An in situ intercomparison exercise on passive samplers for the monitoring of metals, polycyclic aromatic hydrocarbons and pesticides in surface water

An intercomparison exercise on passive samplers (PSs) was organized in summer 2010 for the measurement of selected metals, polycyclic aromatic hydrocarbons (PAHs) and pesticides in surface waters. Various PSs were used and compared at 2 rivers sites and one marine lagoon. A total of 24 laboratories participated. We present selected significant outputs from this exercise, including discussion on quality assurance and quality control for PSs, the interlaboratory variability of field blanks, time weighted average water concentrations and its uncertainties, the representativity of DGT samples, the ability of PSs to lower limits of detection, PAH fingerprints in various PSs compared with spot samples, and the relevance of the permeability reference compounds (PRC) approach for POCIS with pesticides. These in situ intercomparison exercises should enable to progress on the harmonization of practices for the use of passive sampling, especially for priority chemical monitoring and regulatory programs in compliance with the Water Framework Directive (WFD) and Marine Strategy Framework Directive (MSFD)

Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics

[EN] Complexity analysis of glucose time series with Detrended Fluctuation Analysis (DFA) has been proved to be useful for the prediction of type 2 diabetes mellitus (T2DM) development. We propose a modified DFA algorithm, review some of its characteristics and compare it with other metrics derived from continuous glucose monitorization in this setting. Several issues of the DFA algorithm were evaluated: (1) Time windowing: the best predictive value was obtained including all time-windows from 15 minutes to 24 hours. (2) Influence of circadian rhythms: for 48-hour glucometries, DFA alpha scaling exponent was calculated on 24hour sliding segments (1-hour gap, 23-hour overlap), with a median coefficient of variation of 3.2%, which suggests that analysing time series of at least 24-hour length avoids the influence of circadian rhythms. (3) Influence of pretreatment of the time series through integration: DFA without integration was more sensitive to the introduction of white noise and it showed significant predictive power to forecast the development of T2DM, while the pretreated time series did not. (4) Robustness of an interpolation algorithm for missing values: The modified DFA algorithm evaluates the percentage of missing values in a time series. Establishing a 2% error threshold, we estimated the number and length of missing segments that could be admitted to consider a time series as suitable for DFA analysis. For comparison with other metrics, a Principal Component Analysis was performed and the results neatly tease out four different components. The first vector carries information concerned with variability, the second represents mainly DFA alpha exponent, while the third and fourth vectors carry essentially information related to the two "pre-diabetic behaviours" (impaired fasting glucose and impaired glucose tolerance). The scaling exponent obtained with the modified DFA algorithm proposed has significant predictive power for the development of T2DM in a high-risk population compared with other variability metrics or with the standard DFA algorithm.This study has been funded by Instituto de Salud Carlos III through the project PI17/00856 (Co-funded by the European Regional Development Fund, A way to make Europe). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Colás, A.; Vigil, L.; Vargas, B.; Cuesta Frau, D.; Varela, M. (2019). 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Altered Complexity and Correlation Properties of R-R Interval Dynamics Before the Spontaneous Onset of Paroxysmal Atrial Fibrillation. Circulation, 100(20), 2079-2084. doi:10.1161/01.cir.100.20.2079Wang, H., Naghavi, M., Allen, C., Barber, R. M., Bhutta, Z. A., Carter, A., … Coates, M. M. (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1459-1544. doi:10.1016/s0140-6736(16)31012-1Saudek, C. D., Derr, R. L., & Kalyani, R. R. (2006). Assessing Glycemia in Diabetes Using Self-monitoring Blood Glucose and Hemoglobin A1c. JAMA, 295(14), 1688. doi:10.1001/jama.295.14.1688Monnier, L., Colette, C., & Owens, D. R. (2008). Glycemic Variability: The Third Component of the Dysglycemia in Diabetes. Is it Important? How to Measure it? Journal of Diabetes Science and Technology, 2(6), 1094-1100. doi:10.1177/193229680800200618Abdul-Ghani, M. A., Tripathy, D., & DeFronzo, R. A. (2006). Contributions of  -Cell Dysfunction and Insulin Resistance to the Pathogenesis of Impaired Glucose Tolerance and Impaired Fasting Glucose. Diabetes Care, 29(5), 1130-1139. doi:10.2337/dc05-2179(2017). 2. Classification and Diagnosis of Diabetes:Standards of Medical Care in Diabetes—2018. Diabetes Care, 41(Supplement 1), S13-S27. doi:10.2337/dc18-s002Tabák, A. G., Herder, C., Rathmann, W., Brunner, E. J., & Kivimäki, M. (2012). Prediabetes: a high-risk state for diabetes development. The Lancet, 379(9833), 2279-2290. doi:10.1016/s0140-6736(12)60283-9DeFronzo, R. A., Banerji, M. A., Bray, G. A., Buchanan, T. A., Clement, S., … Tripathy, D. (2009). Determinants of glucose tolerance in impaired glucose tolerance at baseline in the Actos Now for Prevention of Diabetes (ACT NOW) study. Diabetologia, 53(3), 435-445. doi:10.1007/s00125-009-1614-2Nathan, D. M., Davidson, M. B., DeFronzo, R. A., Heine, R. J., Henry, R. R., Pratley, R., & Zinman, B. (2007). Impaired Fasting Glucose and Impaired Glucose Tolerance: Implications for care. Diabetes Care, 30(3), 753-759. doi:10.2337/dc07-9920Ogata, H., Tokuyama, K., Nagasaka, S., Tsuchita, T., Kusaka, I., Ishibashi, S., … Yamamoto, Y. (2012). The lack of long-range negative correlations in glucose dynamics is associated with worse glucose control in patients with diabetes mellitus. Metabolism, 61(7), 1041-1050. doi:10.1016/j.metabol.2011.12.007Kohnert, K.-D. (2015). Utility of different glycemic control metrics for optimizing management of diabetes. World Journal of Diabetes, 6(1), 17. doi:10.4239/wjd.v6.i1.17García Maset, L., González, L. B., Furquet, G. L., Suay, F. M., & Marco, R. H. (2016). Study of Glycemic Variability Through Time Series Analyses (Detrended Fluctuation Analysis and Poincaré Plot) in Children and Adolescents with Type 1 Diabetes. Diabetes Technology & Therapeutics, 18(11), 719-724. doi:10.1089/dia.2016.0208Service, F. J., O’Brien, P. C., & Rizza, R. A. (1987). Measurements of Glucose Control. Diabetes Care, 10(2), 225-237. doi:10.2337/diacare.10.2.225Goldberger, A. L., Amaral, L. A. N., Hausdorff, J. M., Ivanov, P. C., Peng, C.-K., & Stanley, H. E. (2002). Fractal dynamics in physiology: Alterations with disease and aging. Proceedings of the National Academy of Sciences, 99(Supplement 1), 2466-2472. doi:10.1073/pnas.012579499Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism, 101(4), 1490-1497. doi:10.1210/jc.2015-4035Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043Pincus, S. M., Gladstone, I. M., & Ehrenkranz, R. A. (1991). A regularity statistic for medical data analysis. Journal of Clinical Monitoring, 7(4), 335-345. doi:10.1007/bf01619355Richman, J. S. (2007). Sample Entropy Statistics and Testing for Order in Complex Physiological Signals. Communications in Statistics - Theory and Methods, 36(5), 1005-1019. doi:10.1080/03610920601036481Platiša, M. M., Bojić, T., Pavlović, S. U., Radovanović, N. N., & Kalauzi, A. (2016). Generalized Poincaré Plots-A New Method for Evaluation of Regimes in Cardiac Neural Control in Atrial Fibrillation and Healthy Subjects. Frontiers in Neuroscience, 10. doi:10.3389/fnins.2016.00038García-Puig, J., Ruilope, L. M., Luque, M., Fernández, J., Ortega, R., & Dal-Ré, R. (2006). Glucose Metabolism in Patients with Essential Hypertension. The American Journal of Medicine, 119(4), 318-326. doi:10.1016/j.amjmed.2005.09.010Lepot, M., Aubin, J.-B., & Clemens, F. (2017). Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment. Water, 9(10), 796. doi:10.3390/w9100796Eke, A., Hermán, P., Bassingthwaighte, J., Raymond, G., Percival, D., Cannon, M., … Ikrényi, C. (2000). Physiological time series: distinguishing fractal noises from motions. Pflügers Archiv - European Journal of Physiology, 439(4), 403-415. doi:10.1007/s004249900135Eke, A., Herman, P., Kocsis, L., & Kozak, L. R. (2002). Fractal characterization of complexity in temporal physiological signals. Physiological Measurement, 23(1), R1-R38. doi:10.1088/0967-3334/23/1/201King, A. B., Philis-Tsimikas, A., Kilpatrick, E. S., Langbakke, I. H., Begtrup, K., & Vilsbøll, T. (2017). A Fixed Ratio Combination of Insulin Degludec and Liraglutide (IDegLira) Reduces Glycemic Fluctuation and Brings More Patients with Type 2 Diabetes Within Blood Glucose Target Ranges. Diabetes Technology & Therapeutics, 19(4), 255-264. doi:10.1089/dia.2016.0405Colas, A., Vigil, L., Rodríguez de Castro, C., Vargas, B., & Varela, M. (2018). New insights from continuous glucose monitoring into the route to diabetes. Diabetes/Metabolism Research and Reviews, 34(5), e3002. doi:10.1002/dmrr.3002Henriques, T., Munshi, M. N., Segal, A. R., Costa, M. D., & Goldberger, A. L. 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Modern Subsurface Bacteria in Pristine 2.7 Ga-Old Fossil Stromatolite Drillcore Samples from the Fortescue Group, Western Australia

