An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Prediction of enzymatic infarct size in ST-segment elevation myocardial infarction

Objectives Predictors of adverse outcomes following myocardial infarction (MI) are well established; however, little is known about what predicts enzymatically estimated infarct size in patients with acute ST-elevation MI. The Complement And Reduction of INfarct size after Angioplasty or Lytics trials of pexelizumab used creatine kinase (CK)-MB area under the curve to determine infarct size in patients treated with primary percutaneous coronary intervention (PCI) or fibrinolysis. Methods Prediction of infarct size was carried out by measuring CK-MB area under the curve in patients with ST-segment elevation MI treated with reperfusion therapy from January 2000 to April 2002. Infarct size was calculated in 1622 patients (PCI=817; fibrinolysis=805). Logistic regression was used to examine the relationship between baseline demographics, total ST-segment elevation, index angiographic findings (PCI group), and binary outcome of CK-MB area under the curve greater than 3000 ng/ml. Results Large infarcts occurred in 63% (515) of the PCI group and 69% (554) of the fibrinolysis group. Independent predictors of large infarcts differed depending on mode of reperfusion. In PCI, male sex, no prior coronary revascularization and diabetes, decreased systolic blood pressure, sum of ST-segment elevation, total (angiographic) occlusion, and nonright coronary artery culprit artery were independent predictors of larger infarcts (C index=0.73). In fibrinolysis, younger age, decreased heart rate, white race, no history of arrhythmia, increased time to fibrinolytic therapy in patients treated up to 2 h after symptom onset, and sum of ST-segment elevation were independently associated with a larger infarct size (C index=0.68). Conclusion Clinical and patient data can be used to predict larger infarcts on the basis of CK-MB quantification. These models may be helpful in designing future trials and in guiding the use of novel pharmacotherapies aimed at limiting infarct size in clinical practice. Coron Artery Dis 23:118-125 (C) 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.

Phenolic compounds from Rosemary (Rosmarinus officinalis L.) attenuate oxidative stress and reduce blood cholesterol concentrations in diet-induced hypercholesterolemic rats

Abstract Background Phenolic compounds combine antioxidant and hypocholesterolemic activities and, consequently, are expected to prevent or minimize cardiometabolic risk. Methods To evaluate the effect of an aqueous extract (AQ) and non-esterified phenolic fraction (NEPF) from rosemary on oxidative stress in diet-induced hypercholesterolemia, 48 male 4-week old Wistar rats were divided into 6 groups: 1 chow diet group (C) and 5 hypercholesterolemic diet groups, with 1 receiving water (HC), 2 receiving AQ at concentrations of 7 and 140 mg/kg body weight (AQ70 and AQ140, respectively), and 2 receiving NEPF at concentrations of 7 and 14 mg/kg body weight (NEPF7 and NEPF14, respectively) by gavage for 4 weeks. Results In vitro, both AQ and NEPF had remarkable antioxidant activity in the 2,2-diphenyl-1-picrylhydrazyl (DPPH●) assay, which was similar to BHT. In vivo, the group that received AQ at 70 mg/kg body weight had lower serum total cholesterol (−39.8%), non-HDL-c (−44.4%) and thiobarbituric acid reactive substance (TBARS) levels (−37.7%) compared with the HC group. NEPF (7 and 14 mg/kg) reduced the tissue TBARS levels and increased the activity of tissular antioxidant enzymes (superoxide dismutase, catalase and glutathione peroxidase). Neither AQ nor NEPF was able to ameliorate the alterations in the hypercholesterolemic diet-induced fatty acid composition in the liver. Conclusions These data suggest that phenolic compounds from rosemary ameliorate the antioxidant defense in different tissues and attenuate oxidative stress in diet-induced hypercholesterolemic rats, whereas the serum lipid profile was improved only in rats that received the aqueous extract.

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties.

Theoretical studies of Membrane Proteins : Properties, Prediction Methods and Genome-wide analysis

Membrane proteins are a large and important class of proteins. They are responsible for several of the key functions in a living cell, e.g. transport of nutrients and ions, cell-cell signaling, and cell-cell adhesion. Despite their importance it has not been possible to study their structure and organization in much detail because of the difficulty to obtain 3D structures. In this thesis theoretical studies of membrane protein sequences and structures have been carried out by analyzing existing experimental data. The data comes from several sources including sequence databases, genome sequencing projects, and 3D structures. Prediction of the membrane spanning regions by hydrophobicity analysis is a key technique used in several of the studies. A novel method for this is also presented and compared to other methods. The primary questions addressed in the thesis are: What properties are common to all membrane proteins? What is the overall architecture of a membrane protein? What properties govern the integration into the membrane? How many membrane proteins are there and how are they distributed in different organisms? Several of the findings have now been backed up by experiments. An analysis of the large family of G-protein coupled receptors pinpoints differences in length and amino acid composition of loops between proteins with and without a signal peptide and also differences between extra- and intracellular loops. Known 3D structures of membrane proteins have been studied in terms of hydrophobicity, distribution of secondary structure and amino acid types, position specific residue variability, and differences between loops and membrane spanning regions. An analysis of several fully and partially sequenced genomes from eukaryotes, prokaryotes, and archaea has been carried out. Several differences in the membrane protein content between organisms were found, the most important being the total number of membrane proteins and the distribution of membrane proteins with a given number of transmembrane segments. Of the properties that were found to be similar in all organisms, the most obvious is the bias in the distribution of positive charges between the extra- and intracellular loops. Finally, an analysis of homologues to membrane proteins with known topology uncovered two related, multi-spanning proteins with opposite predicted orientations. The predicted topologies were verified experimentally, providing a first example of "divergent topology evolution".

Multivariate spectroscopic methods for the analysis of solutions

In this thesis some multivariate spectroscopic methods for the analysis of solutions are proposed. Spectroscopy and multivariate data analysis form a powerful combination for obtaining both quantitative and qualitative information and it is shown how spectroscopic techniques in combination with chemometric data evaluation can be used to obtain rapid, simple and efficient analytical methods. These spectroscopic methods consisting of spectroscopic analysis, a high level of automation and chemometric data evaluation can lead to analytical methods with a high analytical capacity, and for these methods, the term high-capacity analysis (HCA) is suggested. It is further shown how chemometric evaluation of the multivariate data in chromatographic analyses decreases the need for baseline separation. The thesis is based on six papers and the chemometric tools used are experimental design, principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), partial least squares regression (PLS) and parallel factor analysis (PARAFAC). The analytical techniques utilised are scanning ultraviolet-visible (UV-Vis) spectroscopy, diode array detection (DAD) used in non-column chromatographic diode array UV spectroscopy, high-performance liquid chromatography with diode array detection (HPLC-DAD) and fluorescence spectroscopy. The methods proposed are exemplified in the analysis of pharmaceutical solutions and serum proteins. In Paper I a method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of UV-Vis spectroscopy, orthogonal signal correction and multivariate calibration with PLS and SIMCA classification. Paper II proposes a new method for the rapid determination of pharmaceutical solutions by the use of non-column chromatographic diode array UV spectroscopy, i.e. a conventional HPLC-DAD system without any chromatographic column connected. In Paper III an investigation is made of the ability of a control sample, of known content and identity to diagnose and correct errors in multivariate predictions something that together with use of multivariate residuals can make it possible to use the same calibration model over time. In Paper IV a method is proposed for simultaneous determination of serum proteins with fluorescence spectroscopy and multivariate calibration. Paper V proposes a method for the determination of chromatographic peak purity by means of PCA of HPLC-DAD data. In Paper VI PARAFAC is applied for the decomposition of DAD data of some partially separated peaks into the pure chromatographic, spectral and concentration profiles.

Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods. Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules. Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments. Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV. Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed. Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

Prediction of clinical toxicity in locally advanced head and neck cancer patients by radio-induced apoptosis in peripheral blood lymphocytes (PBLs)

[EN] Head and neck cancer is treated mainly by surgery and radiotherapy. Normal tissue toxicity due to x-ray exposure is a limiting factor for treatment success. Many efforts have been employed to develop predictive tests applied to clinical practice. Determination of lymphocyte radio-sensitivity by radio-induced apoptosis arises as a possible method to predict tissue toxicity due to radiotherapy. The aim of the present study was to analyze radio-induced apoptosis of peripheral blood lymphocytes in head and neck cancer patients and to explore their role in predicting radiation induced toxicity. Seventy nine consecutive patients suffering from head and neck cancer, diagnosed and treated in our institution, were included in the study. Toxicity was evaluated using the Radiation Therapy Oncology Group scale. Peripheral blood lymphocytes were isolated and irradiated at 0, 1, 2 and 8 Gy during 24 hours. Apoptosis was measured by flow cytometry using annexin V/propidium iodide. Lymphocytes were marked with CD45 APC-conjugated monoclonal antibody. Radiation-induced apoptosis increased in order to radiation dose and fitted to a semi logarithmic model defined by two constants: α and β. α, as the origin of the curve in the Y axis determining the percentage of spontaneous cell death, and β, as the slope of the curve determining the percentage of cell death induced at a determined radiation dose, were obtained. β value was statistically associated to normal tissue toxicity in terms of severe xerostomia, as higher levels of apoptosis were observed in patients with low toxicity (p = 0.035; Exp(B) 0.224, I.C.95% (0.060-0.904)). These data agree with our previous results and suggest that it is possible to estimate the radiosensitivity of peripheral blood lymphocytes from patients determining the radiation induced apoptosis with annexin V/propidium iodide staining. β values observed define an individual radiosensitivity profile that could predict late toxicity due to radiotherapy in locally advanced head and neck cancer patients. Anyhow, prospective studies with different cancer types and higher number of patients are needed to validate these results.

Development of original analytical methods for the therapeutic drug monitoring of CNS druges: Antipsychotics, Antidepressants and Anxiolytics-hypnotics

Great strides have been made in the last few years in the pharmacological treatment of neuropsychiatric disorders, with the introduction into the therapy of several new and more efficient agents, which have improved the quality of life of many patients. Despite these advances, a large percentage of patients is still considered “non-responder” to the therapy, not drawing any benefits from it. Moreover, these patients have a peculiar therapeutic profile, due to the very frequent application of polypharmacy, attempting to obtain satisfactory remission of the multiple aspects of psychiatric syndromes. Therapy is heavily individualised and switching from one therapeutic agent to another is quite frequent. One of the main problems of this situation is the possibility of unwanted or unexpected pharmacological interactions, which can occur both during polypharmacy and during switching. Simultaneous administration of psychiatric drugs can easily lead to interactions if one of the administered compounds influences the metabolism of the others. Impaired CYP450 function due to inhibition of the enzyme is frequent. Other metabolic pathways, such as glucuronidation, can also be influenced. The Therapeutic Drug Monitoring (TDM) of psychotropic drugs is an important tool for treatment personalisation and optimisation. It deals with the determination of parent drugs and metabolites plasma levels, in order to monitor them over time and to compare these findings with clinical data. This allows establishing chemical-clinical correlations (such as those between administered dose and therapeutic and side effects), which are essential to obtain the maximum therapeutic efficacy, while minimising side and toxic effects. It is evident the importance of developing sensitive and selective analytical methods for the determination of the administered drugs and their main metabolites, in order to obtain reliable data that can correctly support clinical decisions. During the three years of Ph.D. program, some analytical methods based on HPLC have been developed, validated and successfully applied to the TDM of psychiatric patients undergoing treatment with drugs belonging to following classes: antipsychotics, antidepressants and anxiolytic-hypnotics. The biological matrices which have been processed were: blood, plasma, serum, saliva, urine, hair and rat brain. Among antipsychotics, both atypical and classical agents have been considered, such as haloperidol, chlorpromazine, clotiapine, loxapine, risperidone (and 9-hydroxyrisperidone), clozapine (as well as N-desmethylclozapine and clozapine N-oxide) and quetiapine. While the need for an accurate TDM of schizophrenic patients is being increasingly recognized by psychiatrists, only in the last few years the same attention is being paid to the TDM of depressed patients. This is leading to the acknowledgment that depression pharmacotherapy can greatly benefit from the accurate application of TDM. For this reason, the research activity has also been focused on first and second-generation antidepressant agents, like triciclic antidepressants, trazodone and m-chlorophenylpiperazine (m-cpp), paroxetine and its three main metabolites, venlafaxine and its active metabolite, and the most recent antidepressant introduced into the market, duloxetine. Among anxiolytics-hypnotics, benzodiazepines are very often involved in the pharmacotherapy of depression for the relief of anxious components; for this reason, it is useful to monitor these drugs, especially in cases of polypharmacy. The results obtained during these three years of Ph.D. program are reliable and the developed HPLC methods are suitable for the qualitative and quantitative determination of CNS drugs in biological fluids for TDM purposes.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Boron determination in biological samples - Intercomparison of three analytical methods to assist development of a treatment protocol for neoplastic diseases of the liver with Boron Neutron Capture Therapy

Die Bor-Neuroneneinfang-Therapie (engl.: Boron Neutron Capture Therapy, BNCT) ist eine indirekte Strahlentherapie, welche durch die gezielte Freisetzung von dicht ionisierender Strahlung Tumorzellen zerstört. Die freigesetzten Ionen sind Spaltfragmente einer Kernreaktion, bei welcher das Isotop 10B ein niederenergetisches (thermisches) Neutron einfängt. Das 10B wird durch ein spezielles Borpräparat in den Tumorzellen angereichert, welches selbst nicht radioaktiv ist. rnAn der Johannes Gutenberg-Universität Mainz wurde die Forschung für die Anwendung eines klinischen Behandlungsprotokolls durch zwei Heilversuche bei Patienten mit kolorektalen Lebermetastasen an der Universität Pavia, Italien, angeregt, bei denen die Leber außerhalb des Körpers in einem Forschungsreaktor bestrahlt wurde. Als erster Schritt wurde in Kooperation verschiedener universitärer Institute eine klinische Studie zur Bestimmung klinisch relevanter Parameter wie der Borverteilung in verschiedenen Geweben und dem pharmakokinetischen Aufnahmeverhalten des Borpräparates initiiert.rnDie Borkonzentration in den Gewebeproben wurde hinsichtlich ihrer räumlichen Verteilung in verschiedenen Zellarealen bestimmt, um mehr über das Aufnahmeverhalten der Zellen für das BPA im Hinblick auf ihre biologischen Charakteristika zu erfahren. Die Borbestimung wurde per Quantitative Neutron Capture Radiography, Prompt Gamma Activation Analysis und Inductively Coupled Plasma Mass Spectroscopy parallel zur histologischen Analyse des Gewebes durchgeführt. Es war möglich zu zeigen, dass in Proben aus Tumorgewebe und aus tumorfreiem Gewebe mit unterschiedlichen morphologischen Eigenschaften eine sehr heterogene Borverteilung vorliegt. Die Ergebnisse der Blutproben werden für die Erstellung eines pharmakokinetischen Modells verwendet und sind in Übereinstimmung mit existierenden pharmakokinetische Modellen. Zusätzlich wurden die Methoden zur Borbestimmung über speziell hergestellte Referenzstandards untereinander verglichen. Dabei wurde eine gute Übereinstimmung der Ergebnisse festgestellt, ferner wurde für alle biologischen Proben Standardanalyseprotokolle erstellt.rnDie bisher erhaltenen Ergebnisse der klinischen Studie sind vielversprechend, lassen aber noch keine endgültigen Schlussfolgerungen hinsichtlich der Wirksamkeit von BNCT für maligne Lebererkrankungen zu. rn

Protein and lipid binding parameters in rainbow trout (Oncorhynchus mykiss) blood and liver fractions to extrapolate from an in vitro metabolic degradation assay to in vivo bioaccumulation potential of hydrophobic organic chemicals

Binding of hydrophobic chemicals to colloids such as proteins or lipids is difficult to measure using classical microdialysis methods due to low aqueous concentrations, adsorption to dialysis membranes and test vessels, and slow kinetics of equilibration. Here, we employed a three-phase partitioning system where silicone (polydimethylsiloxane, PDMS) serves as a third phase to determine partitioning between water and colloids and acts at the same time as a dosing device for hydrophobic chemicals. The applicability of this method was demonstrated with bovine serum albumin (BSA). Measured binding constants (K(BSAw)) for chlorpyrifos, methoxychlor, nonylphenol, and pyrene were in good agreement with an established quantitative structure-activity relationship (QSAR). A fifth compound, fluoxypyr-methyl-heptyl ester, was excluded from the analysis because of apparent abiotic degradation. The PDMS depletion method was then used to determine partition coefficients for test chemicals in rainbow trout (Oncorhynchus mykiss) liver S9 fractions (K(S9w)) and blood plasma (K(bloodw)). Measured K(S9w) and K(bloodw) values were consistent with predictions obtained using a mass-balance model that employs the octanol-water partition coefficient (K(ow)) as a surrogate for lipid partitioning and K(BSAw) to represent protein binding. For each compound, K(bloodw) was substantially greater than K(S9w), primarily because blood contains more lipid than liver S9 fractions (1.84% of wet weight vs 0.051%). Measured liver S9 and blood plasma binding parameters were subsequently implemented in an in vitro to in vivo extrapolation model to link the in vitro liver S9 metabolic degradation assay to in vivo metabolism in fish. Apparent volumes of distribution (V(d)) calculated from the experimental data were similar to literature estimates. However, the calculated binding ratios (f(u)) used to relate in vitro metabolic clearance to clearance by the intact liver were 10 to 100 times lower than values used in previous modeling efforts. Bioconcentration factors (BCF) predicted using the experimental binding data were substantially higher than the predicted values obtained in earlier studies and correlated poorly with measured BCF values in fish. One possible explanation for this finding is that chemicals bound to proteins can desorb rapidly and thus contribute to metabolic turnover of the chemicals. This hypothesis remains to be investigated in future studies, ideally with chemicals of higher hydrophobicity.

Modeling of red blood cell life-spans in hematologically normal populations

Despite the impact of red blood cell (RBC) Life-spans in some disease areas such as diabetes or anemia of chronic kidney disease, there is no consensus on how to quantitatively best describe the process. Several models have been proposed to explain the elimination process of RBCs: random destruction process, homogeneous life-span model, or a series of 4-transit compartment model. The aim of this work was to explore the different models that have been proposed in literature, and modifications to those. The impact of choosing the right model on future outcomes prediction--in the above mentioned areas--was also investigated. Both data from indirect (clinical data) and direct life-span measurement (biotin-labeled data) methods were analyzed using non-linear mixed effects models. Analysis showed that: (1) predictions from non-steady state data will depend on the RBC model chosen; (2) the transit compartment model, which considers variation in life-span in the RBC population, better describes RBC survival data than the random destruction or homogenous life-span models; and (3) the additional incorporation of random destruction patterns, although improving the description of the RBC survival data, does not appear to provide a marked improvement when describing clinical data.

Rapid measurement of S-100B serum protein levels by Elecsys S100 immunoassay in patients undergoing carotid artery stenting or endarterectomy

OBJECTIVES:: This study was designed to apply the rapid Elecsys(R) S100 immunoassay for real-time measurement of S100 protein serum levels indicating acute brain damage in patients undergoing carotid artery stenting (CAS) or endarterectomy (CEA). DESIGN AND METHODS:: Data of 14 CAS patients were compared to those of 43 CEA and 14 control patients undergoing coronary angiography (CA). S100 serum levels were measured by the full-automatic Elecsys(R) S100 immunoassay and compared to those obtained by the well-established LIA-mat(R) S100 system. RESULTS:: In contrast to CAS and CA patients, median S100 serum levels of CEA patients significantly increased to 0.24 ng/mL before declamping, but subsequently returned to baseline. Three CEA patients with neurological deficits showed sustained elevated S100 levels 6 h after extubation. Absolute S100 values were not significantly different between the two methods. Bland-Altman plot analyses displayed a good agreement, mostly indicating slightly smaller values applying the Elecsys(R) S100 system. CONCLUSIONS:: The Elecsys(R) S100 system appears to be suitable for rapid real-time detection of neurological deficits in patients undergoing CAS and CEA. Persistent elevations of Elecsys(R) S100 levels during CEA were associated with prolonged neurological disorders, whereas transient increases seem to represent impaired blood-brain barrier integrity without neurological deficits.