Child, caregiver, and family factors associated with child dental utilization among Mexican migrant families in California.

OBJECTIVE: To identify associations between child, caregiver, and family-level factors and child dental utilization. RESEARCH DESIGN: Cross-sectional oral health survey. PARTICIPANTS: Caregivers and one study child (ages 0-17) from Mexican migrant families in northern San Diego county, CA (n=142). METHODS: Caregivers reported on child's dental care utilization history and related factors, including: child (age, gender, dental insurance, source of care, believed to have cavities), caregiver (marital status, income, education, acculturation level, depressive symptoms), and family cohesion. Descriptive and logistic regression models identified predisposing, enabling, and need factors associated with child dental utilization during the past year. RESULTS: Most (76%) children had visited the dentist in the past year, while 8.6% had never been. Child factors (gender, insurance), caregiver factors (education, depressive symptoms), and family cohesion were each associated with child dental utilization in the bivariate analyses. In the final adjusted model, uninsured children were less likely to have a past year dental visit compared to insured children (Odds Ratio (OR) = 0.23, 95% Confidence Interval (CI) = 0.06-0.96). Children whose caregivers visited the dentist were 4.29 times more likely to visit the dentist in the past year (CI=1.36-13.61). Higher caregiver education was positively associated with child dental utilization (OR=4.50, CI=1.50-13.55). CONCLUSION: Child age and dental insurance, and caregiver education and dental utilization history were associated with whether or not a child had a past year dental visit. Ensuring child dental coverage and caregiver access to dental care may promote regular dental utilization by children.

Effect of passive heat stress on arterial stiffness in smokers versus non-smokers.

In non-smokers, passive heat stress increases shear stress and vasodilation, decreasing arterial stiffness. Smokers, who reportedly have arterial dysfunction, may have similar improvements in arterial stiffness with passive heat stress. Therefore, we examined the effects of an acute bout of whole-body passive heat stress on arterial stiffness in smokers vs. non-smokers. Thirteen smokers (8.8 ± 5.5 [median = 6] cigarettes per day for > 4 years) and 13 non-smokers matched for age, mass, height, and exercise habits (27 ± 8 years; 78.8 ± 15.4 kg; 177.6 ± 6.7 cm) were passively heated to 1.5 °C core temperature (T C) increase. At baseline and each 0.5 °C T C increase, peripheral (pPWV) and central pulse wave velocity (cPWV) were measured via Doppler ultrasound. No differences existed between smokers and non-smokers for any variables (all p > .05), except cPWV slightly increased from baseline (526.7 ± 81.7 cm · s(-1)) to 1.5 °C ΔT C (579.7 ± 69.8 cm · s(-1); p < 0.005), suggesting heat stress acutely increased central arterial stiffness. pPWV did not change with heating (grand mean: baseline = 691.9 ± 92.9 cm · s(-1); 1.5 °C ΔT C = 691.9 ± 79.5 cm · s(-1); p > 0.05). Changes in cPWV and pPWV during heating correlated (p < 0.05) with baseline PWV in smokers (cPWV: r = -0.59; pPWV: r = -0.62) and non-smokers (cPWV: r = -0.45; pPWV: r = -0.77). Independent of smoking status, baseline stiffness appears to mediate the magnitude of heating-induced changes in arterial stiffness.

Comparison of respiratory virus shedding by conventional and molecular testing methods in patients with haematological malignancy.

Respiratory viruses (RV) are a leading cause of infection-related morbidity and mortality for patients undergoing treatment for cancer. This analysis compared duration of RV shedding as detected by culture and PCR among patients in a high-risk oncology setting (adult patients with haematological malignancy and/or stem cell transplant and all paediatric oncology patients) and determined risk factors for extended shedding. RV infections due to influenza virus, parainfluenza virus (PIV), human metapneumovirus (HMPV) and respiratory syncytial virus (RSV) from two study periods-January 2009-September 2011 (culture-based testing) and September 2011-April 2013 (PCR-based testing)-were reviewed retrospectively. Data were collected from patients in whom re-testing for viral clearance was carried out within 5-30 days after the most recent test. During the study period 456 patients were diagnosed with RV infection, 265 by PCR and 191 by culture. The median range for duration of shedding (days) by culture and PCR, respectively, were as follows-influenza virus: 13 days (5-38 days) versus 14 days (5-58 days), p 0.5; RSV: 11 days (5-35 days) versus 16 days (5-50 days), p 0.001; PIV: 9 days (5-41 days) versus 17 days (5-45 days), p ≤0.0001; HMPV 10.5 days (5-29 days) versus 14 days (5-42 days), p 0.2. In multivariable analysis, age and underlying disease or transplant were not independently associated with extended shedding regardless of testing method. In high-risk oncology settings for respiratory illness due to RSV and PIV, the virus is detectable by PCR for a longer period of time than by culture and extended shedding is observed.

Multiscale denoising of biological data: a comparative analysis.

Measured microarray genomic and metabolic data are a rich source of information about the biological systems they represent. For example, time-series biological data can be used to construct dynamic genetic regulatory network models, which can be used to design intervention strategies to cure or manage major diseases. Also, copy number data can be used to determine the locations and extent of aberrations in chromosome sequences. Unfortunately, measured biological data are usually contaminated with errors that mask the important features in the data. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, different batch as well as online multiscale filtering techniques are used to denoise biological data contaminated with white or colored noise. The performances of these techniques are demonstrated and compared to those of some conventional low-pass filters using two case studies. The first case study uses simulated dynamic metabolic data, while the second case study uses real copy number data. Simulation results show that significant improvement can be achieved using multiscale filtering over conventional filtering techniques.

Inference of gene regulatory networks using S-system: a unified approach.

With the increased availability of DNA microarray time-series data, it is possible to discover dynamic gene regulatory networks (GRNs). S-system is a promising model to capture the rich dynamics of GRNs. However, owing to the complexity of the inference problem and limited number of available data comparing to the number of unknown kinetic parameters, S-system can only be applied to a very small GRN with few parameters. This significantly limits its applications. A unified approach to infer GRNs using the S-system model is proposed. In order to discover the structure of large-scale GRNs, a simplified S-system model is proposed that enables fast parameter estimation to determine the major gene interactions. If a detailed S-system model is desirable for a subset of genes, a two-step method is proposed where the range of the parameters will be determined first using genetic programming and recursive least square estimation. Then the mean values of the parameters will be estimated using a multi-dimensional optimisation algorithm. Both the downhill simplex algorithm and modified Powell algorithm are tested for multi-dimensional optimisation. A 50-dimensional synthetic model with 51 parameters for each gene is tested for the applicability of the simplified S-system model. In addition, real measurement data pertaining to yeast protein synthesis are used to demonstrate the effectiveness of the proposed two-step method to identify the detailed interactions among genes in small GRNs.

Inference of Boolean networks under constraint on bidirectional gene relationships.

The coefficient of determination (CoD) has been used to infer Boolean networks (BNs) from steady-state data, in particular, to estimate the constituent BNs for a probabilistic BN. The advantage of the CoD method over design methods that emphasise graph topology or attractor structure is that the CoD produces a network based on strong predictive relationships between target genes and their predictor (parent) genes. The disadvantage is that spurious attractor cycles appear in the inferred network, so that there is poor inference relative to the attractor structure, that is, relative to the steady-state behaviour of the network. Given steady-state data, there should not be a significant amount of steady-state probability mass in the inferred network lying outside the mass of the data distribution; however, the existence of spurious attractor cycles creates a significant amount of steady-state probability mass not accounted for by the data. Using steady-state data hampers design because the lack of temporal data causes CoD design to suffer from a lack of directionality with regard to prediction. This results in spurious bidirectional relationships among genes in which two genes are among the predictors for each other, when actually only one of them should be a predictor of the other, thereby creating a spurious attractor cycle. This paper characterises the manner in which bidirectional relationships affect the attractor structure of a BN. Given this characterisation, the authors propose a constrained CoD inference algorithm that outperforms unconstrained CoD inference in avoiding the creation of spurious non-singleton attractor. Algorithm performances are compared using a melanoma-based network.

On approximate stochastic control in genetic regulatory networks.

The control of probabilistic Boolean networks as a model of genetic regulatory networks is formulated as an optimal stochastic control problem and has been solved using dynamic programming; however, the proposed methods fail when the number of genes in the network goes beyond a small number. There are two dimensionality problems. First, the complexity of optimal stochastic control exponentially increases with the number of genes. Second, the complexity of estimating the probability distributions specifying the model increases exponentially with the number of genes. We propose an approximate stochastic control method based on reinforcement learning that mitigates the curses of dimensionality and provides polynomial time complexity. Using a simulator, the proposed method eliminates the complexity of estimating the probability distributions and, because the method is a model-free method, it eliminates the impediment of model estimation. The method can be applied on networks for which dynamic programming cannot be used owing to computational limitations. Experimental results demonstrate that the performance of the method is close to optimal stochastic control.

Multi-class cancer classification using multinomial probit regression with Bayesian gene selection.

We consider the problems of multi-class cancer classification from gene expression data. After discussing the multinomial probit regression model with Bayesian gene selection, we propose two Bayesian gene selection schemes: one employs different strongest genes for different probit regressions; the other employs the same strongest genes for all regressions. Some fast implementation issues for Bayesian gene selection are discussed, including preselection of the strongest genes and recursive computation of the estimation errors using QR decomposition. The proposed gene selection techniques are applied to analyse real breast cancer data, small round blue-cell tumours, the national cancer institute's anti-cancer drug-screen data and acute leukaemia data. Compared with existing multi-class cancer classifications, our proposed methods can find which genes are the most important genes affecting which kind of cancer. Also, the strongest genes selected using our methods are consistent with the biological significance. The recognition accuracies are very high using our proposed methods.

Genomic profiles and predictive biological networks in oxidant-induced atherogenesis.

Atherogenic stimuli trigger complex responses in vascular smooth muscle cells (VSMCs) that culminate in activation/repression of overlapping signal transduction cascades involving oxidative stress. In the case of benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon present in tobacco smoke, the atherogenic response involves interference with redox homeostasis by oxidative intermediates of BaP metabolism. The present studies were conducted to define genomic profiles and predictive gene biological networks associated with the atherogenic response of murine (aortic) VSMCs to BaP. A combined oxidant-antioxidant treatment regimen was used to identify redox-sensitive targets during the early course of the atherogenic response. Gene expression profiles were defined using cDNA microarrays coupled to analysis of variance and several clustering methodologies. A predictor algorithm was then applied to gain insight into critical gene-gene interactions during atherogenesis. Supervised and nonsupervised analyses identified clones highly regulated by BaP, unaffected by antioxidant, and neutralized by combined chemical treatments. Lymphocyte antigen-6 complex, histocompatibility class I component factors, secreted phosphoprotein, and several interferon-inducible proteins were identified as novel redox-regulated targets of BaP. Predictor analysis confirmed these relationships and identified immune-related genes as critical molecular targets of BaP. Redox-dependent patterns of gene deregulation indicate that oxidative stress plays a prominent role during the early stages of BaP-induced atherogenesis.

Analysis of expression patterns: the scope of the problem, the problem of scope.

Studies of the expression patterns of many genes simultaneously lead to the observation that even in closely related pathologies, there are numerous genes that are differentially expressed in consistent patterns correlated to each sample type. The early uses of the enabling technology, microarrays, was focused on gathering mechanistic biological insights. The early findings now pose another clear challenge, finding ways to effectively use this kind of information to develop diagnostics.

A clinical scoring system predicts the yield of diagnostic laparoscopy in patients with potentially resectable hepatic colorectal metastases.

BACKGROUND: Laparoscopy may identify occult metastatic disease and prevent unnecessary laparotomy in some patients with potentially resectable colorectal liver metastases but is unnecessary in the majority of individuals who undergo resection. The objectives of the current study were to assess the impact of laparoscopy after extensive preoperative imaging and to determine whether a preoperative clinical risk score can identify those patients most likely to benefit from the procedure. METHODS: Between December 1997 and July 1999, 103 consecutive patients with potentially resectable colorectal liver metastases underwent laparoscopy prior to planned laparotomy and partial hepatectomy. Surgical findings, length of hospital stay, and hospital charges were analyzed. Patients were assigned a clinical risk score (CRS) based on five factors related to the primary tumor and the hepatic disease. The likelihood of finding occult unresectable disease and the yield of laparoscopy were analyzed with respect to the CRS. RESULTS: Seventy-seven patients (75%) underwent resection. Laparoscopy identified 14 of 26 patients with unresectable disease, 10 of whom were spared an unnecessary laparotomy. In patients who underwent biopsy only, the laparoscopic identification of unresectable disease shortened the hospital stay (1.2 +/- 0.6 days vs. 5.8 +/- 2.3 days; p = 0.0001) and reduced the total hospital charges by 55% (P = 0.0001). The CRS predicted the likelihood of occult unresectable disease, which was 12% in those with a score < or = 2 but increased to 42% in those with a score > 2 (P = 0.001). If laparoscopy were used only in high risk patients (CRS > 2), 57 laparoscopies would have been avoided and the net savings doubled. CONCLUSIONS: With extensive preoperative imaging, the vast majority of patients with potentially resectable hepatic colorectal metastases do not benefit from laparoscopy. However, in the minority of patients with occult unresectable disease, laparoscopy prevents unnecessary laparotomy and reduces hospital stay and the total hospital charges. The CRS, previously shown to predict survival after hepatic resection, identifies those high risk patients most likely to benefit from laparoscopy and may improve resource utilization.

Gene-expression profiles in hereditary breast cancer.

BACKGROUND: Many cases of hereditary breast cancer are due to mutations in either the BRCA1 or the BRCA2 gene. The histopathological changes in these cancers are often characteristic of the mutant gene. We hypothesized that the genes expressed by these two types of tumors are also distinctive, perhaps allowing us to identify cases of hereditary breast cancer on the basis of gene-expression profiles. METHODS: RNA from samples of primary tumor from seven carriers of the BRCA1 mutation, seven carriers of the BRCA2 mutation, and seven patients with sporadic cases of breast cancer was compared with a microarray of 6512 complementary DNA clones of 5361 genes. Statistical analyses were used to identify a set of genes that could distinguish the BRCA1 genotype from the BRCA2 genotype. RESULTS: Permutation analysis of multivariate classification functions established that the gene-expression profiles of tumors with BRCA1 mutations, tumors with BRCA2 mutations, and sporadic tumors differed significantly from each other. An analysis of variance between the levels of gene expression and the genotype of the samples identified 176 genes that were differentially expressed in tumors with BRCA1 mutations and tumors with BRCA2 mutations. Given the known properties of some of the genes in this panel, our findings indicate that there are functional differences between breast tumors with BRCA1 mutations and those with BRCA2 mutations. CONCLUSIONS: Significantly different groups of genes are expressed by breast cancers with BRCA1 mutations and breast cancers with BRCA2 mutations. Our results suggest that a heritable mutation influences the gene-expression profile of the cancer.

Small sample issues for microarray-based classification.

In order to study the molecular biological differences between normal and diseased tissues, it is desirable to perform classification among diseases and stages of disease using microarray-based gene-expression values. Owing to the limited number of microarrays typically used in these studies, serious issues arise with respect to the design, performance and analysis of classifiers based on microarray data. This paper reviews some fundamental issues facing small-sample classification: classification rules, constrained classifiers, error estimation and feature selection. It discusses both unconstrained and constrained classifier design from sample data, and the contributions to classifier error from constrained optimization and lack of optimality owing to design from sample data. The difficulty with estimating classifier error when confined to small samples is addressed, particularly estimating the error from training data. The impact of small samples on the ability to include more than a few variables as classifier features is explained.

General nonlinear framework for the analysis of gene interaction via multivariate expression arrays.

A cDNA microarray is a complex biochemical-optical system whose purpose is the simultaneous measurement of gene expression for thousands of genes. In this paper we propose a general statistical approach to finding associations between the expression patterns of genes via the coefficient of determination. This coefficient measures the degree to which the transcriptional levels of an observed gene set can be used to improve the prediction of the transcriptional state of a target gene relative to the best possible prediction in the absence of observations. The method allows incorporation of knowledge of other conditions relevant to the prediction, such as the application of particular stimuli or the presence of inactivating gene mutations, as predictive elements affecting the expression level of a given gene. Various aspects of the method are discussed: prediction quantification, unconstrained prediction, constrained prediction using ternary perceptrons, and design of predictors given small numbers of replicated microarrays. The method is applied to a set of genes undergoing genotoxic stress for validation according to the manner in which it points toward previously known and unknown relationships. The entire procedure is supported by software that can be applied to large gene sets, has a number of facilities to simplify data analysis, and provides graphics for visualizing experimental data, multiple gene interaction, and prediction logic.

Molecular classification of cutaneous malignant melanoma by gene expression profiling.

The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

Further characterization of the gonad-specific virus of corn earworm, Helicoverpa zea.

The gonad-specific virus (GSV) is a DNA virus infecting the reproductive tracts of adults of both sexes of the corn earworm, Helicoverpa zea, causing severe tissue deformities leading to sterility. Atypical occlusion bodies containing large concentrations of virions embedded in a granular matrix were seen in the lumen of the oviduct and the bursa copulatrix of infected females. The virus, transmitted by both sexes, was successfully propagated in vivo and in tissue culture. The GSV genome is about 225 kb in size, with no apparent similarity to the nucleopolyhedrovirus type species, AcMNPV, genomic DNA, as determined by Southern hybridization. PCR amplification of GSV genomic DNA with primers derived from the highly conserved polyhedra gene of several baculoviruses indicated no similarity. GSV at 10(-2) female equivalents (based on virus obtained from the bursa copulatrix and oviducts of one infected female) injected into a newly emerged female and mated to a normal male resulted in >95% agonadal progeny. However, at lower doses, some of the adult progeny looked normal but apparently carried a low level of the virus that could be responsible for sustenance of infection in a given colony, as well as in nature.

Hand-assisted laparoscopic liver resection: lessons from an initial experience.

BACKGROUND: Recent innovations in laparoscopic instrumentation make routine resection of solid organs a clinical possibility. HYPOTHESIS: Hand-assisted laparoscopic liver resection is a safe and feasible procedure for solitary cancers requiring removal of 2 segments of liver or less. DESIGN AND PATIENTS: Eleven patients with liver tumors deemed technically resectable by laparoscopic techniques were subjected to laparoscopic evaluation and attempted hand-assisted laparoscopic resection between July 1998 and July 1999. During the same period, 230 patients underwent open liver resection. SETTING: Tertiary care referral center for liver cancer. MAIN OUTCOME MEASURES: Success of laparoscopic resection, reasons for conversion to open liver resection, blood loss, tumor clearance margin, complications, and length of hospital stay. RESULTS: Five patients underwent successful resection by the hand-assisted laparoscopic technique. Data from the 5 successful cases and the 6 aborted cases are presented to outline the issues and the lessons learned. CONCLUSIONS: In selected patients, hand-assisted laparoscopic liver resection can be safely performed and might have potential advantages over traditional liver resection if the tumor is limited to the left lateral segment or is at the margins of the liver.

Multivariate measurement of gene expression relationships.

The operational activities of cells are based on an awareness of their current state, coupled to a programmed response to internal and external cues in a context-dependent manner. One key goal of functional genomics is to develop analytical methods for delineating the ways in which the individual actions of genes are integrated into our understanding of the increasingly complex systems of organelle, cell, organ, and organism. This paper describes a novel approach to assess the codetermination of gene transcriptional states based upon statistical evaluation of reliably informative subsets of data derived from large-scale simultaneous gene expression measurements with cDNA microarrays. The method finds associations between the expression patterns of individual genes by determining whether knowledge of the transcriptional levels of a small gene set can be used to predict the associated transcriptional state of another gene. To test this approach for identification of the relevant contextual elements of cellular response, we have modeled our approach using data from known gene response pathways including ionizing radiation and downstream targets of inactivating gene mutations. This approach strongly suggests that evaluation of the transcriptional status of a given gene(s) can be combined with data from global expression analyses to predict the expression level of another gene. With data sets of the size currently available, this approach should be useful in finding sets of genes that participate in particular biological processes. As larger data sets and more computing power become available, the method can be extended to validating and ultimately identifying biologic (transcriptional) pathways based upon large-scale gene expression analysis.

Evidence for integration of Glyptapanteles indiensis polydnavirus DNA into the chromosome of Lymantria dispar in vitro.

Polydnaviruses replicate within calyx cells of the female ovaries of certain species of parasitic wasps and are required for the successful parasitization of lepidopteran hosts. These viruses, which have unusual double-stranded circular DNA segmented genomes, are integrated as proviruses into the genomes of their associated wasp hosts and are believed to be transmitted vertically through germline tissue. Here, by combined Southern hybridization, polymerase chain reaction (PCR) assays and viral sequence analyses we provide evidence that DNA originating from two distinct double-stranded circular segments of the polydnavirus genome from the braconid Glyptapanteles indiensis (GiPDV) integrates in vitro into the genome of cells derived from the natural host, Lymantria dispar. The G. indiensis polydnavirus DNA, as a result of its unique ability to be integrated in part into the chromosome of cells derived from its lepidopteran host, has potential to be developed as an in vitro cell transformation system.

A prospective analysis of staging laparoscopy in patients with primary and secondary hepatobiliary malignancies.

Laparoscopy and laparoscopic ultrasound are used widely in cancer staging and are perceived to prevent unnecessary open exploration in many patients. The aim of this study was to analyze the impact of staging laparoscopy in improving resectability in patients with primary and secondary hepatobiliary malignancies. Over a 10-month period (November 1, 1997 to August 31, 1998), 186 patients with primary and secondary hepatobiliary cancers were submitted to operation for potentially curative resection. One hundred four patients staged laparoscopically (LAP) before laparotomy were compared prospectively to 82 patients undergoing exploration without laparoscopy (NO LAP). Assignment to each group was not random but was based on surgeon practice. Demographic data, diagnoses, the extent of preoperative evaluation, and the percentage of patients resected were similar in the two groups. Laparoscopy identified 26 (67%) of 39 patients with unresectable disease. In the NO LAP group, 28 patients (34%) had unresectable disease discovered at laparotomy. In patients with unresectable disease and submitted to biopsy only, the operating times were similar in the two groups (LAP 83 +/- 22 minutes vs. NO LAP 91 +/- 33 minutes; P = 0.4). However, laparoscopic staging significantly reduced the length of hospital stay (LAP 2.2 +/- 2 days vs. NO LAP 8.5 +/- 8.6 days; P = 0.006). Likewise, total hospital charges, normalized to 100 in the NO LAP patients, were significantly lower in the LAP group (LAP 54 +/- 42 vs. NO LAP 100 +/- 84; P = 0.02). Staging laparoscopy identified the majority of patients with unresectable hepatobiliary malignancies, significantly improved resectability, and reduced the number of days in the hospital and the total charges. The yield of laparoscopy was greatest for detecting peritoneal metastases (9 of 10), additional hepatic tumors (10 of 12), and unsuspected advanced cirrhosis (5 of 5) but often failed to identify nonresectability because of lymph node metastases, vascular involvement, or extensive biliary involvement. Eighty-three percent of patients subjected to laparotomy after laparoscopy underwent a potentially curative resection compared to 66% of those who were not staged laparoscopically.