VEGF is a mediator of retinal pigment epithelium permeability leading to photoreceptor cell death in light-induced retinal degeneration : gene therapy strategy to prevent AMD-disorders

ABSTRACTIn normal tissues, a balance between pro- and anti-angiogenic factors tightly controls angiogenesis. Alterations of this balance may have pathological consequences. For instance, concerning the retina, the vascular endothelial growth factor (VEGF) is a potent pro-angiogenic factor, and has been identified has a key player during ocular neovascularization implicated in a variety of retinal diseases. In the exudative form (wet-form) of age-related macular degeneration (AMD), neovascularizations occurring from the choroidal vessels are responsible for a quick and dramatic loss of visual acuity. In diabetic retinopathy and retinopathy of prematurity, sprouting from the retinal vessels leads to vision loss. Furthermore, the aging of the population, the increased- prevalence of diabetes and the better survival rate of premature infants will lead to an increasing rate of these conditions. In this way, anti-VEGF strategy represents an important therapeutic target to treat ocular neovascular disorders.In addition, the administration of Pigmented Epithelial growth factor, a neurotrophic and an anti- angiogenic factor, prevents photoreceptor cell death in a model of retinal degeneration induced by light. Previous results analyzing end point morphology reveal that the light damage (LD) model is used to mimic retinal degenerations arising from environmental insult, as well as aging and genetic disease such as advanced atrophic AMD. Moreover, light has been identified as a co-factor in a number of retinal diseases, speeding up the degeneration process. This protecting effect of PEDF in the LD retina raises the possibility of involvement of the balance between pro- and anti-angiogenic factors not only for angiogenesis, but also in cell survival and maintenance.The aim of the work presented here was to evaluate the importance of this balance in neurodegenerative processes. To this aim, a model of light-induced retinal degeneration was used and characterized, mainly focusing on factors simultaneously controlling neuron survival and angiogenesis, such as PEDF and VEGF.In most species, prolonged intense light exposure can lead to photoreceptor cell damage that can progress to cell death and vision loss. A protocol previously described to induce retinal degeneration in Balb/c mice was used. Retinas were characterized at different time points after light injury through several methods at the functional and molecular levels. Data obtained confirmed that toxic level of light induce PR cell death. Variations were observed in VEGF pathway players in both the neural retina and the eye-cup containing the retinal pigment epithelium (RPE), suggesting a flux of VEGF from the RPE towards the neuroretina. Concomitantly, the integrity of the outer blood-retinal-barrier (BRB) was altered, leading to extravascular albumin leakage from the choroid throughout the photoreceptor layer.To evaluate the importance of VEGF during light-induced retinal degeneration process, a lentiviral vector encoding the cDNA of a single chain antibody directed against all VEGF-A isoforms was developed (LV-V65). The bioactivity of this vector to block VEGF was validated in a mouse model of laser-induced choroidal neovascularization mediated by VEGF upregulation. The vector was then used in the LD model. The administration of the LV-V65 contributed to the maintenance of functional photoreceptors, which was assessed by ERG recording, visual acuity measurement and histological analyses. At the RPE level, the BRB integrity was preserved as shown by the absence of albumin leakage and the maintenance of RPE cell cohesion.These results taken together indicate that the VEGF is a mediator of light induced PR degeneration process and confirm the crucial role of the balance between pro- and anti-angiogenic factors in the PR cell survival. This work also highlights the prime importance of BRB integrity and functional coupling between RPE and PR cells to maintain the PR survival. VEGF dysregulation was already shown to be involved in wet AMD forms and our study suggests that VEGF dysregulation may also occur at early stages of AMD and could thus be a potential therapeutic target for several RPE related diseases.RESUMEDans les différents tissues de l'organisme, l'angiogenèse est strictement contrôlée par une balance entre les facteurs pro- et anti-angiogéniques. Des modifications survenant dans cette balance peuvent engendrer des conséquences pathologiques. Par exemple, concernant la rétine, le facteur de croissance de l'endothélium vasculaire (VEGF) est un facteur pro-angiogénique important. Ce facteur a été identifié comme un acteur majeur dans les néovascularisations oculaires et les processus pathologiques angiogéniques survenant dans l'oeil et responsables d'une grande variété de maladies rétiniennes. Dans la forme humide de la dégénérescence maculaire liée à l'âge (DMLA), la néovascularisation choroïdienne est responsable de la perte rapide et brutale de l'acuité visuelle chez les patients affectés. Dans la rétinopathie diabétique et celle lié à la prématurité, l'émergence de néovaisseaux rétiniens est la cause de la perte de la vision. Les néovascularisations oculaires représentent la principale cause de cécité dans les pays développés. De plus, l'âge croissant de la population, la progression de la prévalence du diabète et la meilleure survie des enfants prématurés mèneront sans doute à l'augmentation de ces pathologies dans les années futures. Dans ces conditions, les thérapies anti- angiogéniques visant à inhiber le VEGF représentent une importante cible thérapeutique pour le traitement de ces pathologies.Plusieurs facteurs anti-angiogéniques ont été identifiés. Parmi eux, le facteur de l'épithélium pigmentaire (PEDF) est à la fois un facteur neuro-trophique et anti-angiogénique, et l'administration de ce facteur au niveau de la rétine dans un modèle de dégénérescence rétinienne induite par la lumière protège les photorécepteurs de la mort cellulaire. Des études antérieures basées sur l'analyse morphologique ont révélé que les modifications survenant lors de la dégénération induite suite à l'exposition à des doses toxiques de lumière représente un remarquable modèle pour l'étude des dégénérations rétiniennes suite à des lésions environnementales, à l'âge ou encore aux maladies génétiques telle que la forme atrophique avancée de la DMLA. De plus, la lumière a été identifiée comme un co-facteur impliqué dans un grand nombre de maladies rétiniennes, accélérant le processus de dégénération. L'effet protecteur du PEDF dans les rétines lésées suite à l'exposition de des doses toxiques de lumière suscite la possibilité que la balance entre les facteurs pro- et anti-angiogéniques soit impliquée non seulement dans les processus angiogéniques, mais également dans le maintient et la survie des cellules.Le but de ce projet consiste donc à évaluer l'implication de cette balance lors des processus neurodégénératifs. Pour cela, un modèle de dégénération induite par la lumière à été utilisé et caractérisé, avec un intérêt particulier pour les facteurs comme le PEDF et le VEGF contrôlant simultanément la survie des neurones et l'angiogenèse.Dans la plupart des espèces, l'exposition prolongée à une lumière intense peut provoquer des dommages au niveau des cellules photoréceptrices de l'oeil, qui peut mener à leur mort, et par conséquent à la perte de la vision. Un protocole préalablement décrit a été utilisé pour induire la dégénération rétinienne dans les souris albinos Balb/c. Les rétines ont été analysées à différents moments après la lésion par différentes techniques, aussi bien au niveau moléculaire que fonctionnel. Les résultats obtenus ont confirmé que des doses toxiques de lumière induisent la mort des photorécepteurs, mais altèrent également la voie de signalisation du VEGF, aussi bien dans la neuro-rétine que dans le reste de l'oeil, contenant l'épithélium pigmentaire (EP), et suggérant un flux de VEGF provenant de ΙΈΡ en direction de la neuro-rétine. Simultanément, il se produit une altération de l'intégrité de la barrière hémato-rétinienne externe, menant à la fuite de protéine telle que l'albumine, provenant de la choroïde et retrouvée dans les compartiments extravasculaires de la rétine, telle que dans la couche des photorécepteurs.Pour déterminer l'importance et le rôle du VEGF, un vecteur lentiviral codant pour un anticorps neutralisant dirigée contre tous les isoformes du VEGF a été développé (LV-V65). La bio-activité de ce vecteur a été testé et validée dans un modèle de laser, connu pour induire des néovascularisations choroïdiennes chez la souris suite à l'augmentation du VEGF. Ce vecteur a ensuite été utilisé dans le modèle de dégénération induite par la lumière. Les résultats des électrorétinogrammes, les mesures de l'acuité visuelle et les analyses histologiques ont montré que l'injection du LV-V65 contribue à la maintenance de photorécepteurs fonctionnels. Au niveau de l'EP, l'absence d'albumine et la maintenance des jonctions cellulaires des cellules de l'EP ont démontré que l'intégrité de la barrière hémato-rétinienne externe est préservée suite au traitement.Par conséquent, tous les résultats obtenus indiquent que le VEGF est un médiateur important impliquée dans le processus de dégénération induit par la lumière et confirme le rôle cruciale de la balance entre les facteurs pro- et anti-angiogéniques dans la survie des photorécepteurs. Cette étude révèle également l'importance de l'intégrité de la barrière hémato-rétinienne et l'importance du lien fonctionnel et structurel entre l'EP et les photorécepteurs, essentiel pour la survie de ces derniers. Par ailleurs, Cette étude suggère que des dérèglements au niveau de l'équilibre du VEGF ne sont pas seulement impliqués dans la forme humide de la DMLA, comme déjà démontré dans des études antérieures, mais pourraient également contribuer et survenir dans des formes précoces de la DMLA, et par conséquent le VEGF représente une cible thérapeutique potentielle pour les maladies associées à des anomalies au niveau de l'EP.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.

