Neuroimaging feature extraction using a neural network classifier for imaging genetics.

BACKGROUND: Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. RESULTS: We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. CONCLUSIONS: The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

A Bayesian spatial model for imaging genetics.

We develop a Bayesian bivariate spatial model for multivariate regression analysis applicable to studies examining the influence of genetic variation on brain structure. Our model is motivated by an imaging genetics study of the Alzheimer's Disease Neuroimaging Initiative (ADNI), where the objective is to examine the association between images of volumetric and cortical thickness values summarizing the structure of the brain as measured by magnetic resonance imaging (MRI) and a set of 486 single nucleotide polymorphism (SNPs) from 33 Alzheimer's disease (AD) candidate genes obtained from 632 subjects. A bivariate spatial process model is developed to accommodate the correlation structures typically seen in structural brain imaging data. First, we allow for spatial correlation on a graph structure in the imaging phenotypes obtained from a neighborhood matrix for measures on the same hemisphere of the brain. Second, we allow for correlation in the same measures obtained from different hemispheres (left/right) of the brain. We develop a mean-field variational Bayes algorithm and a Gibbs sampling algorithm to fit the model. We also incorporate Bayesian false discovery rate (FDR) procedures to select SNPs. We implement the methodology in a new release of the R package bgsmtr. We show that the new spatial model demonstrates superior performance over a standard model in our application. Data used in the preparation of this article were obtained from the ADNI database (https://adni.loni.usc.edu).

Hospital Geographic Location and Unexpected Complications in term Newborns in Florida.

INTRODUCTION: Birth trauma rates in term of neonates is a quality measure used by the Joint Commission. In the United States birth trauma rates occurs at a rate of 37 per 1000 live births and are on the decline. However, this decline has been significantly lower among term neonates born in rural facilities. There is a critical lack of evidence toward the influence geographical risk factors has on birth trauma rates for neonatal patients. We sought to measure rural community and hospital characteristics associated with birth trauma. METHODS: A retrospective longitudinal study design was used to examine inpatient medical discharge data across 103 hospitals of neonates at birth from 2013 to 2018. Discharge data was linked to the American Hospital Association annual survey. We used a multi-level mixed effect model to investigate the relationship between individual and hospital-level attributes associated with increased risk of birth trauma among neonatal patients. RESULTS: We found that rural hospitals were 3.99 times (p < 0.001) more likely to experience higher birth trauma than urban hospitals. Medium sized hospitals were 2.11 times (p < 0.001) more likely to experience higher birth trauma. Hospitals who indicate having a safety culture were more likely (p < 0.05) to have high rates of birth trauma. DISCUSSION: Neonates born at rural hospitals, were more likely to experience a birth-related injury. Policy strategies focusing on improving health care quality in rural areas are critical to mitigating this increased risk of birth trauma. Further research is required to assess how physician characteristics may impact birth trauma rates.

A Review of Bayesian Hypothesis Testing and Its Practical Implementations.

We discuss hypothesis testing and compare different theories in light of observed or experimental data as fundamental endeavors in the sciences. Issues associated with the p-value approach and null hypothesis significance testing are reviewed, and the Bayesian alternative based on the Bayes factor is introduced, along with a review of computational methods and sensitivity related to prior distributions. We demonstrate how Bayesian testing can be practically implemented in several examples, such as the t-test, two-sample comparisons, linear mixed models, and Poisson mixed models by using existing software. Caveats and potential problems associated with Bayesian testing are also discussed. We aim to inform researchers in the many fields where Bayesian testing is not in common use of a well-developed alternative to null hypothesis significance testing and to demonstrate its standard implementation.

Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics.

We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer's disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.

Sparse Estimation Strategies in Linear Mixed Effect Models for High-Dimensional Data Application.

