Comparable Vδ2 Cell Functional Characteristics in Virally Suppressed People Living with HIV and Uninfected Individuals.

Crosstalk between innate and adaptive pathways is a critical component to developing an effective, lasting immune response. Among natural effector cells, innate-like γδ T cells promote immunity by facilitating communication between the two compartments and exerting cytotoxic effector functions. Dysregulation of γδ T cell populations is a byproduct of primary Humanimmunodeficiency virus (HIV) infection. This is most pronounced in the depletion and loss of function within cells expressing a Vγ9Vδ2 TCR (Vδ2 cells). Whether or not prolonged viral suppression mediated by antiretroviral therapy (ART) can reverse these effects has yet to be determined. In this study, we present evidence of similar Vδ2 cell functional responses within a cohort of people living with HIV (PLWH) that has been stably suppressed for >1 year and uninfected donors. Through the use of aminobisphosphonate drugs, we were able to generate a comprehensive comparison between ex vivo and expanded Vδ2 cells within each group. Both groups had largely similar compositions of memory and effector phenotypes, post-expansion TCR repertoire diversity, and cytotoxic capabilities. Our findings support the notion that ART promotes the recovery of Vδ2 polyfunctionality and provides insight for strategies aiming to reconstitute the full immune response after infection with HIV.

Phosphodiesterase sequence variants may predispose to prostate cancer.

We hypothesized that mutations that inactivate phosphodiesterase (PDE) activity and lead to increased cAMP and cyclic guanosine monophosphate levels may be associated with prostate cancer (PCa). We sequenced the entire PDE coding sequences in the DNA of 16 biopsy samples from PCa patients. Novel mutations were confirmed in the somatic or germline state by Sanger sequencing. Data were then compared to the 1000 Genome Project. PDE, CREB and pCREB protein expression was also studied in all samples, in both normal and abnormal tissue, by immunofluorescence. We identified three previously described PDE sequence variants that were significantly more frequent in PCa. Four novel sequence variations, one each in the PDE4B,PDE6C, PDE7B and PDE10A genes, respectively, were also found in the PCa samples. Interestingly, PDE10A and PDE4B novel variants that were present in 19 and 6% of the patients were found in the tumor tissue only. In patients carrying PDE defects, there was pCREB accumulation (P<0.001), and an increase of the pCREB:CREB ratio (patients 0.97±0.03; controls 0.52±0.03; P-value <0.001) by immunohistochemical analysis. We conclude that PDE sequence variants may play a role in the predisposition and/or progression to PCa at the germline and/or somatic state respectively.

Fluorescence cumulants analysis with non-ideal observation profiles.

One of the challenges of fluorescence fluctuation fpectroscopy (FFS) is an adequate approximation of a brightness profile. The key feature of fluorescence intensity distribution analysis (FIDA) is a polynomial approximation of a brightness profile. A broad range of brightness profile shapes can be well described by this approximation. A different approach consisting of the introduction of additional fitting parameters, defined as a relative difference between integrals of the actual brightness profile and its Gaussian approximation, is used in photon counting histogram (PCH) analysis. It is sufficient to introduce only one additional fitting parameter (first-order correction) to get an adequate fit to the experimental data in many practical applications. In the current study, we apply these approaches to the theory of time integrated fluorescence cumulants analysis. We demonstrate that developed corrections improve results of FFS analysis applied to simulated and experimental data. The use of different brightness profile approximations and normalizations in PCH and FIDA leads to different estimates of brightness and the number of molecules, even though they represent the same physical quantities. Based on the developed theory, we derive equations that relate brightness and the number of molecules in PCH and FIDA.

Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons.

Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research.

Estimation of nonlinear differential equation model for glucose-insulin dynamics in type I diabetic patients using generalized smoothing.

In this work, we develop an ordinary differential equations (ODE) model of physiological regulation of glycemia in type 1 diabetes mellitus (T1DM) patients in response to meals and intravenous insulin infusion. Unlike for majority of existing mathematical models of glucose-insulin dynamics, parameters in our model are estimable from a relatively small number of noisy observations of plasma glucose and insulin concentrations. For estimation, we adopt the generalized smoothing estimation of nonlinear dynamic systems of Ramsay et al. (2007). In this framework, the ODE solution is approximated with a penalized spline, where the ODE model is incorporated in the penalty. We propose to optimize the generalized smoothing by using penalty weights that minimize the covariance penalties criterion (Efron, 2004). The covariance penalties criterion provides an estimate of the prediction error for nonlinear estimation rules resulting from nonlinear and/or non-homogeneous ODE models, such as our model of glucose-insulin dynamics. We also propose to select the optimal number and location of knots for B-spline bases used to represent the ODE solution. The results of the small simulation study demonstrate advantages of optimized generalized smoothing in terms of smaller estimation errors for ODE parameters and smaller prediction errors for solutions of differential equations. Using the proposed approach to analyze the glucose and insulin concentration data in T1DM patients we obtained good approximation of global glucose-insulin dynamics and physiologically meaningful parameter estimates.

Glioma grade is associated with the accumulation and activity of cells bearing M2 monocyte markers.

PURPOSE: This study is directed at identifying the cell source(s) of immunomodulatory cytokines in high-grade gliomas and establishing whether the analysis of associated markers has implications for tumor grading. EXPERIMENTAL DESIGN: Glioma specimens classified as WHO grade II-IV by histopathology were assessed by gene expression analysis and immunohistochemistry to identify the cells producing interleukin (IL)-10, which was confirmed by flow cytometry and factor secretion in culture. Finally, principal component analysis (PCA) and mixture discriminant analysis (MDA) were used to investigate associations between expressed genes and glioma grade. RESULTS: The principle source of glioma-associated IL-10 is a cell type that bears phenotype markers consistent with M2 monocytes but does not express all M2-associated genes. Measures of expression of the M2 cell markers CD14, CD68, CD163, and CD204, which are elevated in high-grade gliomas, and the neutrophil/myeloid-derived suppressor cell (MDSC) subset marker CD15, which is reduced, provide the best index of glioma grade. CONCLUSIONS: Grade II and IV astrocytomas can be clearly differentiated on the basis of the expression of certain M2 markers in tumor tissues, whereas grade III astrocytomas exhibit a range of expression between the lower and higher grade specimens. The content of CD163(+) cells distinguishes grade III astrocytoma subsets with different prognosis.

Estimation via corrected scores in general semiparametric regression models with error-prone covariates.

This paper considers the problem of estimation in a general semiparametric regression model when error-prone covariates are modeled parametrically while covariates measured without error are modeled nonparametrically. To account for the effects of measurement error, we apply a correction to a criterion function. The specific form of the correction proposed allows Monte Carlo simulations in problems for which the direct calculation of a corrected criterion is difficult. Therefore, in contrast to methods that require solving integral equations of possibly multiple dimensions, as in the case of multiple error-prone covariates, we propose methodology which offers a simple implementation. The resulting methods are functional, they make no assumptions about the distribution of the mismeasured covariates. We utilize profile kernel and backfitting estimation methods and derive the asymptotic distribution of the resulting estimators. Through numerical studies we demonstrate the applicability of proposed methods to Poisson, logistic and multivariate Gaussian partially linear models. We show that the performance of our methods is similar to a computationally demanding alternative. Finally, we demonstrate the practical value of our methods when applied to Nevada Test Site (NTS) Thyroid Disease Study data.

SIMEX and standard error estimation in semiparametric measurement error models.

SIMEX is a general-purpose technique for measurement error correction. There is a substantial literature on the application and theory of SIMEX for purely parametric problems, as well as for purely non-parametric regression problems, but there is neither application nor theory for semiparametric problems. Motivated by an example involving radiation dosimetry, we develop the basic theory for SIMEX in semiparametric problems using kernel-based estimation methods. This includes situations that the mismeasured variable is modeled purely parametrically, purely non-parametrically, or that the mismeasured variable has components that are modeled both parametrically and nonparametrically. Using our asymptotic expansions, easily computed standard error formulae are derived, as are the bias properties of the nonparametric estimator. The standard error method represents a new method for estimating variability of nonparametric estimators in semiparametric problems, and we show in both simulations and in our example that it improves dramatically on first order methods.We find that for estimating the parametric part of the model, standard bandwidth choices of order O(n(-1/5)) are sufficient to ensure asymptotic normality, and undersmoothing is not required. SIMEX has the property that it fits misspecified models, namely ones that ignore the measurement error. Our work thus also more generally describes the behavior of kernel-based methods in misspecified semiparametric problems.

Aberrant crypt foci and semiparametric modeling of correlated binary data.

Motivated by the spatial modeling of aberrant crypt foci (ACF) in colon carcinogenesis, we consider binary data with probabilities modeled as the sum of a nonparametric mean plus a latent Gaussian spatial process that accounts for short-range dependencies. The mean is modeled in a general way using regression splines. The mean function can be viewed as a fixed effect and is estimated with a penalty for regularization. With the latent process viewed as another random effect, the model becomes a generalized linear mixed model. In our motivating data set and other applications, the sample size is too large to easily accommodate maximum likelihood or restricted maximum likelihood estimation (REML), so pairwise likelihood, a special case of composite likelihood, is used instead. We develop an asymptotic theory for models that are sufficiently general to be used in a wide variety of applications, including, but not limited to, the problem that motivated this work. The splines have penalty parameters that must converge to zero asymptotically: we derive theory for this along with a data-driven method for selecting the penalty parameter, a method that is shown in simulations to improve greatly upon standard devices, such as likelihood crossvalidation. Finally, we apply the methods to the data from our experiment ACF. We discover an unexpected location for peak formation of ACF.

The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements.

RATIONALE AND OBJECTIVES: The goal was to investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, because the latter are always qualified with respect to a given size range of nodules. MATERIALS AND METHODS: This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium (LIDC) using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a unidimensional and two bidimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the interradiologist variation, while those with at least one marking were used to examine the difference between the metrics. RESULTS: The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high interobserver variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges for the three-dimensional, unidimensional, and two bidimensional size metrics, respectively (in mm): 0.49-1.25, 0.67-2.55, 0.78-2.11, and 0.96-2.69. Also, a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on unidimensional, and the two bidimensional size measurements of 10 mm were 7.32, 7.72, and 6.29 mm, respectively. CONCLUSIONS: The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets.

Testing for spatial correlation in nonstationary binary data, with application to aberrant crypt foci in colon carcinogenesis.

In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen, with half the animals also being exposed to radiation. Spatially, we measured the existence of what are referred to as aberrant crypt foci (ACF), namely, morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score-type methods based upon the Matern and conditionally autoregressive (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score-type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran's test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage.