Homogneous time resolved fluorescence: a new technique for detection and quantifications of infliximab in autoimmune patients

RESUMO:As terapias biológicas revolucionaram o tratamento das doenças autoimunes nos últimos anos. Tipicamente têm como alvos mediadores importantes no mecanismo das doenças. Os antagonistas do fator de necrose tumoral-α (TNF-α) são um grupo de agentes biológicos muito prescrito, pois estão indicados no tratamento de doenças imuno-mediadas comuns, tais como artrite reumatoide, artrite idiopática juvenil, artrite psoriática, espondilite anquilosante, doença de Crohn e colite ulcerosa. Com o uso frequente de inibidores do TNF-α, tem-se tornado evidente que estes agentes têm um potencial imunogénico importante, que pode comprometer o prognóstico a longo prazo dos doentes cronicamente tratados. A produção de anticorpos anti-fármaco parece causar falência terapêutica secundária em muitos doentes. Um dos efeitos dos anticorpos anti-fármaco é o aumento da eliminação do fármaco. A eliminação do fármaco, por sua vez, varia entre indivíduos, refletindo diferentes perfis farmacocinéticos. A determinação dos níveis séricos mínimos do agente anti-TNF-α é assim muito informativa e pode auxiliar nas decisões terapêuticas. Contudo, os testes imunológicos para determinar as concentrações séricas do fármaco não estão facilmente disponíveis na prática clínica. De forma a investigar uma nova técnica potencialmente fidedigna e prática para a deteção e quantificação dos agentes biológicos anti-TNF-α, foi testada a técnica por HTRF (homogeneous time-resolved fluorescence resonance energy transfer) para a determinação de concentrações séricas de infliximab. Apesar de apresentar algumas limitações relacionadas com as condições de leitura da fluorescência, esta técnica provou obter resultados próximos das concentrações obtidas por ELISA (enzyme-linked immunosorbent assay) bridging. Adicionalmente, tem a vantagem de ser de execução muito mais fácil e rápida. Deste modo, a técnica por HTRF poderá ser otimizada e tornar-se uma valiosa ferramenta laboratorial para orientar as decisões terapêuticas em doentes autoimunes com falência da terapêutica anti-TNF-α.--------- ABSTRACT: Biologic therapies revolutionized the treatment of autoimmune diseases in the last years. Typically, they target important disease mediators. Tumor necrosis factor-alpha (TNF-α) antagonists constitute a very prescribed group of biologic agents as they are indicated for the treatment of common immune-mediated diseases, such as rheumatoid arthritis, juvenile idiopathic arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease and ulcerative colitis. With the increasing use of TNF-α inhibitors it has been noticed that they have an important immunogenic potential that can compromise long-term outcomes in chronically treated patients. The production of anti-drug antibodies seems to cause secondary therapeutic failure in many patients. One of the effects of anti-drug antibodies is the enhancement of drug clearance. Drug clearance, in turn, varies among individuals, reflecting different pharmacokinetic profiles. Determination of serum anti-TNF-α drug trough levels is though very informative and could support treatment decisions. However, immunologic assays to determine drug serum concentrations are not readily available in clinical practice. In order to investigate a potentially reliable and practical new technique for detection and quantification of anti-TNF-α biologic agents, homogeneous time-resolved fluorescence resonance energy transfer (HTRF) technique was tested for determination of serum infliximab concentrations. Although presenting some limitations related with fluorescence reading conditions, this technique proved to give results close to the concentrations obtained by the widely used bridging enzyme-linked immunosorbent assay (ELISA). In addition, it has the advantage of being much easier and faster to perform. Thus, HTRF technique can be optimized and become a valuable laboratorial tool to guide treatment decisions in autoimmune patients with anti-TNF-α therapy failure.

Detection and molecular analysis of Toxoplasma gondii and Neospora caninum from dogs with neurological disorders

INTRODUCTION: Toxoplasma gondii and Neospora caninum are related Apicomplexa parasites responsible for systemic diseases in many species of animals, including dogs. METHODS: This study aimed to determine the occurrence of T. gondii and N. caninum infections in 50 dogs with neurological signs that were admitted to the Veterinary Hospital of Universidade Estadual Paulista, City of Botucatu, Brazil. All animals were screened for antibodies using an immunofluorescent antibody test for both parasites. Tissues of positive animals were bioassayed in mice (T. gondii) and gerbils (N. caninum), and DNA was analyzed using the polymerase chain reaction (PCR). Positive samples for T. gondii by PCR were typed using restriction fragment length polymorphism-PCR for 11 markers: SAG1, SAG2 (5′-3′-SAG2 and alt.SAG2), SAG3, Btub, GRA6, L358, c22-8, c29-6, PK1 and Apico, and CS3 marker for virulence analysis. RESULTS: Specific antibodies were detected in 11/50 (22%; 95% confidence interval (CI95%), 12.8-35.3%) animals for T. gondii and 7/50 (14%; CI95%, 7.02-26.3%) for N. caninum. In the bioassay and PCR, 7/11 (63.6%; CI95%, 34.9-84.8%) samples were positive for T. gondii and 3/7 (42.9%; CI95%I, 15.7-75.5%) samples were positive for N. caninum. Three different genotypes were identified, but only 1 was unique. CONCLUSIONS: These data confirm the presence of T. gondii and N. caninum in dogs from Brazil, indicating the importance of this host as a sentinel of T. gondii for human beings, and the genotypic variation of this parasite in Brazil.

Rapid detection and differentiation of mycobacterial species using a multiplex PCR system

Introduction The early diagnosis of mycobacterial infections is a critical step for initiating treatment and curing the patient. Molecular analytical methods have led to considerable improvements in the speed and accuracy of mycobacteria detection. Methods The purpose of this study was to evaluate a multiplex polymerase chain reaction system using mycobacterial strains as an auxiliary tool in the differential diagnosis of tuberculosis and diseases caused by nontuberculous mycobacteria (NTM) Results Forty mycobacterial strains isolated from pulmonary and extrapulmonary origin specimens from 37 patients diagnosed with tuberculosis were processed. Using phenotypic and biochemical characteristics of the 40 mycobacteria isolated in LJ medium, 57.5% (n=23) were characterized as the Mycobacterium tuberculosis complex (MTBC) and 20% (n=8) as nontuberculous mycobacteria (NTM), with 22.5% (n=9) of the results being inconclusive. When the results of the phenotypic and biochemical tests in 30 strains of mycobacteria were compared with the results of the multiplex PCR, there was 100% concordance in the identification of the MTBC and NTM species, respectively. A total of 32.5% (n=13) of the samples in multiplex PCR exhibited a molecular pattern consistent with NTM, thus disagreeing with the final diagnosis from the attending physician. Conclusions Multiplex PCR can be used as a differential method for determining TB infections caused by NTM a valuable tool in reducing the time necessary to make clinical diagnoses and begin treatment. It is also useful for identifying species that were previously not identifiable using conventional biochemical and phenotypic techniques.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Biosynthesis, detection and toxicology of ochratoxins - optimisation of production

Ochratoxin A (OTA) is a very well known mycotoxin found in several food commodities for which maximum limits are being discussed in EC in other to produce appropriate regulations. OTA is one of several ochratoxins produced by Aspergillus and Penicillium species. All the compounds in this group have a molecular structure very similar to OTA and some were already isolated from natural substrates. Several of these compounds such as ochratoxin , methyl and ethyl ester of ochratoxin A, 4-R and S-hydroxyochratoxin A, 10-hydroxyochratoxin A and ochratoxin A open lactone are commercially unavailable. However, they can be easily synthesized through OTA modification. With the main objective of its application on further research works, OTA production, isolation and purification has been optimised from an A. alliaceus strain grown on wheat medium. Synthesis and purification of some OTA derivatives has been achieved and an HPLC method for their detection was optimised. Data about their production by several species of Aspergillus will be presented. The toxicological properties of ochratoxins are still not very clear and a future EC safety limit for OTA will depend on e.g., a better clarification of its carcinogenity. Could OTA derivatives play a role here?

Structure and classification of zoophytes. By James D. Dana.

c. 2

Visual analysis, clustering, and classification of contrast-enhanced tumor perfusion MRI data

Magdeburg, Univ., Fak. für Inf., Diss., 2014

Evolution and classification of the Osteostraci. The exoskeleton of early Osteostraci. / Robert H. Denison --

v.11:no.3-4(1951)

Phylogenetic relationships and classification of the major lineages of Apoidea (Hymenoptera) : with emphasis on the crabronid wasps / by Gabriel A. R. Melo.

no.14

The Discrimination of Colored Acrylic, Cotton, and Wool Textile Fibers Using Micro-Raman Spectroscopy. Part 1 : In Situ Detection and Characterization of Dyes

Raman spectroscopy has been applied to characterize fiber dyes and determine the discriminating ability of the method. Black, blue, and red acrylic, cotton, and wool samples were analyzed. Four excitation sources were used to obtain complementary responses in the case of fluorescent samples. Fibers that did not provide informative spectra using a given laser were usually detected using another wavelength. For any colored acrylic, the 633-nm laser did not provide Raman information. The 514-nm laser provided the highest discrimination for blue and black cotton, but half of the blue cottons produced noninformative spectra. The 830-nm laser exhibited the highest discrimination for red cotton. Both visible lasers provided the highest discrimination for black and blue wool, and NIR lasers produced remarkable separation for red and black wool. This study shows that the discriminating ability of Raman spectroscopy depends on the fiber type, color, and the laser wavelength.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.