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At least 1,279 records · Page 71Linked to original sources
PubMed1998

An overview of the effect of computer-assisted management of anticoagulant therapy on the quality of anticoagulation.

Risks and benefits of anticoagulant therapy depend directly of the quality of anticoagulation. We carried out a meta-analysis of published randomized trials to assess the overall effectiveness of computer-assisted prescription systems on the quality of anticoagulation. Randomized controlled trials were identified through electronic searches of the Medline database (1966-1997) and systematic analyses of the references of articles. Two investigators selected relevant papers and summarized data from the studies. The outcome variable was the proportion of days within the target range of anticoagulation. A pooled estimate of the common odds ratio of being in the target range and its confidence interval was obtained by the Mantel-Haenszel method. Nine trials having included 1336 patients were identified. Computer systems were based on a pharmacokinetic-pharmacodynamic model and a bayesian prediction method. Most of them concerned the oral anticoagulant warfarin. The global odds ratio of being in the target range was 1.29 [95% CI: 1.17-1.49], thus meaning that the use of a computer for anticoagulation optimization increased by 29% the proportion of visits where patients were appropriately treated. The proportion of clinical events was too low for allowing a summary analysis, but major hemorrhages tended to be less frequent among patients of the computer groups than among patients of the control groups (2.0 versus 3.9%). Evidence from randomized controlled trials supports the effectiveness of computer-aided anticoagulant prescription. Widespread use of these systems in ambulatory care could increase the benefit/risk ratio of anticoagulant treatment at a low cost.

Anticoagulants↗
PubMed1998

Adjusting AIDS incidence for non-stationary reporting delays: a necessity for country comparisons.

In many industrialized countries, infection with the human immunodeficiency virus (HIV) is one of the leading causes of mortality in adult persons below age 45. The incidence of the acquired immunodeficiency syndrome (AIDS) from surveillance systems is the most common indicator to compare the situation of the HIV-epidemic in different geographic regions or countries. Due to reporting delays, AIDS diagnoses in recent years are incompletely reported and need to be estimated. In this study, we analyze reporting delays in Switzerland and Spain for the period from 1988 to mid-1995 and estimate the number of AIDS diagnoses per year. A descriptive analysis for Switzerland shows increasing reporting delays in recent years. Then, a Bayesian generalized linear model on reverse-time hazards is used to model time trends of the reporting delay distribution. The model shows that in recent years (i) for Switzerland reporting delays became longer and yearly AIDS incidence might continue to increase, and (ii) for Spain, reporting delays became considerably shorter resulting in too large estimates of yearly AIDS incidence if stationarity of reporting delays is assumed. Critical issues of modeling non-stationarity of the reporting system are discussed and it is emphasized that estimates of recent AIDS incidence can be biased significantly if time trends of reporting are ignored-as in the example of Switzerland and Spain, this may severely distort comparisons of the AIDS epidemic in different countries.

Acquired Immunodeficiency Syndrome↗
PubMedDate not supplied

Spatiotemporal analysis of environmental exposure-health effect associations.

The goal of this work is to discuss a general methodology for studying associations between environmental exposures and health effect by means of the spatiotemporal random field theory. This theory is the tool of choice for rigorously accounting for important spatiotemporal variations and uncertainties related to exposures and effect. Within the framework of the random field theory, the Bayesian maximum entropy model neatly synthesizes various sources of physical and epidemiological knowledge into spatiotemporal analysis. Therefore, unlike technical statistics, this approach relies on the blending of substantive physical knowledge with powerful mathematical techniques and a coherent rationale. Given the well-founded fact that certain health effects may be caused by environmental exposures, the significance of these exposures is assessed in terms of a criterion that is based on the joint stochastic representation of exposure and health-effect distributions in space/time. In view of this criterion, the strength and consistency of the exposure-effect association are evaluated on the basis of the health-effect predictions that the combined physico-epidemiologic analysis generates in space/time. The main features of the approach are demonstrated by a simulation example and a real case study involving mortality and cold temperatures in North Carolina. The studies demonstrated the practical usefulness of the stochastic human exposure analysis in assessing the exposure-effect association. The results reported here emphasize the links between spatiotemporal models of physical systems and population health-effect distributions, thus suggesting directions for improving the current understanding of quantitative "exposure-health effect" functions.

Cold Temperature↗
PubMed2003

Supervised classification for gene network reconstruction.

