The Effect Of Induced Intraocular Straylight (cataract Simulation) On Sensitivity Measures Of Standard Automated Perimetry, Pulsar Perimetry And Moorfields Motion Displacement Test

Purpose: To investigate the effect of incremental increases in intraocular straylight on threshold measurements made by three modern forms of perimetry: Standard Automated Perimetry (SAP) using Octopus (Dynamic, G-Pattern), Pulsar Perimetry (PP) (TOP, 66 points) and the Moorfields Motion Displacement Test (MDT) (WEBS, 32 points).Methods: Four healthy young observers were recruited (mean age 26yrs [25yrs, 28yrs]), refractive correction [+2 D, -4.25D]). Five white opacity filters (WOF), each scattering light by different amounts were used to create incremental increases in intraocular straylight (IS). Resultant IS values were measured with each WOF and at baseline (no WOF) for each subject using a C-Quant Straylight Meter (Oculus, Wetzlar, Germany). A 25 yr old has an IS value of ~0.85 log(s). An increase of 40% in IS to 1.2log(s) corresponds to the physiological value of a 70yr old. Each WOFs created an increase in IS between 10-150% from baseline, ranging from effects similar to normal aging to those found with considerable cataract. Each subject underwent 6 test sessions over a 2-week period; each session consisted of the 3 perimetric tests using one of the five WOFs and baseline (both instrument and filter were randomised).Results: The reduction in sensitivity from baseline was calculated. A two-way ANOVA on mean change in threshold (where subjects were treated as rows in the block and each increment in fog filters was treated as column) was used to examine the effect of incremental increases in straylight. Both SAP (p<0.001) and Pulsar (p<0.001) were significantly affected by increases in straylight. The MDT (p=0.35) remained comparatively robust to increases in straylight.Conclusions: The Moorfields MDT measurement of threshold is robust to effects of additional straylight as compared to SAP and PP.

Computational anatomy for studying use-dependant brain plasticity.

In this article we provide a comprehensive literature review on the in vivo assessment of use-dependant brain structure changes in humans using magnetic resonance imaging (MRI) and computational anatomy. We highlight the recent findings in this field that allow the uncovering of the basic principles behind brain plasticity in light of the existing theoretical models at various scales of observation. Given the current lack of in-depth understanding of the neurobiological basis of brain structure changes we emphasize the necessity of a paradigm shift in the investigation and interpretation of use-dependent brain plasticity. Novel quantitative MRI acquisition techniques provide access to brain tissue microstructural properties (e.g., myelin, iron, and water content) in-vivo, thereby allowing unprecedented specific insights into the mechanisms underlying brain plasticity. These quantitative MRI techniques require novel methods for image processing and analysis of longitudinal data allowing for straightforward interpretation and causality inferences.

Adaptive divergence of ancient gene duplicates in the avian MHC class II beta.

Gene duplication and neofunctionalization are known to be important processes in the evolution of phenotypic complexity. They account for important evolutionary novelties that confer ecological adaptation, such as the major histocompatibility complex (MHC), a multigene family crucial to the vertebrate immune system. In birds, two MHC class II β (MHCIIβ) exon 3 lineages have been recently characterized, and two hypotheses for the evolutionary history of MHCIIβ lineages were proposed. These lineages could have arisen either by 1) an ancient duplication and subsequent divergence of one paralog or by 2) recent parallel duplications followed by functional convergence. Here, we compiled a data set consisting of 63 MHCIIβ exon 3 sequences from six avian orders to distinguish between these hypotheses and to understand the role of selection in the divergent evolution of the two avian MHCIIβ lineages. Based on phylogenetic reconstructions and simulations, we show that a unique duplication event preceding the major avian radiations gave rise to two ancestral MHCIIβ lineages that were each likely lost once later during avian evolution. Maximum likelihood estimation shows that following the ancestral duplication, positive selection drove a radical shift from basic to acidic amino acid composition of a protein domain facing the α-chain in the MHCII α β-heterodimer. Structural analyses of the MHCII α β-heterodimer highlight that three of these residues are potentially involved in direct interactions with the α-chain, suggesting that the shift following duplication may have been accompanied by coevolution of the interacting α- and β-chains. These results provide new insights into the long-term evolutionary relationships among avian MHC genes and open interesting perspectives for comparative and population genomic studies of avian MHC evolution.

