Adjustable Suture Removal: Increasing Aqueous Flow in "Safe" Trabeculectomy.

Purpose: Aqueous flow through trabeculectomy blebs has been suggested to influence filtration bleb survival. We investigated the relationship between the requirement to increase aqueous flow via adjustable suture removal and surgical outcomes following "safe trabeculectomy" with mitomycin C (MMC). Methods: 62 consecutive eyes of 53 patients underwent fornix based trabeculectomy with adjustable sutures, intraoperative MMC and intensive postoperative steroids. Subconjunctival antimetabolite injections and bleb needlings were administered according to bleb vascularity and IOP trends. Main outcome measures were: success rates (definition: IOP≤21mmHg and 20% IOP reduction); number of antimetabolite injections; bleb needlings; number of of eyes recommencing glaucoma medications and complications. Results: Mean age was 70.4±16.0 years (mean± SD); mean preoperative IOP was 24.5±9.1 mmHg and decreased to 12.3±8.9mmHg postoperatively. Mean number of sutures was 2.6 ± 0.7. Eyes were divided into 2 groups in relation to the number of sutures removed. The number of subconjunctival MMC injections required for those requiring 2 suture removals was significantly greater than those requiring 1 suture removal (p<0.05) The number of needlings and 5FU injections also increased but did not reach significance (p=0.09 and p=0.34 respectively). Least-squared linear regression analysis showed the number of needlings required had a statistically significant (p=0.05) trend with respect to time elapsed between surgery and first suture removal. No other interventions had significant trends. Mean time between surgery and suture removal was: 4.2±9.2 weeks (suture #1) and 5.7±9.7 weeks (suture#2). Antiglaucoma medication was restarted in only 5 eyes. Postoperative complications were infrequent: Seidel (3.2%), peripheral choroidal effusions at any time (3.2%), and shallow anterior chamber (1.6%). Conclusion: Eyes requiring a greater number of suture removals required a significantly greater number of antifibrosis interventions. The time elapsed before suture removal was inversely related to the number of postoperative needlings, suggesting these eyes may have decreased aqueous production and therefore require aggressive post-operative management to prevent bleb failure.

Application of text-mining for updating protein post-translational modification annotation in UniProtKB.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

Mining the human phenome using allelic scores that index biological intermediates.

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approach's properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

Proteomics of methylene blue photo-treated plasma before and after removal of the dye by an absorbent filter.

Methylene blue (MB) and light are used for virus inactivation of plasma for transfusion. However, the presence of MB has been the subject of concern, and efforts have been made to efficiently remove the dye after photo-treatment. For this study, plasma was collected by apheresis from 10 donors (group A), then treated using the MacoPharma THERAFLEX procedure (MB; 1 microM, and light exposure; 180 J/cm(2)) (group B), and finally filtered in order to remove the dye (group C). Proteins were analyzed by two-dimensional electrophoresis, and peptides showing modifications were characterized by mass spectrometry. Clottable and antigenic fibrinogen levels, as well as fibrin polymerization time were measured. Analyses of the gels focused on a region corresponding to pI between 4.5 and 6.5, and M(r) from 7000 to 58 000. In this area, 387 +/- 47 spots matched, and four of these spots presented significant modifications. They corresponded to changes of the gamma-chain of fibrinogen, of transthyretin, and of apolipoprotein A-I, respectively. A decrease of clottable fibrinogen and a prolongation of fibrin polymerization time were observed in groups B and C. Removal of MB by filtration was not responsible for additional protein alterations. The effect of over-treatment of plasma by very high concentrations of MB (50 microM) in association with prolonged light exposure (3 h) was also analyzed, and showed complex alterations of most of the plasma proteins, including fibrinogen gamma-chain, transthyretin, and apolipoprotein A-I. Our data indicates that MB treatment at high concentration and prolonged illumination severely injure plasma proteins. By contrast, at the MB concentration used to inactivate viruses, damages are apparently very restricted.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Removal of unusual, large high-velocity metallic maxillary sinus foreign bodies by a modified free bone flap technique.

Metallic foreign bodies are rarely found in the maxillary sinus, and usually they have a dental origin.Potential complications related to foreign bodies include recurrent sinusitis, rhinolith formation, cutaneous fistula,chemical poisoning, facial neuralgic pain and even malignancies.Two main surgical approaches are currently used for the removal of foreign bodies in the maxillary sinus: the bone flap and the endoscopic sinus techniques. We are reporting two unusual cases of large high-velocity foreign bodies removed by a modified maxillary lateral antrotomy,with free bone flap repositioning and fixation with a titanium miniplate.

6.2

Airborne particles can come from a variety of sources and contain variable chemical constituents. Some particles are formed by natural processes, such as volcanoes, erosion, sea spray, and forest fires, while other are formed by anthropogenic processes, such as industrial- and motor vehicle-related combustion, road-related wear, and mining. In general, larger particles (those greater than 2.5 μm) are formed by mechanical processes, while those less than 2.5 μm are formed by combustion processes. The chemical composition of particles is highly influenced by the source: for combustion-related particles, factors such as temperature of combustion, fuel type, and presence of oxygen or other gases can also have a large impact on PM composition. These differences can often be observed at a regional level, such as the greater sulphate-composition of PM in regions that burn coal for electricity production (which contains sulphur) versus regions that do not. Most countries maintain air monitoring networks, and studies based on the resulting data are the most common basis for epidemiology studies on the health effects of PM. Data from these monitoring stations can be used to evaluate the relationship between community-level exposure to ambient particles and health outcomes (i.e., morbidity or mortality from various causes). Respiratory and cardiovascular outcomes are the most commonly assessed, although studies have also considered other related specific outcomes such as diabetes and congenital heart disease. The data on particle characteristics is usually not very detailed and most often includes some combination of PM2.5, PM10, sulphate, and NO2. Other descriptors that are less commonly found include particle number (ultrafine particles), metal components of PM, local traffic intensity, and EC/OC. Measures of association are usually reported per 10 μg/m3 or interquartile range increase in pollutant concentration. As the exposure data are taken from regional monitoring stations, the measurements are not representative of an individual's exposure. Particle size is an important descriptor for understanding where in the human respiratory system the particles will deposit: as a general rule, smaller particles penetrate to deeper regions of the lungs. Initial studies on the health effects of particulate matter focused on mass of the particles, including either all particles (often termed total suspended particulate or TSP) or PM10 (all particles with an aerodynamic diameter less than 10 μm). More recently, studies have considered both PM10 and PM2.5, with the latter corresponding more directly to combustion-related processes. UFPs are a dominant source of particles in terms of PNC, yet are negligible in terms of mass. Very few epidemiology studies have measured the effect of UFPs on health; however, the numbers of studies on this topic are increasing. In addition to size, chemical composition is of importance when understanding the toxicity of particles. Some studies consider the composition of particles in addition to mass; however this is not common, in part due the cost and labour involved in such analyses.

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.