Fluid dynamical prediction of changed v1 flow at energies available at the CERN Large Hadron Collider

Substantial collective flow is observed in collisions between lead nuclei at Large Hadron Collider (LHC) as evidenced by the azimuthal correlations in the transverse momentum distributions of the produced particles. Our calculations indicate that the global v1-flow, which at RHIC peaked at negative rapidities (named third flow component or antiflow), now at LHC is going to turn toward forward rapidities (to the same side and direction as the projectile residue). Potentially this can provide a sensitive barometer to estimate the pressure and transport properties of the quark-gluon plasma. Our calculations also take into account the initial state center-of-mass rapidity fluctuations, and demonstrate that these are crucial for v1 simulations. In order to better study the transverse momentum flow dependence we suggest a new "symmetrized" v1S(pt) function, and we also propose a new method to disentangle global v1 flow from the contribution generated by the random fluctuations in the initial state. This will enhance the possibilities of studying the collective Global v1 flow both at the STAR Beam Energy Scan program and at LHC.

Prediction of Infant Drug Exposure Through Breastfeeding: Population PK Modeling and Simulation of Fluoxetine Exposure.

The likelihood of significant exposure to drugs in infants through breast milk is poorly defined, given the difficulties of conducting pharmacokinetics (PK) studies. Using fluoxetine (FX) as an example, we conducted a proof-of-principle study applying population PK (popPK) modeling and simulation to estimate drug exposure in infants through breast milk. We simulated data for 1,000 mother-infant pairs, assuming conservatively that the FX clearance in an infant is 20% of the allometrically adjusted value in adults. The model-generated estimate of the milk-to-plasma ratio for FX (mean: 0.59) was consistent with those reported in other studies. The median infant-to-mother ratio of FX steady-state plasma concentrations predicted by the simulation was 8.5%. Although the disposition of the active metabolite, norfluoxetine, could not be modeled, popPK-informed simulation may be valid for other drugs, particularly those without active metabolites, thereby providing a practical alternative to conventional PK studies for exposure risk assessment in this population.

Validation of a clinical prediction score for ruling out coronary heart disease in primary care patients with chest pain.

