG-12100 and HHSN271201200022C with contributions from NEI, NIDCD, and NHLBI, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). Cardiovascular Health Study (CHS). This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, and N01HC85086 and grant U01HL080295 and U01HL130114 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG033193, R01AG023629, R01AG15928, and R01AG20098 and by U01AG049505 from the National Institute on Aging (NIA). The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. A full list of CHS principal investigators and institutions can be found at https://chs-nhlbi.org/. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. Framingham Heart Study. This work was supported by the National Heart, Lung, and Blood Institute's Framingham Heart Study (contracts N01-HC-25195 and HHSN268201500001I). This study was also supported by grants from the National Institute on Aging: R01AG033193, U01AG049505, U01AG52409, R01AG054076 (S. Seshadri). S. Seshadri and A.L.D. were also supported by additional grants from the National Institute on Aging (R01AG049607, R01AG033040) and the National Institute of Neurological Disorders and Stroke (R01- NS017950, NS100605). The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health. GR@ACE cohort. Fundació ACE We would like to thank patients and controls who participated in this project. Genome Resesarch @ Fundació ACE project (GR@ACE) is supported by Fundación bancaria “La Caixa”, Grifols SA, Fundació ACE and ISCIII. We also want to thank other private sponsors supporting the basic and clinical projects of our institution (Piramal AG, Laboratorios Echevarne, Araclon Biotech S.A. and Fundació ACE). We are indebted to Trinitat Port-Carbó legacy and her family for their support of Fundació ACE research programs. Fundació ACE collaborates with the Centro de Investigación Biomédica en Red sobreEnfermedades Neurodegenerativas (CIBERNED, Spain) and is one of the participating centers of the Dementia Genetics Spanish Consortium (DEGESCO). A.R. and M.B. are receiving support from the European Union/EFPIA Innovative Medicines Initiative Joint Undertaking ADAPTED and MOPEAD projects (Grants No. 115975 and 115985 respectively). M.B. and A.R. are also supported by national grants PI13/02434, PI16/01861 and PI17/01474. Acción Estratégica en Salud integrated in the Spanish National R + D + I Plan and funded by ISCIII (Instituto de Salud Carlos III)-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER- “Una manera de Hacer Europa”). Control samples and data from patients included in this study were provided in part by the National DNA Bank Carlos III (www.bancoadn.org, University of Salamanca, Spain) and Hospital Universitario Virgen de Valme (Sevilla, Spain) and they were processed following standard operating procedures with the appropriate approval of the Ethical and Scientific Committee. GERAD/PERADES. We thank all individuals who participated in this study. Cardiff University was supported by the Wellcome Trust, Alzheimer’s Society (AS; grant RF014/164), the Medical Research Council (MRC; grants G0801418/1, MR/K013041/1, MR/L023784/1), the European Joint Programme for Neurodegenerative Disease (JPND, grant MR/L501517/1), Alzheimer’s Research UK (ARUK, grant ARUK-PG2014-1), Welsh Assembly Government (grant SGR544:CADR), a donation from the Moondance Charitable Foundation, and the UK Dementia Research Institute at Cardiff. Cambridge University acknowledges support from the MRC. ARUK supported sample collections at the Kings College London, the South West Dementia Bank, Universities of Cambridge, Nottingham, Manchester and Belfast. King’s College London was supported by the NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at the South London and Maudsley NHS Foundation Trust and Kings College London and the MRC. Alzheimer’s Research UK (ARUK) and the Big Lottery Fund provided support to Nottingham University. Ulster Garden Villages, AS, ARUK, American Federation for Aging Research, NI R&D Office and the Royal College of Physicians/Dunhill Medical Trust provided support for Queen’s University, Belfast. The University of Southampton acknowledges support from the AS. The MRC and Mercer’s Institute for Research on Ageing supported the Trinity College group. DCR is a Wellcome Trust Principal Research fellow. The South West Dementia Brain Bank acknowledges support