One of the central problems of functional genomics is revealing gene expression networks - the relationships between genes that reflect observations of how the expression level of each gene affects those of others. Microarray data are currently a major source of information about the interplay of biochemical network participants in living cells. Various mathematical techniques, such as differential equations, Bayesian and Boolean models and several statistical methods, have been applied to expression data in attempts to extract the underlying knowledge. Unsupervised clustering methods are often considered as the necessary first step in visualization and analysis of the expression data. As for supervised classification, the problem mainly addressed so far has been how to find discriminative genes separating various samples or experimental conditions. Numerous methods have been applied to identify genes that help to predict treatment outcome or to confirm a diagnosis, as well as to identify primary elements of gene regulatory circuits. However, less attention has been devoted to using supervised learning to uncover relationships between genes and/or their products. To start filling this gap a machine-learning approach for gene networks reconstruction is described here. This approach is based on building classifiers--functions, which determine the state of a gene's transcription machinery through expression levels of other genes. The method can be applied to various cases where relationships between gene expression levels could be expected.

Genes↗
PubMed2006

Continued evolution in gp41 after interruption of enfuvirtide in subjects with advanced HIV type 1 disease.

Careful examination of viral dynamics during antiretroviral treatment can provide important insights into HIV pathogenesis. We examined viral evolution during and after enfuvirtide (T-20) treatment with an objective of defining the characteristics of viral evolution during advanced HIV immunodeficiency. Specifically, we examined the viral quasispecies from nine patients who experienced incomplete viral suppression on an enfuvirtide-based regimen, and who subsequently interrupted enfuvirtide while remaining on a stable optimized background regimen (enfuvirtide "partial treatment interruption"). On average, eight clones were sequenced from three time points: pre-enfuvirtide, post-enfuvirtide failure, and post-enfuvirtide interruption. We utilized a Bayesian hierarchical phylogenetic model to assess the evolutionary relatedness of the virus that emerged over time. In most subjects, interruption of enfuvirtide was associated with continued viral evolution as enfuvirtide mutations waned; in no subject did we find clear evidence supporting the reemergence of archived wild-type variants (Bayes factor=30.2, p=0.01). Evidence supporting ongoing viral evolution was particularly strong in subjects whose virus remained diverse or became more diverse during enfuvirtide therapy. In contrast to observations when all drugs are interrupted, loss of resistance during enfuvirtide interruption is most likely due to ongoing viral evolution (and back-mutation), rather than emergence of an archived virus.

Bayes Theorem↗
PubMed2025

BISON: bi-clustering of spatial omics data with feature selection.

MOTIVATION: The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes (DGs) that elucidate the clustering result, referred to as spatial domain-specific DGs. Existing methods for identifying these genes typically rely on a two-stage approach, which can lead to the phenomenon known as double-dipping. RESULTS: To address the challenge, we propose a unified Bayesian latent block model that simultaneously detects a list of DGs contributing to spatial domain identification while clustering these DGs and spatial locations. The efficacy of our proposed method is validated through a series of simulation experiments, and its capability to identify DGs is demonstrated through applications to benchmark SRT datasets. AVAILABILITY AND IMPLEMENTATION: The R/C++ implementation of BISON is available at https://github.com/new-zbc/BISON.

Software↗
PubMed2004

Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data.

MOTIVATION: Sigma factors regulate the expression of genes in Bacillus subtilis at the transcriptional level. We assess the accuracy of a fold-change analysis, Bayesian networks, dynamic models and supervised learning based on coregulation in predicting gene regulation by sigma factors from gene expression data. To improve the prediction accuracy, we combine sequence information with expression data by adding their log-likelihood scores and by using a logistic regression model. We use the resulting score function to discover currently unknown gene regulations by sigma factors. RESULTS: The coregulation-based supervised learning method gave the most accurate prediction of sigma factors from expression data. We found that the logistic regression model effectively combines expression data with sequence information. In a genome-wide search, highly significant logistic regression scores were found for several genes whose transcriptional regulation is currently unknown. We provide the corresponding RNA polymerase binding sites to enable a straightforward experimental verification of these predictions.

Algorithms↗
PubMed2005

Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes.

MOTIVATION: We present a sequence-based framework and algorithm PHYLOCLUS for predicting co-regulated genes. In our approach, de novo discovery methods are used to find motifs conserved by evolution and then a Bayesian hierarchical clustering model is used to cluster these motifs, thereby grouping together genes that are putatively co-regulated. Our clustering procedure allows both the number of clusters and the motif width within each cluster to be unknown. RESULTS: We use our framework to predict co-regulated genes in the bacterium Bacillus subtilis using six other closely related bacterial species. Our predicted motifs and gene clusters are validated using several external sources and significant clusters are examined in detail. An extension to the discovery and clustering of two-block motifs can be used for inference about synergistic binding relationships between transcription factors. AVAILABILITY: Software and Supplementary Materials can be downloaded at http://stat.wharton.upenn.edu/~stjensen/research/phyloclus.html or http://www.fas.harvard.edu/~junliu/phyloclus.html CONTACT: stjensen@wharton.upenn.edu.

Algorithms↗
PubMed2005

Bayesian sparse hidden components analysis for transcription regulation networks.

MOTIVATION: In systems like Escherichia Coli, the abundance of sequence information, gene expression array studies and small scale experiments allows one to reconstruct the regulatory network and to quantify the effects of transcription factors on gene expression. However, this goal can only be achieved if all information sources are used in concert. RESULTS: Our method integrates literature information, DNA sequences and expression arrays. A set of relevant transcription factors is defined on the basis of literature. Sequence data are used to identify potential target genes and the results are used to define a prior distribution on the topology of the regulatory network. A Bayesian hidden component model for the expression array data allows us to identify which of the potential binding sites are actually used by the regulatory proteins in the studied cell conditions, the strength of their control, and their activation profile in a series of experiments. We apply our methodology to 35 expression studies in E.Coli with convincing results. AVAILABILITY: www.genetics.ucla.edu/labs/sabatti/software.html SUPPLEMENTARY INFORMATION: The supplementary material are available at Bioinformatics online.

Algorithms↗
PubMed2001

Bayesian discrimination with longitudinal data.

The motivation for the methodological development is a double-blind clinical trial designed to estimate the effect of regular injection of growth hormone, with the purpose of identifying growth hormone abusers in sport. The data formed part of a multicentre investigation jointly sponsored by the European Union and the International Olympic Committee. The data are such that for each individual there is a matrix of marker variables by time point (nominally 8 markers at each of 7 time points). Data arise out of a double-blind trial in which individuals are given growth hormone at one of two dose levels or placebo daily for 28 days. Monitoring by means of blood samples is at 0, 21, 28, 30, 33, 42 and 84 days. We give a new method of Bayesian discrimination for multivariate longitudinal data. This involves a Kronecker product covariance structure for the time by measurements (markers) data on each individual. This structure is estimated by an empirical Bayes approach, using an ECM algorithm, within a Bayesian Gaussian discrimination model. In future one may have markers for an individual at one or more time points. The method gives probabilities that an individual is on placebo or on one of the two dose regimes.

Journal Article↗
PubMed2002

Statistical analysis of temporal evolution in single-neuron firing rates.

A fundamental methodology in neurophysiology involves recording the electrical signals associated with individual neurons within brains of awake behaving animals. Traditional statistical analyses have relied mainly on mean firing rates over some epoch (often several hundred milliseconds) that are compared across experimental conditions by analysis of variance. Often, however, the time course of the neuronal firing patterns is of interest, and a more refined procedure can produce substantial additional information. In this paper we compare neuronal firing in the supplementary eye field of a macaque monkey across two experimental conditions. We take the electrical discharges, or 'spikes', to be arrivals in a inhomogeneous Poisson process and then model the firing intensity function using both a simple parametric form and more flexible splines. Our main interest is in making inferences about certain characteristics of the intensity, including the timing of the maximal firing rate. We examine data from 84 neurons individually and also combine results into a hierarchical model. We use Bayesian estimation methods and frequentist significance tests based on a nonparametric bootstrap procedure. We are thereby able to conclude that a substantial fraction of the neurons exhibit important temporal differences in firing intensity across the two conditions, and we quantify the effect across the population of neurons.

Journal Article↗
PubMed1997

Spatial and temporal variation in abundance of Anopheles (Diptera:Culicidae) in a malaria endemic area in Papua New Guinea.