In a host of business applications, biomedical and epidemiological studies, the problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis for linear mixed models (LMM). We consider an efficient estimation strategy for high-dimensional data application, where the dimensions of the parameters are larger than the number of observations. In this paper, we are interested in estimating the fixed effects parameters of the LMM when it is assumed that some prior information is available in the form of linear restrictions on the parameters. We propose the pretest and shrinkage estimation strategies using the ridge full model as the base estimator. We establish the asymptotic distributional bias and risks of the suggested estimators and investigate their relative performance with respect to the ridge full model estimator. Furthermore, we compare the numerical performance of the LASSO-type estimators with the pretest and shrinkage ridge estimators. The methodology is investigated using simulation studies and then demonstrated on an application exploring how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer's disease.

Ant Colony System Optimization for Spatiotemporal Modelling of Combined EEG and MEG Data.

Electroencephalography/Magnetoencephalography (EEG/MEG) source localization involves the estimation of neural activity inside the brain volume that underlies the EEG/MEG measures observed at the sensor array. In this paper, we consider a Bayesian finite spatial mixture model for source reconstruction and implement Ant Colony System (ACS) optimization coupled with Iterated Conditional Modes (ICM) for computing estimates of the neural source activity. Our approach is evaluated using simulation studies and a real data application in which we implement a nonparametric bootstrap for interval estimation. We demonstrate improved performance of the ACS-ICM algorithm as compared to existing methodology for the same spatiotemporal model.

Silicon supplementation mitigates salinity stress on Ocimum basilicum L. via improving water balance, ion homeostasis, and antioxidant defense system.

Salinity is a key worldwide ecological restriction to sustainable crop production and food security. Various methods were used for inducing salinity tolerance including biotechnological approaches or application of stress tolerance-inducing substances. Silicon supplementation has a decisive role in alleviating of salinity injury, however, the definite mechanisms behind stay scantily understood, and must be examined. The imperative roles of sodium metasilicate (Si, 100 ppm) application methods (foliar spraying at 100 mg/l; soil additive at 100 mg/kg soil; foliar spraying at 100 mg/l plus soil additive at 100 mg/kg soil), in improving growth and essential oil yield, maintaining water status, activating antioxidant system, and keeping ion homeostasis of salt affected-sweet basil (6000 mg NaCl/kg soil) were studied. Salinity induced a notable increase in oxidative biomarkers, coupled with higher osmolyte concentration and osmotic potential (OP) values, as well as increased superoxide dismutase and peroxidase activities. Alternatively, sweet basil growth, essential oil yield, and catalase activity were reduced under salinity. Furthermore, salinity aggravated ion imbalance, decreased photosynthetic pigment and disrupted the plants' water status. Silicon application drastically increased osmolyte accumulation associated with sustained water status, increased OP, and improved osmotic adjustment (OA) capacity. Additionally, Si application enhanced antioxidant aptitude associated with decreased oxidative biomarkers and improved growth, photosynthetic pigment, and essential oil yield. Greater outcomes were achieved with the foliar spraying method, compared with other application methods. Salinity stress evoked modification in protein assimilation capacity and possibly will withdraw protein biosynthesis and reduce total protein band number; however, Si application may adjust the expression of salinity inducible proteins. Foliar spraying of Si with or without soil additive accelerates the expression of peroxidase isozyme over salinized or control plants. Collectively, Si foliar spraying alleviated salinity-related injuries on sweet basil by maintaining water status, increasing osmolyte assimilation, improving OA, enhancing redox homeostasis, and antioxidant capacity.

A Review of Statistical Methods in Imaging Genetics.

With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function, and brain-related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention, and treatment of numerous complex brain-related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high-dimensional imaging phenotypes and high-dimensional genetic data, a big data squared (BD2) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high-dimensional nature of BD2, various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel-wise approaches, reduced rank regression, mixture models, and group sparse multi-task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research.

A Bayesian group sparse multi-task regression model for imaging genetics.

MOTIVATION: Recent advances in technology for brain imaging and high-throughput genotyping have motivated studies examining the influence of genetic variation on brain structure. Wang et al. have developed an approach for the analysis of imaging genomic studies using penalized multi-task regression with regularization based on a novel group l2,1-norm penalty which encourages structured sparsity at both the gene level and SNP level. While incorporating a number of useful features, the proposed method only furnishes a point estimate of the regression coefficients; techniques for conducting statistical inference are not provided. A new Bayesian method is proposed here to overcome this limitation. RESULTS: We develop a Bayesian hierarchical modeling formulation where the posterior mode corresponds to the estimator proposed by Wang et al. and an approach that allows for full posterior inference including the construction of interval estimates for the regression parameters. We show that the proposed hierarchical model can be expressed as a three-level Gaussian scale mixture and this representation facilitates the use of a Gibbs sampling algorithm for posterior simulation. Simulation studies demonstrate that the interval estimates obtained using our approach achieve adequate coverage probabilities that outperform those obtained from the nonparametric bootstrap. Our proposed methodology is applied to the analysis of neuroimaging and genetic data collected as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI), and this analysis of the ADNI cohort demonstrates clearly the value added of incorporating interval estimation beyond only point estimation when relating SNPs to brain imaging endophenotypes. AVAILABILITY AND IMPLEMENTATION: Software and sample data is available as an R package 'bgsmtr' that can be downloaded from The Comprehensive R Archive Network (CRAN). CONTACT: nathoo@uvic.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Time series analysis of fMRI data: Spatial modelling and Bayesian computation.

Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. Spatial models and Bayesian approaches for inference in such models have advantages over more traditional mass univariate approaches; however, a major challenge for such analyses is the required computation. As a result, the neuroimaging community has embraced approximate Bayesian inference based on mean-field variational Bayes (VB) approximations. These approximations are implemented in standard software packages such as the popular statistical parametric mapping software. While computationally efficient, the quality of VB approximations remains unclear even though they are commonly used in the analysis of neuroimaging data. For reliable statistical inference, it is important that these approximations be accurate and that users understand the scenarios under which they may not be accurate. We consider this issue for a particular model that includes spatially varying coefficients. To examine the accuracy of the VB approximation, we derive Hamiltonian Monte Carlo (HMC) for this model and conduct simulation studies to compare its performance with VB in terms of estimation accuracy, posterior variability, the spatial smoothness of estimated images, and computation time. As expected, we find that the computation time required for VB is considerably less than that for HMC. In settings involving a high or moderate signal-to-noise ratio (SNR), we find that the 2 approaches produce very similar results suggesting that the VB approximation is useful in this setting. On the other hand, when one considers a low SNR, substantial differences are found, suggesting that the approximation may not be accurate in such cases and we demonstrate that VB produces Bayes estimators with larger mean squared error. A comparison of the 2 computational approaches in an application examining the hemodynamic response to face perception in addition to a comparison with the traditional mass univariate approach in this application is also considered. Overall, our work clarifies the usefulness of VB for the spatiotemporal analysis of fMRI data, while also pointing out the limitation of VB when the SNR is low and the utility of HMC in this case.

Chymase Level Is a Predictive Biomarker of Dengue Hemorrhagic Fever in Pediatric and Adult Patients.

Background: Most patients with dengue experience mild disease, dengue fever (DF), while few develop the life-threatening diseases dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS). No laboratory tests predict DHF or DSS. We evaluated whether the serum chymase level can predict DHF or DSS in adult and pediatric patients and the influence of preexisting conditions (PECs) on chymase levels. Methods: Serum chymase levels were measured in patients presenting with undifferentiated fever to hospitals in Colombo District, Sri Lanka. The value of serum the chymase concentration and clinical signs and symptoms as predictors of DHF and/or DSS was evaluated by multivariate analysis. We assessed the influence of age, PECs, and day after fever onset on the robustness of the chymase level as a biomarker for DHF and/or DSS. Results: An elevated chymase level in acute phase blood samples was highly indicative of later diagnosis of DHF or DSS for pediatric and adult patients with dengue. No recorded PECs prevented an increase in the chymase level during DHF. However, certain PECs (obesity and cardiac or lung-associated diseases) resulted in a concomitant increase in chymase levels among adult patients with DHF. Conclusions: These results show that patients with acute dengue who present with high levels of serum chymase consistently are at greater risk of DHF. The chymase level is a robust prognostic biomarker of severe dengue for adult and pediatric patients.

Bayesian Computation for Log-Gaussian Cox Processes: A Comparative Analysis of Methods.

The Log-Gaussian Cox Process is a commonly used model for the analysis of spatial point pattern data. Fitting this model is difficult because of its doubly-stochastic property, i.e., it is an hierarchical combination of a Poisson process at the first level and a Gaussian Process at the second level. Various methods have been proposed to estimate such a process, including traditional likelihood-based approaches as well as Bayesian methods. We focus here on Bayesian methods and several approaches that have been considered for model fitting within this framework, including Hamiltonian Monte Carlo, the Integrated nested Laplace approximation, and Variational Bayes. We consider these approaches and make comparisons with respect to statistical and computational efficiency. These comparisons are made through several simulation studies as well as through two applications, the first examining ecological data and the second involving neuroimaging data.

Skew-elliptical spatial random effect modeling for areal data with application to mapping health utilization rates.

Mixed models incorporating spatially correlated random effects are often used for the analysis of areal data. In this setting, spatial smoothing is introduced at the second stage of a hierarchical framework, and this smoothing is often based on a latent Gaussian Markov random field. The Markov random field provides a computationally convenient framework for modeling spatial dependence; however, the Gaussian assumption underlying commonly used models can be overly restrictive in some applications. This can be a problem in the presence of outliers or discontinuities in the underlying spatial surface, and in such settings, models based on non-Gaussian spatial random effects are useful. Motivated by a study examining geographic variation in the treatment of acute coronary syndrome, we develop a robust model for smoothing small-area health service utilization rates. The model incorporates non-Gaussian spatial random effects, and we develop a formulation for skew-elliptical areal spatial models. We generalize the Gaussian conditional autoregressive model to the non-Gaussian case, allowing for asymmetric skew-elliptical marginal distributions having flexible tail behavior. The resulting new models are flexible, computationally manageable, and can be implemented in the standard Bayesian software WinBUGS. We demonstrate performance of the proposed methods and comparisons with other commonly used Gaussian and non-Gaussian spatial prior formulations through simulation and analysis in our motivating application, mapping rates of revascularization for patients diagnosed with acute coronary syndrome in Quebec, Canada.

Improving Rates of Germline BRCA Mutation Testing for Patients With Ovarian Cancer in Vancouver Island, British Columbia, Canada.

PURPOSE: Despite more than a decade of endorsement from multiple international cancer authorities advocating all women with ovarian cancer be offered germline breast cancer (BRCA) gene testing, British Columbia Cancer Victoria was not meeting this target. A quality improvement project was undertaken with the aim of increasing completed BRCA testing rates for all eligible patients seen at British Columbia Cancer Victoria to > 90% by 1 year from April 2016. METHODS: A current state analysis was completed, and multiple change ideas were developed, including education of medical oncologists, referral process update, initiating a group consenting seminar, and engagement of a nurse practitioner to lead the seminar. We used a retrospective chart audit from December 2014 to February 2018. On April 15, 2016, we initiated our Plan, Do, Study, Act (PDSA) cycles and completed them on February 28, 2018. We evaluated sustainability through an additional retrospective chart audit from January 2021 to August 2021. RESULTS: Patients with completed germline BRCA genetic testing climbed from an average of 58%-89% per month. Before our project, patients waited on average 243 days (± 214) for their genetic test results. After implementation, patients received results within 118 days (± 98). This was sustained with an average of 83% of patients per month having completed germline BRCA testing almost 3 years after project completion. CONCLUSION: Our quality improvement initiative resulted in a sustained increase in germline BRCA test completion for eligible patients with ovarian cancer.

Investigating the relationship between the Bayes factor and the separation of credible intervals.

We examined the relationship between the Bayes factor and the separation of credible intervals in between- and within-subject designs under a range of effect and sample sizes. For the within-subject case, we considered five intervals: (1) the within-subject confidence interval of Loftus and Masson (1994); (2) the within-subject Bayesian interval developed by Nathoo et al. (2018), whose derivation conditions on estimated random effects; (3) and (4) two modifications of (2) based on a proposal by Heck (2019) to allow for shrinkage and account for uncertainty in the estimation of random effects; and (5) the standard Bayesian highest-density interval. We derived and observed through simulations a clear and consistent relationship between the Bayes factor and the separation of credible intervals. Remarkably, for a given sample size, this relationship is described well by a simple quadratic exponential curve and is most precise in case (4). In contrast, interval (5) is relatively wide due to between-subjects variability and is likely to obscure effects when used in within-subject designs, rendering its relationship with the Bayes factor unclear in that case. We discuss how the separation percentage of (4), combined with knowledge of the sample size, could provide evidence in support of either a null or an alternative hypothesis. We also present a case study with example data and provide an R package 'rmBayes' to enable computation of each of the within-subject credible intervals investigated here using a number of possible prior distributions.

CD8 + T cell infiltration is associated with improved survival and negatively correlates with hypoxia in clear cell ovarian cancer.

Unlike other histological types of epithelial ovarian carcinoma, clear cell ovarian carcinoma (CCOC) has poor response to therapy. In many other carcinomas, expression of the hypoxia-related enzyme Carbonic anhydrase IX (CAIX) by cancer cells is associated with poor prognosis, while the presence of CD8 + tumor-infiltrating lymphocytes (TIL) is positively prognostic. We employed [18F]EF5-PET/CT imaging, transcriptome profiling, and spatially-resolved histological analysis to evaluate relationships between CAIX, CD8, and survival in CCOC. Tissue microarrays (TMAs) were evaluated for 218 cases in the Canadian COEUR study. Non-spatial relationships between CAIX and CD8 were investigated using Spearman rank correlation, negative binomial regression and gene set enrichment analysis. Spatial relationships at the cell level were investigated using the cross K-function. Survival analysis was used to assess the relationship of CAIX and CD8 with patient survival for 154 cases. CD8 + T cell infiltration positively predicted survival with estimated hazard ratio 0.974 (95% CI 0.950, 1000). The negative binomial regression analysis found a strong TMA effect (p-value < 0.0001). It also indicated a negative association between CD8 and CAIX overall (p-value = 0.0171) and in stroma (p-value = 0.0050) but not in tumor (p-value = 0.173). Examination of the spatial association between the locations of CD8 + T cells and CAIX cells found a significant amount of heterogeneity in the first TMA, while in the second TMA there was a clear signal indicating negative spatial association in stromal regions. These results suggest that hypoxia may contribute to immune exclusion, primarily mediated by effects in stroma.

Radioprotective potential of whey protein against gamma irradiation-induced lingual damage.

Introduction: Ionizing radiation (IR) is effectively used in the treatment of oral malignancies; however, it might also significantly harm the surrounding tissues. Whey protein isolate (WP) is a protein derived from milk that exhibits a wide range of bioactivities. Therefore, the present research aimed to delineate the mitigating impact of WP against gamma irradiation-induced lingual damage. Methods: Rats were randomized into 5 groups: Control (saline, orally, 14 days), WP (WP; 0.5 g/kg b. w., orally, 14 days), IR (saline, orally, 14 days, exposed to 6 and 3 Gy on days 4 and 6, respectively), WP+IR (WP was given orally for 14 days before and after IR exposure; exposed to 6 and 3 Gy on days 4 and 6, respectively), and IR+WP (WP, orally, started 24 h after 1st IR exposure till the end of the experiment) groups. Samples were collected at two-time intervals (on the 7th and 14th days). Results and Discussion: Oxidative stress was stimulated upon IR exposure in tongue, indicated by boosted malondialdehyde (MDA) level, along with a decrease in the total antioxidant capacity (TAC) level, superoxide dismutase (SOD), and catalase (CAT) activities. Additionally, IR exposure depicted an increase of serum IgE, inflammatory cytokines, including tumor necrosis factor-α (TNF-α), interleukin (IL)-6, along with overexpression mRNA levels of nuclear factor kappa-B transcription factor/p65 (NF-κB/p65), and down-regulation of nuclear factor erythroid 2-related factor 2 (NRF2) and heme oxygenase (HO-1) mRNA levels in tongue tissue. Moreover, IR triggered alterations in lingual histological architecture. The antioxidant and anti-inflammatory properties of WP mitigated oxidative damage, inflammation, and desquamation that were brought on following IR exposure. The protective administration of WP markedly decreases IR-induced lingual harm compared to the mitigation protocol. Our findings recommend WP supplements to the diets of cancer patients undergoing IR that might aid radioprotective effects.

Impaired neutralizing activity by transplacental measles antibodies in infants born to HIV-1-infected mothers.

AIM: Growing numbers of newborns are saved from HIV infection through increased access to mother-to-child transmission prevention programmes. The maternally derived humoral immunity of these children might be impaired, both in terms of quantity and in terms of quality, with consequences for the timing of immunization against measles. METHODS: A cell-ELISA technique compared the neutralizing activity on Edmonston strain measles virus of sera from 1- to 4-month-old infants. Ten serum specimens came from noninfected infants of HIV-infected mothers and another 10 from infants of healthy mothers. The sera were matched for the level of conventional ELISA measles antibodies. RESULTS: Reflecting infection of the Vero cells by non-neutralized virus, optical density values were significantly higher for the sera from the children of the HIV-infected mothers than for those of the noninfected mothers (p < 0.001). CONCLUSION: Maternally derived protection against measles may be impaired by the mother's HIV infection, relating to the quality rather than to the quantity of transplacental antibodies. Selective, early immunization with live attenuated measles vaccine should be evaluated in noninfected children of HIV-1-infected mothers.

Immunohistochemical expression of cyclin A in testicular biopsies of fertile and infertile men: correlation with the morphometry of seminiferous tubules.

Cyclin A is a member of the cyclin family of proteins, which are required for both the mitotic and meiotic divisions that characterise spermatogenesis in human and other mammalian species. The data on cyclin A expression in various human spermatogenic disorders and its relationship to the morphology of seminiferous tubules are not well clarified. This study aimed to evaluate the immunohistochemical expression of cyclin A in testicular biopsies of different spermatogenic disorders correlating with the morphology of seminiferous tubules using morphometry tools. Immunohistochemical evaluation of cyclin A was carried out on testicular biopsies obtained from 48 infertile males (nonobstructive azoospermia) and 15 normal subjects together with using semiautomatic morphometric analysis for evaluation of seminiferous tubules. Cyclin A is expressed in 100% of normal and hypospermatogenesis groups and in 80% of maturation arrest group, with complete absence in Sertoli cell only group. In positive cases, cyclin A stained the nuclei of spermatogonia and primary spermatocytes with a higher intensity of expression in normal cases compared with infertile group. Cyclin A expression was significantly associated with the different examined morphometric parameters. Cyclin A is involved in both mitosis and meiosis of human spermatogenesis as it is expressed in spermatogonia and primary spermatocytes. Morphometry of human testis is intimately correlated with the testicular histopathology.