from Bristol Research into Alzheimer’s and Care of the Elderly. The Charles Wolfson Charitable Trust supported the OPTIMA group. Washington University was funded by NIH grants, Barnes Jewish Foundation and the Charles and Joanne Knight Alzheimer’s Research Initiative. Patient recruitment for the MRC Prion Unit/UCL Department of Neurodegenerative Disease collection was supported by the UCLH/UCL Biomed- ical Centre and their work was supported by the NIHR Queen Square Dementia BRU. LASER-AD was funded by Lundbeck SA. The Bonn group would like to thank Dr. Heike Koelsch for her scientific support. The Bonn group was funded by the German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant number 01GI0102, 01GI0711, 01GI0420. The AgeCoDe study group was supported by the German Federal Ministry for Education and Research grants 01 GI 0710, 01 GI 0712, 01 GI 0713, 01 GI 0714, 01 GI 0715, 01 GI 0716, 01 GI 0717. Genotyping of the Bonn case-control sample was funded by the German centre for Neurodegenerative Diseases (DZNE), Germany. The GERAD Consortium also used samples ascertained by the NIMH AD Genetics Initiative. HH was supported by a grant of the Katharina-Hardt-Foundation, Bad Homburg vor der Höhe, Germany. The KORA F4 studies were financed by Helmholtz Zentrum München; German Research Center for Environmental Health; BMBF; German National Genome Research Network and the Munich Center of Health Sciences. The Heinz Nixdorf Recall cohort was funded by the Heinz Nixdorf Foundation (Dr. Jur. G.Schmidt, Chairman) and BMBF. Coriell Cell Repositories is supported by NINDS and the Intramural Research Program of the National Institute on Aging. We acknowledge use of genotype data from the 1958 Birth Cohort collection, funded by the MRC and the Wellcome Trust which was genotyped by the Wellcome Trust Case Control Consortium and the Type-1 Diabetes Genetics Consortium, sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development and Juvenile Diabetes Research Foundation International. The Bonn samples are part of the German Dementia Competance Network (DCN) and the German Research Network on Degenerative Dementia (KNDD), which are funded by the German Federal Ministry of Education and Research (grants KND: 01G10102, 01GI0420, 01GI0422, 01GI0423, 01GI0429, 01GI0431, 01GI0433, 04GI0434; grants KNDD: 01GI1007A, 01GI0710, 01GI0711, 01GI0712, 01GI0713, 01GI0714, 01GI0715, 01GI0716, 01ET1006B). Markus M Nothen is a member of the German Research Foundation (DFG) cluster of excellence ImmunoSensation. Funding for Saarland University was provided by the German Federal Ministry of Education and Research (BMBF), grant number 01GS08125 to Matthias Riemenschneider. The University of Washington was supported by grants from the National Institutes of Health (R01-NS085419 and R01-AG044546), the Alzheimer’s Association (NIRG-11-200110) and the American Federation for Aging Research (Carlos Cruchaga was recipient of a New Investigator Award in Alzhei

Array data extractor (ADE): a LabVIEW program to extract and merge gene array data

BACKGROUND: Large data sets from gene expression array studies are publicly available offering information highly valuable for research across many disciplines ranging from fundamental to clinical research. Highly advanced bioinformatics tools have been made available to researchers, but a demand for user-friendly software allowing researchers to quickly extract expression information for multiple genes from multiple studies persists. FINDINGS: Here, we present a user-friendly LabVIEW program to automatically extract gene expression data for a list of genes from multiple normalized microarray datasets. Functionality was tested for 288 class A G protein-coupled receptors (GPCRs) and expression data from 12 studies comparing normal and diseased human hearts. Results confirmed known regulation of a beta 1 adrenergic receptor and further indicate novel research targets. CONCLUSIONS: Although existing software allows for complex data analyses, the LabVIEW based program presented here, “Array Data Extractor (ADE)”, provides users with a tool to retrieve meaningful information from multiple normalized gene expression datasets in a fast and easy way. Further, the graphical programming language used in LabVIEW allows applying changes to the program without the need of advanced programming knowledge