Surface complement C3 fragments and cellular binding of microparticles in patients with SLE

OBJECTIVES: To examine microparticles (MPs) from patients with SLE and healthy controls (HCs) by determining the cellular origin of the MPs, quantifying attached fragments of complement component 3 (C3) and assessing the ability of MPs to bind to circulating phagocytes and erythrocytes. These features may be relevant for clearance of MPs in SLE pathogenesis. METHODS: Attached C3 fragments (C3b, iC3b, C3d), membrane integrity and cell surface markers of MPs from 18 patients with SLE and 11 HCs were measured by adding specific antibodies, 7-aminoactinomycin D (7AAD) and annexin V. MPs from all subjects were labelled with carboxyfluorescein diacetate succinimidyl ester and allowed to bind to autologous phagocytes and erythrocytes in the presence of autologous serum, and the binding to individual cell populations was assessed by flow cytometry. RESULTS: The proportion of MPs bearing C3 fragments was higher in patients with SLE than in HCs (p=0.026), but the amount of opsonising C3b/iC3b molecules was lower (p=0.004). The C3b/iC3b level correlated with the concentration of circulating C3 (r(s)=0.53, p=0.036). Phagocytes and erythrocytes from patients and HCs bound autologous MPs, and granulocytes from patients bound 13% more MPs than those from HCs (p=0.043). The presence of erythrocytes inhibited the MP binding to granulocytes by approximately 50%. CONCLUSIONS: Our demonstration of altered composition of C3 fragments on MPs from patients with SLE, including decreased numbers of opsonising C3 fragments, and competitive binding of MPs to circulating phagocytes and erythrocytes corroborates the hypothesis of defective clearance of apoptotic material in SLE, and indicates that differences in both MP opsonisation and binding of MPs to cells are important in the pathogenesis of SLE

Sparse Discriminant Analysis

tionanddimensionreductionareofgreatimportanceiscommonin Classi cationinhigh-dimensionalfeaturespaceswhereinterpreta-biologicalandmedicalapplications. methodsasmicroarrays,1DNMR,andspectroscopyhavebecomeev- Fortheseapplicationsstandard erydaytoolsformeasuringthousandsoffeaturesinsamplesofinterest. Furthermore,thesamplesareoftencostlyandthereforemanysuch problemshavefewobservationsinrelationtothenumberoffeatures. Traditionallysuchdataareanalyzedby lectionbeforeclassi cation. Weproposeamethodwhichperforms rstperformingafeaturese-lineardiscriminantanalysiswithasparsenesscriterionimposedsuch thattheclassi mergedintooneanalysis. cation, featureselectionanddimensionreductionis thantraditionalfeatureselectionmethodsbasedoncomputationally Thesparsediscriminantanalysisisfaster heavycriteriasuchasWilk'slambda,andtheresultsarebetterwith regardstoclassi tomixturesofGaussianswhichisusefulwhene.g.biologicalclusters cationratesandsparseness.Themethodisextended arepresentwithineachclass. low-dimensionalviewsofthediscriminativedirections. Finally,themethodsproposedprovide 1

How do we sell the hygiene message? With dollars, dong or excreta?

In North and Central Vietnam it is common among farmers to use excreta from the family double vault composting latrine (DVC) as fertilizer in the fields. The official Vietnamese health guidelines stipulate a six-month period of composting before applying excreta to two of their three annual crops. However, farmers in this region cannot afford to follow these guidelines and this paper presents the reasons why

SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling

Applications in biotechnology such as gene expression analysis and image processing have led to a tremendous development of statistical methods with emphasis on reliable solutions to severely underdetermined systems. Furthermore, interpretations of such solutions are of importance, meaning that the surplus of inputs has been reduced to a concise model. At the core of this development are methods which augment the standard linear models for regression, classification and decomposition such that sparse solutions are obtained. This toolbox aims at making public available carefully implemented and well-tested variants of the most popular of such methods for the MATLAB programming environment. These methods consist of easy-to-read yet efficient implementations of various coefficient-path following algorithms and implementations of sparse principal component analysis and sparse discriminant analysis which are not available in MATLAB. The toolbox builds on code made public in 2005 and which has since been used in several studies