Abundance of anophelines in 10 villages in the Wosera area of Papua New Guinea was monitored during 1990-1993. Of 85,197 anophelines collected in 1,276 paired indoor and outdoor landing catches, 40.4% were Anopheles koliensis Owen, 36.7% An. punctulatus Donitz, 14.3% An. karwari (James), 4.9% An. farauti s.l. Laveran, 3.1%, An, longirostris Brug, and 0.7% An. bancroftii Giles. Maps of average indoor biting rates were produced using a Bayesian conditional autoregressive model which allowed for heterogeneities in sampling effort over time and space. Differences in spatial distributions among species were observed among and within villages and were related to the distribution of larval habitats and vegetation. Abundance of An. punctulatus and An. koliensis decreased with distance from the main waterway and probably from a sago swamp forest at 6 villages in North Wosera. Abundance of An. punctulatus was associated negatively with those of An. farauti s.l., An. longirostris, and An. bancroftii. The latter 3 species also had relatively low ratios of indoor-to-outdoor biting rates, and earlier biting times than An. punctulatus. Human blood indices of at least 0.79 were observed for all species except An. bancroftii. Abundance of all 6 species was correlated temporally with recent rainfall, but An. koliensis, An. karwari, and An. longirostris showed greater temporal variability than the other species. An punctulatus and An. koliensis tended to occur together in time and space (index of association, I = 0.85). Weaker associations were seen between An. farauti s.l. and An. longirostris (I = 0.44) and An. koliensis and An. karwari (I = 0.34). The most frequently collected species occurred together and were concentrated near the Amugu river; the remaining species tended to occur together but in different parts of the Wosera area. The importance of understanding ecological requirements of the different Anopheles vectors and their association with key household and landscape features are discussed in relation to malaria transmission and control.

Animals↗
PubMed2004

Population pharmacokinetics of ethanol in drinking drivers using breath measures.

This study was undertaken to evaluate the population pharmacokinetic behavior of ethanol from breath ethanol measures and to see if these results could be used to establish the drinking history of our drinking drivers. The population consisted of 55 self-identified light to heavy drinkers. All had been arrested at least once for driving under the influence of alcohol. Sixteen were women, and 39 were men. Breath was analyzed for ethanol using a 3-wavelength infrared spectrophotometer. An iterative 2-stage Bayesian (IT2B) parametric modeling program was used first to obtain gamma, a measure of the relative magnitude of the intraindividual variability. The nonparametric adaptive grid (NPAG) maximum likelihood program, using gamma, was then used to obtain the full nonparametric joint parameter density. A 2-compartment Michaelis-Menten model was evaluated. The 2-compartment model gave a gamma of 1.75. Thus, the standard deviation (SD) of the nonassay sources of intraindividual variability was 1.75 times the SD of the assay itself for the 2-compartment model. The NPAG program gave the following means, medians, modes, and standard deviations for the 2-compartment model: ka (h(-1)) = 6.43, 5.46, 2.93, 4.58; Vmax (g/h) = 12.09, 11.90, 13.03, 3.73; Km (g/L) = 0.1273, 0.1367, 0.1991, 0.0528; Vc (L) = 31.32, 29.30, 24.88, 10.52; kcp (h(-1)) = 4.38, 1.30, 1.12, 6.16; and kpc (h(-1)) = 9.11, 2.47, 0.89, 8.98. These drinking drivers had a rate of metabolism of ethanol that was between that of moderate drinkers and confirmed alcoholics. Properly collected breath ethanol measures can be useful in a therapeutic drug-monitoring situation to obtain quick, accurate, and reliable measures of a patient's ethanol concentrations.

Adult↗
PubMed2006

Quetiapine in overdosage: a clinical and pharmacokinetic analysis of 14 cases.

Data on quetiapine overdosage are only sparsely available in the literature. This study provides additional data on the pharmacokinetics and clinical effects of intoxication with this atypical antipsychotic drug. The authors performed a retrospective analysis of all quetiapine intoxications reported to and screened by the toxicological laboratory of the Central Hospital Pharmacy The Hague between January 1999 and December 2003. Cases with known suggested amount of intake and medical outcome were included. From the patient's medical record and from the toxicological laboratory findings, patient demographic characteristics (gender, age), details of quetiapine intoxication (estimated time of ingestion, estimated amount of ingestion, and coingested drugs) and clinical parameters were obtained. Severity of intoxication was graded by the Poisoning Severity Score (PSS). Individual pharmacokinetic parameter values were calculated using a one-compartment open model and a Bayesian fitting procedure. Out of a total of 21 intoxications with quetiapine, 14 fulfilled the inclusion criteria. The ingested dose ranged from 1200 to 18,000 mg. The blood concentration ranged from 1.1 to 8.8 mg/L with a lag time of 1 to 26.2 hours between time of ingestion and blood sampling at the emergency ward. The most frequent findings were somnolence and tachycardia. The PSS was minor in 6 patients (43%), moderate in 5 patients (36%), and severe in 3 patients (21%). Severity of intoxication was not associated with a higher amount of quetiapine intake. The authors found no correlation between the serum concentration of quetiapine and the amount ingested. Elimination t(1/2) was not prolonged. It can be concluded that quetiapine intoxications appear to proceed mildly. Tachycardia and somnolence were the main clinical symptoms in our case series. No fatalities occurred. The severity of clinical symptoms was not associated with either a high serum concentration or the suggested amount ingested of quetiapine.

Administration, Oral↗
PubMed2000

Skeletal growth estimation using radiographic image processing and analysis.

An automated knowledge-based vision system for skeletal growth estimation in children is reported in this paper. Images were obtained from hand radiographs of 32 male and 25 female children of age 1-16 yr. Phalanx bones were automatically localized and segmented using hierarchical inferences and active shape models, respectively. A number of shape descriptors were obtained from the segmented bone contour to quantify skeletal growth. From these descriptors, a feature vector was selected for a regression model and a Bayesian estimator. The estimation accuracy was 84% for females and 82% for males. This level of accuracy is comparable to that of expert pediatric radiologists, which suggests that the proposed approach has a potential application in pediatric medicine.

Adolescent↗
PubMed2004

Bayesian support vector regression using a unified loss function.

In this paper, we use a unified loss function, called the soft insensitive loss function, for Bayesian support vector regression. We follow standard Gaussian processes for regression to set up the Bayesian framework, in which the unified loss function is used in the likelihood evaluation. Under this framework, the maximum a posteriori estimate of the function values corresponds to the solution of an extended support vector regression problem. The overall approach has the merits of support vector regression such as convex quadratic programming and sparsity in solution representation. It also has the advantages of Bayesian methods for model adaptation and error bars of its predictions. Experimental results on simulated and real-world data sets indicate that the approach works well even on large data sets.

Bayes Theorem↗
PubMed1999

Bayesian inference for prevalence in longitudinal two-phase studies.

We consider Bayesian inference and model selection for prevalence estimation using a longitudinal two-phase design in which subjects initially receive a low-cost screening test followed by an expensive diagnostic test conducted on several occasions. The change in the subject's diagnostic probability over time is described using four mixed-effects probit models in which the subject-specific effects are captured by latent variables. The computations are performed using Markov chain Monte Carlo methods. These models are then compared using the deviance information criterion. The methodology is illustrated with an analysis of alcohol and drug use in adolescents using data from the Great Smoky Mountains Study.

Adolescent↗
PubMed2001

Phylogeography of the New Zealand cicada Maoricicada campbelli based on mitochondrial DNA sequences: ancient clades associated with cenozoic environmental change.

New Zealand's isolation, its well-studied rapidly changing landscape, and its many examples of rampant speciation make it an excellent location for studying the process of genetic differentiation. Using 1520 base pairs of mitochondrial DNA from the cytochrome oxidase subunit I, ATPase subunits 6 and 8 and tRNA(Asp) genes, we detected two well-differentiated, parapatrically distributed clades within the widespread New Zealand cicada species Maoricicada campbelli that may prove to represent two species. The situation that we uncovered is unusual in that an ancient lineage with low genetic diversity is surrounded on three sides by two recently diverged lineages. Using a relaxed molecular clock model coupled with Bayesian statistics, we dated the earliest divergence within M. campbelli at 2.3 +/- 0.55 million years. Our data suggest that geological and climatological events of the late Pliocene divided a once-widespread species into northern and southern components and that near the middle of the Pleistocene the northern lineage began moving south eventually reaching the southern clade. The southern clade seems to have moved northward to only a limited extent. We discovered five potential zones of secondary contact through mountain passes that will be examined in future work. We predict that, as in North American periodical cicadas, contact between these highly differentiated lineages will exist but will not involve gene flow.

Adenosine Triphosphatases↗