Nanostructured Pt(NH3)4Cl2/SiO2 for nanomedicine: catalytic degradation of DNA in cancer cells

In vivo suppression of glioblastoma multiforme (GBM) in Wistar rats using silica-shelled biocatalytic Pt(NH3)4Cl2 nanoparticles is reported. These nanoparticles were synthesized by a sol-gel technique and characterized by SEM and HRTEM imaging. We confirmed morphological uniformity (30 nm) and surface acidity of the nanoparticles, respectively, by TEM imaging and FTIR spectral analysis. Interestingly, treatment of Wistar rats intraperitoneally inoculated with C6 cells using the biocatalysts resulted in considerable tumor shrinkage. Efficiency of the biocatalyst to shrink a tumor is superior to that by the commercial cytotoxic agent cisplatin. The tumor suppression property of Pt(NH3)4Cl2 nanoparticles is attributed to catalytic damage of DNA in C6 cells

Genome-wide estimation of gender differences in the gene expression of human livers: Statistical design and analysis

BACKGROUND: Gender differences in gene expression were estimated in liver samples from 9 males and 9 females. The study tested 31,110 genes for a gender difference using a design that adjusted for sources of variation associated with cDNA arrays, normalization, hybridizations and processing conditions. RESULTS: The genes were split into 2,800 that were clearly expressed (expressed genes) and 28,310 that had expression levels in the background range (not expressed genes). The distribution of p-values from the 'not expressed' group was consistent with no gender differences. The distribution of p-values from the 'expressed' group suggested that 8 % of these genes differed by gender, but the estimated fold-changes (expression in males / expression in females) were small. The largest observed fold-change was 1.55. The 95 % confidence bounds on the estimated fold-changes were less than 1.4 fold for 79.3 %, and few (1.1%) exceed 2-fold. CONCLUSION: Observed gender differences in gene expression were small. When selecting genes with gender differences based upon their p-values, false discovery rates exceed 80 % for any set of genes, essentially making it impossible to identify any specific genes with a gender difference

Parametric study of EEG sensitivity to phase noise during face processing

<b>Background: </b> The present paper examines the visual processing speed of complex objects, here faces, by mapping the relationship between object physical properties and single-trial brain responses. Measuring visual processing speed is challenging because uncontrolled physical differences that co-vary with object categories might affect brain measurements, thus biasing our speed estimates. Recently, we demonstrated that early event-related potential (ERP) differences between faces and objects are preserved even when images differ only in phase information, and amplitude spectra are equated across image categories. Here, we use a parametric design to study how early ERP to faces are shaped by phase information. Subjects performed a two-alternative force choice discrimination between two faces (Experiment 1) or textures (two control experiments). All stimuli had the same amplitude spectrum and were presented at 11 phase noise levels, varying from 0% to 100% in 10% increments, using a linear phase interpolation technique. Single-trial ERP data from each subject were analysed using a multiple linear regression model. <b>Results: </b> Our results show that sensitivity to phase noise in faces emerges progressively in a short time window between the P1 and the N170 ERP visual components. The sensitivity to phase noise starts at about 120–130 ms after stimulus onset and continues for another 25–40 ms. This result was robust both within and across subjects. A control experiment using pink noise textures, which had the same second-order statistics as the faces used in Experiment 1, demonstrated that the sensitivity to phase noise observed for faces cannot be explained by the presence of global image structure alone. A second control experiment used wavelet textures that were matched to the face stimuli in terms of second- and higher-order image statistics. Results from this experiment suggest that higher-order statistics of faces are necessary but not sufficient to obtain the sensitivity to phase noise function observed in response to faces. <b>Conclusion: </b> Our results constitute the first quantitative assessment of the time course of phase information processing by the human visual brain. We interpret our results in a framework that focuses on image statistics and single-trial analyses

Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach

Background: In this study, we quantified age-related changes in the time-course of face processing by means of an innovative single-trial ERP approach. Unlike analyses used in previous studies, our approach does not rely on peak measurements and can provide a more sensitive measure of processing delays. Young and old adults (mean ages 22 and 70 years) performed a non-speeded discrimination task between two faces. The phase spectrum of these faces was manipulated parametrically to create pictures that ranged between pure noise (0% phase information) and the undistorted signal (100% phase information), with five intermediate steps. Results: Behavioural 75% correct thresholds were on average lower, and maximum accuracy was higher, in younger than older observers. ERPs from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The earliest age-related ERP differences occurred in the time window of the N170. Older observers had a significantly stronger N170 in response to noise, but this age difference decreased with increasing phase information. Overall, manipulating image phase information had a greater effect on ERPs from younger observers, which was quantified using a hierarchical modelling approach. Importantly, visual activity was modulated by the same stimulus parameters in younger and older subjects. The fit of the model, indexed by R2, was computed at multiple post-stimulus time points. The time-course of the R2 function showed a significantly slower processing in older observers starting around 120 ms after stimulus onset. This age-related delay increased over time to reach a maximum around 190 ms, at which latency younger observers had around 50 ms time lead over older observers. Conclusion: Using a component-free ERP analysis that provides a precise timing of the visual system sensitivity to image structure, the current study demonstrates that older observers accumulate face information more slowly than younger subjects. Additionally, the N170 appears to be less face-sensitive in older observers

Discovery of Rare Variants via Sequencing: Implications for the Design of Complex Trait Association Studies

There is strong evidence that rare variants are involved in complex disease etiology. The first step in implicating rare variants in disease etiology is their identification through sequencing in both randomly ascertained samples (e.g., the 1,000 Genomes Project) and samples ascertained according to disease status. We investigated to what extent rare variants will be observed across the genome and in candidate genes in randomly ascertained samples, the magnitude of variant enrichment in diseased individuals, and biases that can occur due to how variants are discovered. Although sequencing cases can enrich for casual variants, when a gene or genes are not involved in disease etiology, limiting variant discovery to cases can lead to association studies with dramatically inflated false positive rates

Fuzzy min-max neural networks for categorical data: application to missing data imputation

The fuzzy min–max neural network classifier is a supervised learning method. This classifier takes the hybrid neural networks and fuzzy systems approach. All input variables in the network are required to correspond to continuously valued variables, and this can be a significant constraint in many real-world situations where there are not only quantitative but also categorical data. The usual way of dealing with this type of variables is to replace the categorical by numerical values and treat them as if they were continuously valued. But this method, implicitly defines a possibly unsuitable metric for the categories. A number of different procedures have been proposed to tackle the problem. In this article, we present a new method. The procedure extends the fuzzy min–max neural network input to categorical variables by introducing new fuzzy sets, a new operation, and a new architecture. This provides for greater flexibility and wider application. The proposed method is then applied to missing data imputation in voting intention polls. The micro data—the set of the respondents’ individual answers to the questions—of this type of poll are especially suited for evaluating the method since they include a large number of numerical and categorical attributes

Predictors and correlates for weight changes in patients co-treated with olanzapine and weight mitigating agents; a post-hoc analysis

<p>Abstract</p> <p>Background</p> <p>This study focuses on exploring the relationship between changes in appetite or eating behaviors and subsequent weight change for adult patients with schizophrenia or bipolar disorder treated with olanzapine and adjunctive potential weight mitigating pharmacotherapy. The aim is not to compare different weight mitigating agents, but to evaluate patients' characteristics and changes in their eating behaviors during treatment. Identification of patient subgroups with different degrees of susceptibility to the effect of weight mitigating agents during olanzapine treatment may aid clinicians in treatment decisions.</p> <p>Methods</p> <p>Data were obtained from 3 randomized, double-blind, placebo-controlled, 16-week clinical trials. Included were 158 patients with schizophrenia or bipolar disorder and a body mass index (BMI) ≥ 25 kg/m²who had received olanzapine treatment in combination with nizatidine (n = 68), sibutramine (n = 42), or amantadine (n = 48). Individual patients were analyzed for categorical weight loss ≥ 2 kg and weight gain ≥ 1 kg. Variables that were evaluated as potential predictors of weight outcomes included baseline patient characteristics, factors of the Eating Inventory, individual items of the Eating Behavior Assessment, and the Visual Analog Scale.</p> <p>Results</p> <p>Predictors/correlates of weight loss ≥ 2 kg included: high baseline BMI, low baseline interest in food, and a decrease from baseline to endpoint in appetite, hunger, or cravings for carbohydrates. Reduced cognitive restraint, increase in hunger, and increased overeating were associated with a higher probability of weight gain ≥ 1 kg.</p> <p>Conclusion</p> <p>The association between weight gain and lack of cognitive restraint in the presence of increased appetite suggests potential benefit of psychoeducational counseling in conjunction with adjunctive pharmacotherapeutic agents in limiting weight gain during antipsychotic drug therapy.</p> <p>Trial Registration</p> <p>This analysis was not a clinical trial and did not involve any medical intervention.</p

Exceptionally Preserved Jellyfishes from the Middle Cambrian

Cnidarians represent an early diverging animal group and thus insight into their origin and diversification is key to understanding metazoan evolution. Further, cnidarian jellyfish comprise an important component of modern marine planktonic ecosystems. Here we report on exceptionally preserved cnidarian jellyfish fossils from the Middle Cambrian (∼505 million years old) Marjum Formation of Utah. These are the first described Cambrian jellyfish fossils to display exquisite preservation of soft part anatomy including detailed features of structures interpreted as trailing tentacles and subumbrellar and exumbrellar surfaces. If the interpretation of these preserved characters is correct, their presence is diagnostic of modern jellyfish taxa. These new discoveries may provide insight into the scope of cnidarian diversity shortly after the Cambrian radiation, and would reinforce the notion that important taxonomic components of the modern planktonic realm were in place by the Cambrian period

Quantitative High-Resolution Genomic Analysis of Single Cancer Cells

During cancer progression, specific genomic aberrations arise that can determine the scope of the disease and can be used as predictive or prognostic markers. The detection of specific gene amplifications or deletions in single blood-borne or disseminated tumour cells that may give rise to the development of metastases is of great clinical interest but technically challenging. In this study, we present a method for quantitative high-resolution genomic analysis of single cells. Cells were isolated under permanent microscopic control followed by high-fidelity whole genome amplification and subsequent analyses by fine tiling array-CGH and qPCR. The assay was applied to single breast cancer cells to analyze the chromosomal region centred by the therapeutical relevant EGFR gene. This method allows precise quantitative analysis of copy number variations in single cell diagnostics