Background: A patient's chest pain raises concern for the possibility of coronary heart disease (CHD). An easy to use clinical prediction rule has been derived from the TOPIC study in Lausanne. Our objective is to validate this clinical score for ruling out CHD in primary care patients with chest pain. Methods: This secondary analysis used data collected from a oneyear follow-up cohort study attending 76 GPs in Germany. Patients attending their GP with chest pain were questioned on their age, gender, duration of chest pain (1-60 min), sternal pain location, pain increases with exertion, absence of tenderness point at palpation, cardiovascular risks factors, and personal history of cardiovascular disease. Area under the curve (ROC), sensitivity and specificity of the Lausanne CHD score were calculated for patients with full data. Results: 1190 patients were included. Full data was available for 509 patients (42.8%). Missing data was not related to having CHD (p = 0.397) or having a cardiovascular risk factor (p = 0.275). 76 (14.9%) were diagnosed with a CHD. Prevalence of CHD were respectively of 68/344 (19.8%), 2/62 (3.2%), 6/103 (5.8%) in the high, intermediate and low risk category. ROC was of 72.9 (CI95% 66.8; 78.9). Ruling out patients with low risk has a sensitivity of 92.1% (CI95% 83.0; 96.7) and a specificity of 22.4% (CI95% 18.6%; 26.7%). Conclusion: The Lausanne CHD score shows reasonably good sensitivity and can be used to rule out coronary events in patients with chest pain. Patients at risk of CHD for other rarer reasons should nevertheless also be investigated.

Bactericidal/permeability-increasing protein (BPI) and BPI homologs at mucosal sites.

At mucosal surfaces, we must co-exist with a high density of diverse microorganisms; therefore, protection against these occurs on multiple levels. Leukocyte- and epithelial derived-antimicrobial peptides and proteins (AMPs) comprise an essential component of immune defense. These molecules possess antibacterial, antifungal and signalling properties and probably contribute to defence and maintenance of homeostasis between the host and commensal microorganisms. Among these AMPs is bactericidal/permeability-increasing protein (BPI), an antimicrobial protein with potent endotoxin-neutralising activity, and several homologs. This review explores the roles of BPI and and its homologs at the mucosal interface. Congeners of BPI are under biopharmaceutical development as novel anti-infective agents, highlighting the potential therapeutic relevance of this protein family.

Ruling out coronary artery disease in primary care: development and validation of a simple prediction rule.

BACKGROUND: Chest pain can be caused by various conditions, with life-threatening cardiac disease being of greatest concern. Prediction scores to rule out coronary artery disease have been developed for use in emergency settings. We developed and validated a simple prediction rule for use in primary care. METHODS: We conducted a cross-sectional diagnostic study in 74 primary care practices in Germany. Primary care physicians recruited all consecutive patients who presented with chest pain (n = 1249) and recorded symptoms and findings for each patient (derivation cohort). An independent expert panel reviewed follow-up data obtained at six weeks and six months on symptoms, investigations, hospital admissions and medications to determine the presence or absence of coronary artery disease. Adjusted odds ratios of relevant variables were used to develop a prediction rule. We calculated measures of diagnostic accuracy for different cut-off values for the prediction scores using data derived from another prospective primary care study (validation cohort). RESULTS: The prediction rule contained five determinants (age/sex, known vascular disease, patient assumes pain is of cardiac origin, pain is worse during exercise, and pain is not reproducible by palpation), with the score ranging from 0 to 5 points. The area under the curve (receiver operating characteristic curve) was 0.87 (95% confidence interval [CI] 0.83-0.91) for the derivation cohort and 0.90 (95% CI 0.87-0.93) for the validation cohort. The best overall discrimination was with a cut-off value of 3 (positive result 3-5 points; negative result <or= 2 points), which had a sensitivity of 87.1% (95% CI 79.9%-94.2%) and a specificity of 80.8% (77.6%-83.9%). INTERPRETATION: The prediction rule for coronary artery disease in primary care proved to be robust in the validation cohort. It can help to rule out coronary artery disease in patients presenting with chest pain in primary care.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Outcome Prediction after Cardiac Arrest Treated with Hypothermia

