Molecular detection and identification of zygomycetes species from paraffin-embedded tissues in a murine model of disseminated zygomycosis: a collaborative European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Fungal Infection Study Group (EFISG) evaluation.

The present study was performed to assess the interlaboratory reproducibility of the molecular detection and identification of species of Zygomycetes from formalin-fixed paraffin-embedded kidney and brain tissues obtained from experimentally infected mice. Animals were infected with one of five species (Rhizopus oryzae, Rhizopus microsporus, Lichtheimia corymbifera, Rhizomucor pusillus, and Mucor circinelloides). Samples with 1, 10, or 30 slide cuts of the tissues were prepared from each paraffin block, the sample identities were blinded for analysis, and the samples were mailed to each of seven laboratories for the assessment of sensitivity. A protocol describing the extraction method and the PCR amplification procedure was provided. The internal transcribed spacer 1 (ITS1) region was amplified by PCR with the fungal universal primers ITS1 and ITS2 and sequenced. As negative results were obtained for 93% of the tissue specimens infected by M. circinelloides, the data for this species were excluded from the analysis. Positive PCR results were obtained for 93% (52/56), 89% (50/56), and 27% (15/56) of the samples with 30, 10, and 1 slide cuts, respectively. There were minor differences, depending on the organ tissue, fungal species, and laboratory. Correct species identification was possible for 100% (30 cuts), 98% (10 cuts), and 93% (1 cut) of the cases. With the protocol used in the present study, the interlaboratory reproducibility of ITS sequencing for the identification of major Zygomycetes species from formalin-fixed paraffin-embedded tissues can reach 100%, when enough material is available.

Diagnostic value of vestibulo-ocular reflex parameters in the detection and characterization of labyrinthine lesions.

OBJECTIVE: To evaluate the power of various parameters of the vestibulo-ocular reflex (VOR) in detecting unilateral peripheral vestibular dysfunction and in characterizing certain inner ear pathologies. STUDY DESIGN: Prospective study of consecutive ambulatory patients presenting with acute onset of peripheral vertigo and spontaneous nystagmus. SETTING: Tertiary referral center. PATIENTS: Seventy-four patients (40 females, 34 males) and 22 normal subjects (11 females, 11 males) were included in the study. Patients were classified in three main diagnoses: vestibular neuritis: 40; viral labyrinthitis: 22; Meniere's disease: 12. METHODS: The VOR function was evaluated by standard caloric and impulse rotary tests (velocity step). A mathematical model of vestibular function was used to characterize the VOR response to rotational stimulation. The diagnostic value of the different VOR parameters was assessed by uni- and multivariable logistic regression. RESULTS: In univariable analysis, caloric asymmetry emerged as the most powerful VOR parameter in identifying unilateral vestibular deficit, with a boundary limit set at 20%. In multivariable analysis, the combination of caloric asymmetry and rotational time constant asymmetry significantly improved the discriminatory power over caloric alone (p<0.0001) and produced a detection score with a correct classification of 92.4%. In discriminating labyrinthine diseases, different combinations of the VOR parameters were obtained for each diagnosis (p<0.003) supporting that the VOR characteristics differ between the three inner ear disorders. However, the clinical usefulness of these characteristics in separating the pathologies was limited. CONCLUSION: We propose a powerful logistic model combining the indices of caloric and time constant asymmetries to detect a peripheral vestibular loss, with an accuracy of 92.4%. Based on vestibular data only, the discrimination between the different inner ear diseases is statistically possible, which supports different pathophysiologic changes in labyrinthine pathologies.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Detection and chemical profiling of medicine counterfeits by Raman spectroscopy and chemometrics

Raman spectroscopy combined with chemometrics has recently become a widespread technique for the analysis of pharmaceutical solid forms. The application presented in this paper is the investigation of counterfeit medicines. This increasingly serious issue involves networks that are an integral part of industrialized organized crime. Efficient analytical tools are consequently required to fight against it. Quick and reliable authentication means are needed to allow the deployment of measures from the company and the authorities. For this purpose a method in two steps has been implemented here. The first step enables the identification of pharmaceutical tablets and capsules and the detection of their counterfeits. A nonlinear classification method, the Support Vector Machines (SVM), is computed together with a correlation with the database and the detection of Active Pharmaceutical Ingredient (API) peaks in the suspect product. If a counterfeit is detected, the second step allows its chemical profiling among former counterfeits in a forensic intelligence perspective. For this second step a classification based on Principal Component Analysis (PCA) and correlation distance measurements is applied to the Raman spectra of the counterfeits.

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.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Quantitative ultrasound for the detection and management of osteoporosis.

Quantitative ultrasound (QUS) appears to be developing into an acceptable, low-cost and readily-accessible alternative to dual X-ray absorptiometry (DXA) measurements of bone mineral density (BMD) in the detection and management of osteoporosis. Perhaps the major difficulty with their widespread use is that many different QUS devices exist that differ substantially from each other, in terms of the parameters they measure and the strength of empirical evidence supporting their use. But another problem is that virtually no data exist outside of Caucasian or Asian populations. In general, heel QUS appears to be most tested and most effective. Some, but not all heel QUS devices are effective assessing fracture risk in some, but not all populations, the evidence being strongest for Caucasian females > 55 years old, though some evidence exists for Asian females > 55 and for Caucasian and Asian males > 70. Certain devices may allow to estimate the likelihood of osteoporosis, but very limited evidence exists supporting QUS use during the initiation or monitoring of osteoporosis treatment. Likely, QUS is most effective when combined with an assessment of clinical risk factors (CRF); with DXA reserved for individuals who are not identified as either high or low risk using QUS and CRF. However, monitoring and maintenance of test and instrument accuracy, precision and reproducibility are essential if QUS devices are to be used in clinical practice; and further scientific research in non-Caucasian, non-Asian populations clearly is compulsory to validate this tool for more widespread use.

Methods for detection and confirmation of Hematide?/peginesatide in anti-doping samples.

Since the 1990's, cheating athletes have abused substances to increase their oxygen transport capabilities; among these substances, recombinant EPO is the most well known. Currently, other investigational pharmaceutical products are able to produce an effect similar to EPO but without having chemical structures related to EPO; these are the synthetic erythropoiesis stimulating agents (ESAs). Peginesatide (also known as Hematide?) is being developed by Affymax and Takeda and, if approved by regulatory authorities, could soon be released on the international market. To detect potential athletic abuse of this product and deter athletes who consider cheating, we initiated a collaboration to implement a detection test for anti-doping purposes. Peginesatide is a synthetic, PEGylated, investigational, peptide-based erythropoiesis-stimulating agent that is designed and engineered to stimulate specifically the erythropoietin receptor dimer that governs erythropoiesis. It is undetectable using current anti-doping tests due to its lack of sequence homology to EPO. To detect and deter potential abuse of peginesatide, we initiated an industry/antidoping laboratory collaboration to develop and validate screening and confirmation assays so that they would be available before peginesatide reaches the market. We describe a screening ELISA and a confirmation assay consisting of immune-purification followed by separation with SDS-PAGE and revelation with Western double blotting. Both assays can detect 0.5 ng/mL concentrations of peginesatide in blood samples, enabling detection for several days after administration of a physiologically relevant dose. This initial report describes experimental characterization of these assays, including testing with a blinded set of samples from a clinical study conducted in healthy volunteers.

Nosology and classification of genetic skeletal disorders: 2010 revision.

Genetic disorders involving the skeletal system arise through disturbances in the complex processes of skeletal development, growth and homeostasis and remain a diagnostic challenge because of their variety. The Nosology and Classification of Genetic Skeletal Disorders provides an overview of recognized diagnostic entities and groups them by clinical and radiographic features and molecular pathogenesis. The aim is to provide the Genetics, Pediatrics and Radiology community with a list of recognized genetic skeletal disorders that can be of help in the diagnosis of individual cases, in the delineation of novel disorders, and in building bridges between clinicians and scientists interested in skeletal biology. In the 2010 revision, 456 conditions were included and placed in 40 groups defined by molecular, biochemical, and/or radiographic criteria. Of these conditions, 316 were associated with mutations in one or more of 226 different genes, ranging from common, recurrent mutations to "private" found in single families or individuals. Thus, the Nosology is a hybrid between a list of clinically defined disorders, waiting for molecular clarification, and an annotated database documenting the phenotypic spectrum produced by mutations in a given gene. The Nosology should be useful for the diagnosis of patients with genetic skeletal diseases, particularly in view of the information flood expected with the novel sequencing technologies; in the delineation of clinical entities and novel disorders, by providing an overview of established nosologic entities; and for scientists looking for the clinical correlates of genes, proteins and pathways involved in skeletal biology. © 2011 Wiley-Liss, Inc.

The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation.

Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.

Pathophysiology and classification of the vibration white finger.

Skin, arteries and nerves of the upper extremities can be affected by vibration exposure. Recent advances in skin and vascular biology as well as new investigative methods, have shown that neurovascular symptoms may be due to different vascular and neurological disorders which should be differentiated if proper management is to be evaluated. Three types of vascular disorder can be observed in the vibration white finger: digital organic microangiopathy, a digital vasospastic phenomenon and arterial thrombosis in the upper extremities. An imbalance between endothelin-1 and calcitonin-gene-related peptide is probably responsible for the vasospastic phenomenon. Moreover, paresthesiae can be due to either a diffuse vibration neuropathy or a carpal tunnel syndrome. A precise diagnosis is then necessary to adapt the treatment to individual cases. A classification describing the type and severity of the vascular lesions is presented. Asymptomatic lesions are included for adequate epidemiological studies and risk assessment of vibrating tools. Monitoring of vibration exposed workers should include not only a questionnaire about symptoms, but also a clinical evaluation including diagnostic tests for the screening of early asymptomatic neurovascular injuries.

Sex chromosome trisomies in Europe: prevalence, prenatal detection and outcome of pregnancy.

This study aims to assess prevalence and pregnancy outcome for sex chromosome trisomies (SCTs) diagnosed prenatally or in the first year of life. Data held by the European Surveillance of Congenital Anomalies (EUROCAT) database on SCT cases delivered 2000-2005 from 19 population-based registries in 11 European countries covering 2.5 million births were analysed. Cases included were livebirths diagnosed to 1 year of age, fetal deaths from 20 weeks gestation and terminations of pregnancy for fetal anomaly (TOPFA). In all, 465 cases of SCT were diagnosed between 2000 and 2005, a prevalence of 1.88 per 10,000 births (95% CI 1.71-2.06). Prevalence of XXX, XXY and XYY were 0.54 (95% CI 0.46-0.64), 1.04 (95% CI 0.92-1.17) and 0.30 (95% CI 0.24-0.38), respectively. In all, 415 (89%) were prenatally diagnosed and 151 (36%) of these resulted in TOPFA. There was wide country variation in prevalence (0.19-5.36 per 1000), proportion prenatally diagnosed (50-100%) and proportion of prenatally diagnosed resulting in TOPFA (13-67%). Prevalence of prenatally diagnosed cases was higher in countries with high prenatal detection rates of Down syndrome. The EUROCAT prevalence rate for SCTs diagnosed prenatally or up to 1 year of age represents 12% of the prevalence expected from cytogenetic studies of newborn babies, as the majority of cases are never diagnosed or are diagnosed later in life. There is a wide variation between European countries in prevalence, prenatal detection and TOPFA proportions, related to differences in screening policies as well as organizational and cultural factors.

Coulier, Paul-Jean (1824-1890) : a precursor in the history of fingermark detection and their potential use for identifying their source (1863)

An online copy of a 1863 French book, The Scientific and Industrial Year (English translation of the title), that predates other historically significant writings about fingerprints suggests the use of iodine stains to reproduce papillary lines of the skin and suggests the feasibility of identifying suspects by touch. It also suggests the use of a magnifying glass for comparing those impressions whose origins need to be determined.