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.

Population PK-guided simulation study in children exposed to drugs in human milk

Risks of significant infant drug exposure through human milk arepoorly defined due to lack of large-scale PK data. We propose to useBayesian approach based on population PK (popPK)-guided modelingand simulation for risk prediction. As a proof-of-principle study, weexploited fluoxetine milk concentration data from 25 women. popPKparameters including milk-to-plasma ratio (MP ratio) were estimatedfrom the best model. The dose of fluoxetine the breastfed infant wouldreceive through mother's milk, and infant plasma concentrations wereestimated from 1000 simulated mother-infant pairs, using randomassignment of feeding times and milk volume. A conservative estimateof CYP2D6 activity of 20% of the allometrically-adjusted adult valuewas assumed. Derived model parameters, including MP ratio were consistentwith those reported in the literature. Visual predictive check andother model diagnostics showed no signs of model misspecifications.The model simulation predicted that infant exposure levels to fluoxetinevia mother's milk were below 10% of weight-adjusted maternal therapeuticdoses in >99% of simulated infants. Predicted median ratio ofinfant-mother serum levels at steady state was 0.093 (range 0.033-0.31),consistent with literature reported values (mean=0.07; range 0-0.59).Predicted incidence of relatively high infant-mother ratio (>0.2) ofsteady-state serum fluoxetine concentrations was <1.3%. Overall, ourpredictions are consistent with clinical observations. Our approach maybe valid for other drugs, allowing in silico prediction of infant drugexposure risks through human milk. We will discuss application of thisapproach to another drug used in lactating women.

Prediction of infant drug exposure through breastfeeding: population PK modeling and simulation of fluoxetine

BACKGROUND: Risks of significant infant drug exposurethrough breastmilk are poorly defined for many drugs, and largescalepopulation data are lacking. We used population pharmacokinetics(PK) modeling to predict fluoxetine exposure levels ofinfants via mother's milk in a simulated population of 1000 motherinfantpairs.METHODS: Using our original data on fluoxetine PK of 25breastfeeding women, a population PK model was developed withNONMEM and parameters, including milk concentrations, wereestimated. An exponential distribution model was used to account forindividual variation. Simulation random and distribution-constrainedassignment of doses, dosing time, feeding intervals and milk volumewas conducted to generate 1000 mother-infant pairs with characteristicssuch as the steady-state serum concentrations (Css) and infantdose relative to the maternal weight-adjusted dose (relative infantdose: RID). Full bioavailability and a conservative point estimate of1-month-old infant CYP2D6 activity to be 20% of the adult value(adjusted by weigth) according to a recent study, were assumed forinfant Css calculations.RESULTS: A linear 2-compartment model was selected as thebest model. Derived parameters, including milk-to-plasma ratios(mean: 0.66; SD: 0.34; range, 0 - 1.1) were consistent with the valuesreported in the literature. The estimated RID was below 10% in >95%of infants. The model predicted median infant-mother Css ratio was0.096 (range 0.035 - 0.25); literature reported mean was 0.07 (range0-0.59). Moreover, the predicted incidence of infant-mother Css ratioof >0.2 was less than 1%.CONCLUSION: Our in silico model prediction is consistent withclinical observations, suggesting that substantial systemic fluoxetineexposure in infants through human milk is rare, but further analysisshould include active metabolites. Our approach may be valid forother drugs. [supported by CIHR and Swiss National Science Foundation(SNSF)]

Experimental and computational approaches to quantitative proteomics: status quo and outlook.

Proteomics has come a long way from the initial qualitative analysis of proteins present in a given sample at a given time ("cataloguing") to large-scale characterization of proteomes, their interactions and dynamic behavior. Originally enabled by breakthroughs in protein separation and visualization (by two-dimensional gels) and protein identification (by mass spectrometry), the discipline now encompasses a large body of protein and peptide separation, labeling, detection and sequencing tools supported by computational data processing. The decisive mass spectrometric developments and most recent instrumentation news are briefly mentioned accompanied by a short review of gel and chromatographic techniques for protein/peptide separation, depletion and enrichment. Special emphasis is placed on quantification techniques: gel-based, and label-free techniques are briefly discussed whereas stable-isotope coding and internal peptide standards are extensively reviewed. Another special chapter is dedicated to software and computing tools for proteomic data processing and validation. A short assessment of the status quo and recommendations for future developments round up this journey through quantitative proteomics.

SIMULIT : un modèle de simulation pour l'analyse et la planification de l'activité hospitalière

Le modèle développé à l'Institut universitaire de médecine sociale et préventive de Lausanne utilise un programme informatique pour simuler les mouvements d'entrées et de sorties des hôpitaux de soins généraux. Cette simulation se fonde sur les données récoltées de routine dans les hôpitaux; elle tient notamment compte de certaines variations journalières et saisonnières, du nombre d'entrées, ainsi que du "Case-Mix" de l'hôpital, c'est-à-dire de la répartition des cas selon les groupes cliniques et l'âge des patients.

Intravenous streptomycin dosing regimen in a patient undergoing hemodialysis: Plasma level monitoring and pharmacokinetic simulation

Introduction: Streptomycin, as other aminoglycosides, exhibits concentration-dependent bacterial killing but has a narrow therapeutic window. It is primarily eliminated unchanged by the kidneys. Data and dosing information to achieve a safe regimen in patients with chronic renal failure undergoing hemodialysis (HD) are scarce. Although main adverse reactions are related to prolonged, elevated serum concentrations, literature recommendation is to administer streptomycin after each HD. Patients (or Materials) and Methods: We report the case of a patient with end-stage renal failure, undergoing HD, who was successfully treated with streptomycin for gentamicin-resistant Enterococcus faecalis bacteremia with prosthetic arteriovenous fistula infection. Streptomycin was administered intravenously 7.5 mg/kg, 3 hours before each dialysis (3 times a week) during 6 weeks in combination with amoxicillin. Streptomycin plasma levels were monitored with repeated blood sampling before, after, and between HD sessions. A 2-compartment model was used to reconstruct the concentration time profile over days on and off HD. Results: Streptomycin trough plasma-concentration was 2.8 mg/L. It peaked to 21.4 mg/L 30 minutes after intravenous administration, decreased to 18.2 mg/L immediately before HD, and dropped to 4.5 mg/L at the end of a 4-hour HD session. Plasma level increased again to 5.7 mg/L 2 hours after the end of HD and was 2.8 mg/L 48 hours later, before the next administration and HD. The pharmacokinetics of streptomycin was best described with a 2-compartment model. The computer simulation fitted fairly well to the observed concentrations during or between HD sessions. Redistribution between the 2 compartments after the end of HD reproduced the rebound of plasma concentrations after HD. No significant toxicity was observed during treatment. The outcome of the infection was favorable, and no sign of relapse was observed after a follow-up of 3 months. Conclusion: Streptomycin administration of 7.5 mg/kg 3 hours before HD sessions in a patient with end-stage renal failure resulted in an effective and safe dosing regimen. Monitoring plasma levels along with pharmacokinetic simulation document the suitability of this dosing scheme, which should replace current dosage recommendations for streptomycin in HD.

Genome-Wide Prediction Methods in Highly Diverse and Heterozygous Species: Proof-of-Concept through Simulation in Grapevine.

Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.

SwissParam: a fast force field generation tool for small organic molecules.

The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch.