Determination of diquat by flow injection-chemiluminescence

A simple, economic, sensitive and rapid method for the determination of the pesticide diquat was described. This new method was based on the coupling of flow injection analysis methodology and direct chemiluminescent detection; to the authors' knowledge, this approach had not been used up to now with this pesticide. It was based on its oxidation with ferricyanide in alkaline medium; significant improvements in the analytical signal were achieved by using high temperatures and quinine as sensitiser. Its high throughput (144 h(-1)), together with its low limit of detection (2 ng mL(-1)), achieved without need of preconcentration steps, permitted the reliable quantification of diquat over the linear range of (0.01-0.6) mu g mL(-1) in samples from different origins (river, tap, mineral and ground waters), even in the presence of a 40-fold concentration of paraquat, a pesticide commonly present in the commercial formulations of diquat.López-Paz, JL.; Catalá-Icardo, M.; Antón Garrido, B. (2009). Determination of diquat by flow injection-chemiluminescence. Analytical and Bioanalytical Chemistry. 394(4):1073-1079. doi:10.1007/s00216-009-2609-zS107310793944Hayes WJ Jr, Laws ER Jr (1991) Handbook of pesticide toxicology, Academic Press, San DiegoUS Environmental Protection Agency. http://www.epa.gov/06WDW/contaminants/dw_contamfs/diquat.html (accessed in August 2008)Horwitz W (2000) Official methods of analysis of AOAC International 17th edition. AOAC International, Gaithersburg, MD, USAHara S, Sasaki N, Takase D, Shiotsuka S, Ogata K, Futagami K, Tamura K (2007) Anal Sci 23(5):523–531Rial Otero R, Cancho Grande B, Pérez Lamela C, Simal Gandara J, Aria Estevez M (2006) J Chromatogr Sci 44(9):539–542Aramendia MA, Borau V, Lafont F, Marinas JM, Moreno JM, Porras JM, Urbano FJ (2006) Food Chem 97(1):181–188Nuñez O, Moyano E, Galceran MT (2004) Anal Chim Acta 525(2):183–190Martinez Vidal JL, Belmonte Vega A, Sanchez Lopez FJ, Garrido Frenich AJ (2004) Chromatogr A 1050(2):179–184Lee XP, Kumazawa T, Fujishiro M, Hasegawa C, Arinobu T, Seno H, Sato K (2004) J Mass Spectrom 39(10):1147–1152De Almeida RM, Yonamine M (2007) J Chromatogr B 853(1–2):260–264De Souza D, Machado SAS (2006) Electroanalysis 18(9):862–872De Souza D, Da Silva MRC, Machado SAS (2006) Electroanalysis 18(23):2305–2313Picó Y, Rodriguez R, Manes J (2003) Trends Anal Chem 22(3):133–151Ishiwata T (2004) Bunseki Kagaku 53(8):863–864Carneiro MC, Puignou L, Galcerán MT (2000) Anal Chim Acta 408:263Luque M, Rios A, Valcarcel M (1998) Analyst 123(11):2383–2387Perez Ruiz T, Martínez Lozano C, Tomas V (1991) Int J Environ Anal Chem 44(4):243–252Perez Ruiz T, Martínez Lozano C, Tomas V (1991) Anal Chim Acta 244(1):99–104Townshend A (1990) Analyst 115:495–500López Paz JL, Catalá Icardo M (2008) Anal Chim Acta 625:173–179Pawlicová Z, Sahuquillo I, Catalá Icardo M, García Mateo JV, Martínez Calatayud J (2006) Anal Sci 22:29–34Albert García JR, Catalá Icardo M, Martínez Calatayud J (2006) Talanta 69:608–614Tomlin CDS (1997) The pesticide manual, 11th edn.The British Crop Protection CouncilUKCatalá-Icardo M, Martínez-Calatayud J (2008) Crit Rev Anal Chem 38:118–130Ministerio de Medio Ambiente y Medio Rural y Marino. http://www.marm.es/ (accessed in September 2008)US Environmental Protection Agency. http://www.epa.gov/OGWWDW/contaminants (accessed in October 2008

HLA-DRB1 association with Henoch-Schonlein purpura

Objective: Henoch-Schönlein purpura (HSP) is the most common vasculitis in children but it is not exceptional in adults. Increased familial occurrence supports a genetic predisposition for HSP. In this context, an association with the human leukocyte antigen-HLA-DRB1*01 phenotype has been suggested in Caucasian individuals with HSP. However, data on the potential association of HSP with HLA-DRB1*01 were based on small case series. To further investigate this issue, we performed HLA-DRB1 genotyping of the largest series of HSP patients ever assessed for genetic studies in Caucasians. Methods: 342 Spanish patients diagnosed with HSP fulfilling the American College of Rheumatology and the Michel et al classification criteria, and 303 sex and ethnically matched controls were assessed. HLA-DRB1 alleles were determined using a PCR-Sequence-Specific-Oligonucleotide Probe (PCR-SSOP) method. Results: A statistically significant increase of HLA-DRB1*01 in HSP patients when compared with controls was found (43% vs 7%, respectively; p<0.001; odds ratio-OR=2.03 [1.43-2.87]). It was due to the increased frequency of HLA-DRB1*0103 phenotype in HSP (14% vs 2%; p<0.001; OR=8.27 [3.46-23.9]). These results remained statistically significant after adjusting for Bonferroni correction. In contrast, a statistically significant decreased frequency of the HLA-DRB1*0301 phenotype was observed in patients compared to controls (5.6% vs 18.1%, respectively; p<0.001, OR=0.26 [0.14-0.47]), even after adjustment for Bonferroni correction. No HLA-DRB1 association with specific features of the disease was found. Conclusion: Our study confirms an association of HSP with HLA-DRB1*01 in Caucasians. Also, a protective effect against the development of HSP appears to exist in Caucasians carrying the HLA-DRB1*03 phenotype

Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions