Evaluation of method performance for oxidative stress biomarkers in urine and biological variations in urine of patients with type 2 diabetes mellitus and diabetic nephropathy

Background

Oxidative stress biomarkers such as superoxide dismutase (CuZnSOD), catalase (CAT) and malondialdehyde (MDA) play an important role in the pathogenesis or progression of numerous diseases. Data regarding the biological variation and analytical quality specifications (imprecision, bias and total error) for judging the acceptability of method performance for oxidative stress biomarkers in urine are conspicuously lacking in the literature. Such data are important in setting analytical quality specifications, assessing the utility of population reference intervals (index of individuality) and assessing the significance of changes in serial results from an individual (reference change value; RCV).

Materials and methods

20 patients with type 2 diabetes mellitus (T2DM), 20 patients with diabetic nephropathy (DN) and 14 healthy individuals as control were involved in this study. Timed first morning urine samples were taken from patients and healthy groups on the zero, 1st, 3rd, 5th, 7th, 15th and 30th days. Index of individuality and reference change value were calculated from within-subject and between-subject variations. Methods of oxidative stress biomarkers in human blood were adopted in human urine and markers were measured as spectrophotometrically. Also, analytical quality specifications for evaluation of the method performance were established for oxidative stress biomarkers in urine.

Results

Within-subject variations of oxidative stress biomarkers were significantly higher in patients with DN and T2DM compared to healthy subjects. MDA showed low individuality, and within-subject variances of MDA were larger than between-subject variances in all groups. However, CAT and CuZnSOD showed strong individuality, but within-subject variances of them were smaller than between-subject variances in all groups. RCVs of all analytes in diabetic patients were relatively higher, because of high within-subject variation, resulting in a higher RCV. Also, the described methodology achieves these goals, with analytical CVs of < 3.5% for all analytes. Goals for bias and total error were 6.0-7.9% and 12.5-23.3%, respectively.

Conclusions

RCVs concept for predicting the clinical status in diabetic patients represents an optimization of laboratory reporting and could be a valuable tool for clinical decision. Furthermore, for oxidative stress biomarkers’ measurements in urine, the desirable imprecision goals based on biological variation are obtainable by current methodologies.     

Identifying kinetic gating mechanisms for ion channels by using two-dimensional distributions of simulated dwell times.

Ion channels are integral membrane proteins that regulate ionic flux through cell membranes by opening and closing (or gating) their pores. The gating can be monitored by observing step changes in the current flowing through single channels. Analysis of the durations of the open and closed intervals and of the correlations among the interval durations can give insight into the gating mechanism. Although it is well known that the correlation information can be essential to distinguish among possible gating mechanisms, it has been difficult to use this information because it has not been possible to correct the predicted correlations for the distortion of the single-channel data because of filtering and noise. To overcome this limitation we present a method based on a comparison of simulated and experimental two-dimensional dwell-time distributions constructed by analysing simulated and experimental single-channel currents in an identical manner. The simulated currents incorporate the true effects of filtering and noise, the two-dimensional distributions retain the correlation information, and the identical analysis allows direct maximum-likelihood comparison of the simulated and experimental two-dimensional distributions. We show that the two-dimensional simulation method has a greatly increased ability to distinguish among models, compared with methods that use one-dimensional distributions.     

Infection of SDAV-immune rats with SDAV and rat coronavirus

Infection of rats with sialodacryoadenitis virus (SDAV) or rat coronavirus (RCV) is acute and self-limiting, and elimination and control of either virus is based on the assumption that recovered rats are immune to reinfection. To test this hypothesis, we examined whether SDAV-immune rats could be infected with RCV or reinfected with SDAV. Sprague Dawley (SD) rats were inoculated intranasally with SDAV or with culture medium alone and serial SDAV antibody titers were obtained. Eleven months after inoculation, when antibody titers had stabilized, SDAV-immune and nonimmune rats were challenged with SDAV or RCV, and euthanatized 3 or 6 days later. SDAV-immune rats challenged with SDAV or RCV manifested acute rhinitis associated with virus antigen by 3 days after inoculation, but no lesions or antigen were subsequently found in the lower respiratory tract, salivary glands or lacrimal glands. There was also a marked anamnestic increase in antibody titer by 6 days after challenge. SDAV-immune rats challenged with SDAV or RCV also transmitted infection to nonimmune cage mates. This study indicates that 11 months after primary infection with SDAV, rats can be infected with SDAV or RCV, but that the severity of disease is significantly reduced.     

Physiology and its Importance for Reference Intervals

Reference intervals are ideally defined on apparently healthy individuals and should be distinguished from clinical decision limits that are derived from known diseased patients. Knowledge of physiological changes is a prerequisite for understanding and developing reference intervals. Reference intervals may differ for various subpopulations because of differences in their physiology, most obviously between men and women, but also in childhood, pregnancy and the elderly. Changes in laboratory measurements may be due to various physiological factors starting at birth including weaning, the active toddler, immunological learning, puberty, pregnancy, menopause and ageing. The need to partition reference intervals is required when there are significant physiological changes that need to be recognised. It is important that laboratorians are aware of these changes otherwise reference intervals that attempt to cover a widened inter-individual variability may lose their usefulness. It is virtually impossible for any laboratory to directly develop reference intervals for each of the physiological changes that are currently known, however indirect techniques can be used to develop or validate reference intervals in some difficult situations such as those for children. Physiology describes our life’s journey, and it is only when we are familiar with that journey that we can appreciate a pathological departure.     

Noise in attractor networks in the brain produced by graded firing rate representations

Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry.     

Conductance versus current noise in a neuronal model for noisy subthreshold oscillations and related spike generation

Biological systems are notoriously noisy. Noise, therefore, also plays an important role in many models of neural impulse generation. Noise is not only introduced for more realistic simulations but also to account for cooperative effects between noisy and nonlinear dynamics. Often, this is achieved by a simple noise term in the membrane equation (current noise). However, there are ongoing discussions whether such current noise is justified or whether rather conductance noise should be introduced because it is closer to the natural origin of noise. Therefore, we have compared the effects of current and conductance noise in a neuronal model for subthreshold oscillations and action potential generation. We did not see any significant differences in the model behavior with respect to voltage traces, tuning curves of interspike intervals, interval distributions or frequency responses when the noise strength is adjusted. These findings indicate that simple current noise can give reasonable results in neuronal simulations with regard to physiological relevant noise effects.     

Predictive vs. Empiric Assessment of Schistosomiasis: Implications for Treatment Projections in Ghana

BackgroundMapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies.Conclusions/SignificanceUsing the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible; modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers.     

Application of the Stockholm Hierarchy to Defining the Quality of Reference Intervals and Clinical Decision Limits

The Stockholm Hierarchy is a professional consensus created to define the preferred approaches to defining analytical quality. The quality of a laboratory measurement can also be classified by the quality of the limits that the value is compared with, namely reference interval limits and clinical decision limits. At the highest level in the hierarchy would be placed clinical decision limits based on clinical outcome studies. The second level would include both formal reference interval studies (studies of intra and inter-individual variations) and clinical decision limits based on clinician survey. While these approaches are commonly used, they require a lot of resources to define accurately. Placing laboratory experts on the third level would suggest that although they can also define reference intervals by consensus, theirs aren’t as well regarded as clinician defined limits which drive clinical behaviour. Ideally both analytical and clinical considerations should be made, with clinicians and laboratorians both having important information to consider. The fourth level of reference intervals would be for those defined by survey or by regulatory authorities because of the focus on what is commonly achieved rather than what is necessarily correct. Finally, laboratorians know that adopting reference limits from kit inserts or textbook publications is problematic because both methodological issues and reference populations are often not the same as their own. This approach would rank fifth and last. When considering which so called ‘common’ or ‘harmonised reference intervals’ to adopt, both these characteristics and the quality of individual studies need to be assessed. Finally, we should also be aware that reference intervals describe health and physiology while clinical decision limits focus on disease and pathology, and unless we understand and consider the two corresponding issues of test specificity and test sensitivity, we cannot assure the quality of the limits that we report.     

The impact of stochasticity on the behaviour of nonlinear population models: synchrony and the Moran effect

Environmental variation is ubiquitous, but its effects on nonlinear population dynamics are poorly understood. Using simple (unstructured) nonlinear models we investigate the effects of correlated noise on the dynamics of two otherwise independent populations (the Moran effect), i.e. we focus on noise rather than dispersion or trophic interaction as the cause of population synchrony. We find that below the bifurcation threshold for periodic behaviour (1) synchrony between populations is strongly dependent on the shape of the noise distribution but largely insensitive to which model is studied, (2) there is, in general, a loss of synchrony as the noise is filtered by the model, (3) for specially structured noise distributions this loss can be effectively eliminated over a restricted range of distribution parameter values even though the model might be nonlinear, (4) for unstructured models there is no evidence of correlation enhancement, a mechanism suggested by Moran, but above the bifurcation threshold enhancement is possible for weak noise through phase-locking, (5) rapid desynchronisation occurs as the chaotic regime is approached. To carry out the investigation the stochastic models are (a) reformulated in terms of their joint asymptotic probability distributions and (b) simulated to analyse temporal patterns.     

Influence of viral infections on body weight, survival, and tumor prevalence in Fischer 344/NCr rats on two-year studies

Sendai virus (SV), pneumonia virus of mice (PVM), and rat coronavirus/sialodacryoadenitis virus (RCV/SDAV) were common viral infections of rats in the National Cancer Institute-National Toxicology Program (NCI-NTP) studies from 1977 to 1983. Influence of these viral infections on body weight, survival, and prevalences of spontaneous tumors in the F344/NCr rats of 28 diet control groups at five different laboratories were evaluated. Tumor prevalences evaluated in this investigation included the following: leukemia and tumors of the anterior pituitary, lungs, salivary glands and Harderian glands in both sexes; adrenal pheochromocytomas in male rats; and mammary tumors in female rats. SV and PVM but not RCV/SDAV infections were associated with significant (P less than 0.05) decreases in body weights of male and female rats. Male rat groups with PVM infection had a lower prevalence of leukemia and male rat groups with RCV/SDAV infection had a higher prevalence of anterior pituitary tumors than the corresponding uninfected groups. Female rat groups with SV infection had greater survival and a higher prevalence of lung tumors than groups without SV infection. However, none of the tumor prevalence and survival differences were statistically significant when interlaboratory variability and time-related effects were taken into account.     

Neural decision boundaries for maximal information transmission

We consider here how to separate multidimensional signals into two categories, such that the binary decision transmits the maximum possible information about those signals. Our motivation comes from the nervous system, where neurons process multidimensional signals into a binary sequence of responses (spikes). In a small noise limit, we derive a general equation for the decision boundary that locally relates its curvature to the probability distribution of inputs. We show that for Gaussian inputs the optimal boundaries are planar, but for non-Gaussian inputs the curvature is nonzero. As an example, we consider exponentially distributed inputs, which are known to approximate a variety of signals from natural environment.     

Application of template matching technique to particle detection in electron micrographs

Template matching together with the comprehensive theory of image formation in electron microscope provides an optimal (in Bayesian sense) tool for solving one of the outstanding problems in single particle analysis, i.e., automatic selection of particle views from noisy micrograph fields. The method is based on the assumption that the reference three-dimensional structure is known and that the relevant parameters of the model of the image formation process can be estimated. In the first stage of the procedure, a set of possible particle views is generated using the available reference structure. The template images are constructed as linear combinations of available particle views using a clustering technique. Next, the micrograph noise characteristic is established using an automated contrast transfer function (CTF) estimation procedure. Finally, the CTF parameters calculated are used to construct a matched filter and correlation functions corresponding to the available template images are calculated. In order to alleviate the problem of the biased caused by varying image formation conditions, a decision making strategy based on the predicted distribution of correlation coefficients is proposed. It is demonstrated that due to the inclusion of CTF considerations, the template matching method performed very well in a broad range of microscopy conditions.     

Confidence regions for treatment effects in subgroups in biomarker stratified designs

Subgroup analysis has important applications in the analysis of controlled clinical trials. Sometimes the result of the overall group fails to demonstrate that the new treatment is better than the control therapy, but for a subgroup of patients, the treatment benefit may exist; or sometimes, the new treatment is better for the overall group but not for a subgroup. Hence we are interested in constructing a simultaneous confidence interval for the difference of the treatment effects in a subgroup and the overall group. Subgroups are usually formed on the basis of a predictive biomarker such as age, sex, or some genetic marker. While, for example, age can be detected precisely, it is often only possible to detect the biomarker status with a certain probability. Because patients detected with a positive or negative biomarker may not be truly biomarker positive or negative, responses in the subgroups depend on the treatment therapy as well as on the sensitivity and specificity of the assay used in detecting the biomarkers. In this work, we show how (approximate) simultaneous confidence intervals and confidence ellipsoid for the treatment effects in subgroups can be found for biomarker stratified clinical trials using a normal framework with normally distributed or binary data. We show that these intervals maintain the nominal confidence level via simulations.     

Mass transfer limit of fluorescent dyes during multicolor staining of aerobic granules

Multicolor fluorescence experiments are conducted to investigate the distributions of extracellular polymeric substances and/or cells in the bioaggregates. Successful staining requires that the dyes could fully stain the targeted substances of bioaggregates in a finite time. The mass transfer limit for one of the four fluorescent dyes, calcofluor white, concanavalin A conjugated with tetramethylrhodamine, Nile red, and SYTO 63, penetrating entire phenol-fed granules or those sectioned at 50 μm thick, was quantitatively determined. The former three dyes sufficiently stained the entire granule within prescribed time intervals. However, the SYTO 63 could not penetrate the 600-μm granule in a finite time. Simplified one-dimensional diffusional model estimated the apparent diffusivity of SYTO 63 in the aerobic granule matrix. This work revealed that each staining scheme should be examined for the possible mass transfer limit of dyes during staining.     

An adaptive trial design to optimize dose-schedule regimes with delayed outcomes

This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision-making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design's performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.     

The distribution of the intervals between neural impulses in the maintained discharges of retinal ganglion cells

Simulated neural impulse trains were generated by a digital realization of the integrate-and-fire model. The variability in these impulse trains had as its origin a random noise of specified distribution. Three different distributions were used: the normal (Gaussian) distribution (no skew, normokurtic), a first-order gamma distribution (positive skew, leptokurtic), and a uniform distribution (no skew, platykurtic). Despite these differences in the distribution of the variability, the distributions of the intervals between impulses were nearly indistinguishable. These inter-impulse distributions were better fit with a hyperbolic gamma distribution than a hyperbolic normal distribution, although one might expect a better approximation for normally distributed inverse intervals. Consideration of why the inter-impulse distribution is independent of the distribution of the causative noise suggests two putative interval distributions that do not depend on the assumed noise distribution: the log normal distribution, which is predicated on the assumption that long intervals occur with the joint probability of small input values, and the random walk equation, which is the diffusion equation applied to a random walk model of the impulse generating process. Either of these equations provides a more satisfactory fit to the simulated impulse trains than the hyperbolic normal or hyperbolic gamma distributions. These equations also provide better fits to impulse trains derived from the maintained discharges of ganglion cells in the retinae of cats or goldfish. It is noted that both equations are free from the constraint that the coefficient of variation (CV) have a maximum of unity. The concluding discussion argues against the random walk equation because it embodies a constraint that is not valid, and because it implies specific parameters that may be spurious.     

When should one subtract background fluorescence in 2-color microarrays?

Two-color microarrays are a powerful tool for genomic analysis, but have noise components that make inferences regarding gene expression inefficient and potentially misleading. Background fluorescence, whether attributable to nonspecific binding or other sources, is an important component of noise. The decision to subtract fluorescence surrounding spots of hybridization from spot fluorescence has been controversial, with no clear criteria for determining circumstances that may favor, or disfavor, background subtraction. While it is generally accepted that subtracting background reduces bias but increases variance in the estimates of the ratios of interest, no formal analysis of the bias-variance trade off of background subtraction has been undertaken. In this paper, we use simulation to systematically examine the bias-variance trade off under a variety of possible experimental conditions. Our simulation is based on data obtained from 2 self versus self microarray experiments and is free of distributional assumptions. Our results identify factors that are important for determining whether to background subtract, including the correlation of foreground to background intensity ratios. Using these results, we develop recommendations for diagnostic visualizations that can help decisions about background subtraction.     

Noise underlies switching behavior of the bacterial flagellum

We report the switching behavior of the full bacterial flagellum system that includes the filament and the motor in wild-type Escherichia coli cells. In sorting the motor behavior by the clockwise bias, we find that the distributions of the clockwise (CW) and counterclockwise (CCW) intervals are either exponential or nonexponential with long tails. At low bias, CW intervals are exponentially distributed and CCW intervals exhibit long tails. At intermediate CW bias (0.5) both CW and CCW intervals are mainly exponentially distributed. A simple model suggests that these two distinct switching behaviors are governed by the presence of signaling noise within the chemotaxis network. Low noise yields exponentially distributed intervals, whereas large noise yields nonexponential behavior with long tails. These drastically different motor statistics may play a role in optimizing bacterial behavior for a wide range of environmental conditions.     

Estimating kinetic parameters for single channels with simulation. A general method that resolves the missed event problem and accounts for noise.

Analysis of currents recorded from single channels is complicated by the limited time resolution (filtering) of the data which can prevent the detection of brief intervals. Although a number of approaches have been used to correct for the undetected intervals (missed events) when identifying kinetic models and estimating parameters, none of them provide a general method which takes into account the true effects of noise and limited time resolution. This paper presents such a method. The approach is to use simulated single-channel currents to incorporate the true effects of filtering and noise on missed events and interval durations. The simulated currents are then analyzed in a manner identical to that used to analyze the experimental currents. An iterative search process using likelihood comparison of two-dimensional dwell-time distributions obtained from the simulated and experimental single-channel currents then allows the most likely rate constants to be determined. The large errors and false solutions that can result from the more typically applied assumptions of no noise and an absolute dead time (idealized filtering) are excluded by the iterative simulation method, and the correlation information contained in the two-dimensional distributions should increase the ability to distinguish among different gating mechanisms. The iterative simulation method is generally applicable to channels which typically open to a single conductance level. For these channels the method places no restrictions on the proposed gating mechanism or the form of the predicted dwell-time distributions.     

Reference Tree and Environmental Sequence Diversity of Labyrinthulomycetes

Labyrinthulomycetes are heterotrophic stramenopiles that are ubiquitous in a wide range of both marine and freshwater habitats and play important roles in decomposition of organic matter. The diversity and taxonomy of Labyrinthulomycetes has been studied for many years, but we nevertheless lack both a comprehensive reference database and up‐to‐date phylogeny including all known diversity, which hinders many global insights into their ecological distribution and the relative importance of various subgroups in different environments. Here, we present a curated reference database and a phylogenetic tree of Labyrinthulomycetes small subunit ribosomal RNA (SSU or 18S rRNA) data. Based on this created reference database, we analyzed high‐throughput environmental sequencing data, revealing many previously unknown environmental clades and exploring the ecological distribution of various subgroups. Particularly, a number of newly identified environmental clades that are widespread in the open ocean. Comparing the manually curated reference database to existing tools for identification of environmental sequences (e.g. PR2 or SILVA databases) suggests that the curated database provides a higher degree of specificity and a lower frequency of misidentification. The phylogenetic framework and database will be a useful tool for future ecological and evolutionary studies.