Rationale: Clinical and electrophysiological prognostic markers of brain anoxia have been mostly evaluated in comatose survivors of out hospital cardiac arrest (OHCA) after standard resuscitation, but their predictive value in patients treated with mild induced hypothermia (IH) is unknown. The objective of this study was to identify a predictive score of independent clinical and electrophysiological variables in comatose OHCA survivors treated with IH, aiming at a maximal positive predictive value (PPV) and a high negative predictive value (NPV) for mortality. Methods: We prospectively studied consecutive adult comatose OHCA survivors from April 2006 to May 2009, treated with mild IH to 33-34_C for 24h at the intensive care unit of the Lausanne University Hospital, Switzerland. IH was applied using an external cooling method. As soon as subjects passively rewarmed (body temperature >35_C) they underwent EEG and SSEP recordings (off sedation), and were examined by experienced neurologists at least twice. Patients with status epilepticus were treated with AED for at least 24h. A multivariable logistic regression was performed to identify independent predictors of mortality at hospital discharge. These were used to formulate a predictive score. Results: 100 patients were studied; 61 died. Age, gender and OHCA etiology (cardiac vs. non-cardiac) did not differ among survivors and nonsurvivors. Cardiac arrest type (non-ventricular fibrillation vs. ventricular fibrillation), time to return of spontaneous circulation (ROSC) >25min, failure to recover all brainstem reflexes, extensor or no motor response to pain, myoclonus, presence of epileptiform discharges on EEG, EEG background unreactive to pain, and bilaterally absent N20 on SSEP, were all significantly associated with mortality. Absent N20 was the only variable showing no false positive results. Multivariable logistic regression identified four independent predictors (Table). These were used to construct the score, and its predictive values were calculated after a cut-off of 0-1 vs. 2-4 predictors. We found a PPV of 1.00 (95% CI: 0.93-1.00), a NPV of 0.81 (95% CI: 0.67-0.91) and an accuracy of 0.93 for mortality. Among 9 patients who were predicted to survive by the score but eventually died, only 1 had absent N20. Conclusions: Pending validation in a larger cohort, this simple score represents a promising tool to identify patients who will survive, and most subjects who will not, after OHCA and IH. Furthermore, while SSEP are 100% predictive of poor outcome but not available in most hospitals, this study identifies EEG background reactivity as an important predictor after OHCA. The score appears robust even without SSEP, suggesting that SSEP and other investigations (e.g., mismatch negativity, serum NSE) might be principally needed to enhance prognostication in the small subgroup of patients failing to improve despite a favorable score.

Surface Complexation Modeling in Variable Charge Soils: Prediction of Cadmium Adsorption

ABSTRACT Intrinsic equilibrium constants for 22 representative Brazilian Oxisols were estimated from a cadmium adsorption experiment. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. Intrinsic equilibrium constants were optimized by FITEQL and by hand calculation using Visual MINTEQ in sweep mode, and Excel spreadsheets. Data from both models were incorporated into Visual MINTEQ. Constants estimated by FITEQL and incorporated in Visual MINTEQ software failed to predict observed data accurately. However, FITEQL raw output data rendered good results when predicted values were directly compared with observed values, instead of incorporating the estimated constants into Visual MINTEQ. Intrinsic equilibrium constants optimized by hand calculation and incorporated in Visual MINTEQ reliably predicted Cd adsorption reactions on soil surfaces under changing environmental conditions.

Rgtsp: a generalized top scoring pairs package for class prediction.

SUMMARY: A top scoring pair (TSP) classifier consists of a pair of variables whose relative ordering can be used for accurately predicting the class label of a sample. This classification rule has the advantage of being easily interpretable and more robust against technical variations in data, as those due to different microarray platforms. Here we describe a parallel implementation of this classifier which significantly reduces the training time, and a number of extensions, including a multi-class approach, which has the potential of improving the classification performance. AVAILABILITY AND IMPLEMENTATION: Full C++ source code and R package Rgtsp are freely available from http://lausanne.isb-sib.ch/~vpopovic/research/. The implementation relies on existing OpenMP libraries.

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.