Several abiotic processes leading to the formation of life-like signatures or later contamination with actual biogenic traces can blur the interpretation of the earliest fossil record. In recent years, a large body of evidence showing the occurrence of diverse and active microbial communities in the terrestrial subsurface has accumulated. Considering the time elapsed since Archaean sedimentation, the contribution of subsurface microbial communities postdating the rock formation to the fossil biomarker pool and other biogenic remains in Archaean rocks may be far from negligible.In order to evaluate the degree of potential contamination of Archean rocks by modern microorganisms, we looked for the presence of living indigenous bacteria in fresh diamond drillcores through 2,724 Myr-old stromatolites (Tumbiana Formation, Fortescue Group, Western Australia) using molecular methods based on the amplification of small subunit ribosomal RNA genes (SSU rDNAs). We analyzed drillcore samples from 4.3 m and 66.2 m depth, showing signs of meteoritic alteration, and also from deeper "fresh" samples showing no apparent evidence for late stage alteration (68 m, 78.8 m, and 99.3 m). We also analyzed control samples from drilling and sawing fluids and a series of laboratory controls to establish a list of potential contaminants introduced during sample manipulation and PCR experiments. We identified in this way the presence of indigenous bacteria belonging to Firmicutes, Actinobacteria, and Alpha-, Beta-, and Gammaproteobacteria in aseptically-sawed inner parts of drillcores down to at least 78.8 m depth.The presence of modern bacterial communities in subsurface fossil stromatolite layers opens the possibility that a continuous microbial colonization had existed in the past and contributed to the accumulation of biogenic traces over geological timescales. This finding casts shadow on bulk analyses of early life remains and makes claims for morphological, chemical, isotopic, and biomarker traces syngenetic with the rock unreliable in the absence of detailed contextual analyses at microscale

Pratiques d'échantillonnage et de conditionnement (mesure de micropolluants en assainissement). Point des activités des sous-groupes techniques (SGT)

National audiencePrésentations des précautions spécifiques à prendre lors de la recherche de micropolluants dans les eaux usées (agitation, nettoyage, vérification, matériel spécifique)

Risques de contamination des échantillons lors des opérations d'échantillonnage : synthèse opérationnelle (eau et sédiment)

Depuis plusieurs années, AQUAREF réalise des études visant à identifier et quantifier les risques de contamination des échantillons liés à l'utilisation de différents types de matériels d'échantillonnage. Plus récemment, la question a été élargie à l'impact de l'opérateur (cas des parabènes en eau de surface). L'objectif de cette note est de réaliser une synthèse de ces études afin d'informer les gestionnaires et utilisateurs finaux de la donnée, des risques identifiés lors de l'échantillonnage et leur permettre de préciser, si besoin, des recommandations opérationnelles auprès de leurs prestataires. Cette note émet également des recommandations à destination des opérateurs de prélèvement et des gestionnaires

Substances prioritaires de la directive cadre européenne sur l`eau : difficultés analytiques pour la surveillance du milieu et l`application des seuils de qualité

With the new Water Directive the European Union has given a new dimension to environmental monitoring. It is now necessary to follow and account for the ecological and chemical status of aquatic ecosystems. This status should improve with the objective of "good status" in 2015 for all waters of Member States. Such a regulation will lead to an intensification of chemical contaminants control and has important metrological implications. In order to ensure the comparability of data at European scale, it appears necessary to develop internationally validated standard methods and to organise intercomparison exercises for analytical laboratories.L`Union Européenne vient d`introduire, avec la nouvelle réglementation sur l`eau, une dimension nouvelle dans le domaine de la surveillance de l`environnement. Il ne s`agit plus seulement de mesurer un état de contamination mais de suivre et rendre compte de l`état de qualité écologique et chimique des eaux. Cet état doit s`améliorer avec un objectif exigeant de « bon état » dès 2015 pour toutes les masses d`eau en Europe. Une telle mesure, qui doit se traduire notamment par une intensification du contrôle des contaminants chimiques, a des conséquences importantes en terme de développement analytique. Afin d`assurer la comparabilité des données au niveau européen, il apparaît indispensable de développer des méthodes de mesure appropriées et validées au plan international et d`organiser des exercices d`intercomparaison, voire à plus long terme d`organiser un réseau de laboratoires qui assurerait le suivi des pratiques de qualité

Exceptional Extension of Benign Angiomyolipoma in the Renal Vein.

TEACHING POINT: When a renal angiomyolipoma (AML) is incidentally detected on imaging, the venous system should be assessed for intravascular fat component