bluuebunny/crossref_metadata_2025_split huggingface dataset:
Imatinib as a potential antiresorptive therapy for bone disease
Authors: Dewar Andrea L. , Farrugia Amanda N. , Condina Mark R. , Bik To L. , Hughes Timothy P. , Vernon-Roberts Barrie , Zannettino Andrew C. W. 
Published: February 2006

Abstract
Osteoclasts (OCs) are large multinucleated cells derived from progenitor cells of the monocyte-macrophage lineage. Signal transduction via the macrophage–colony-stimulating factor (M-CSF) receptor, c-fms, is essential for OC formation. Since we have previously demonstrated inhibition of c-fms by imatinib, we examined the effect of imatinib on OC formation and activity. OC formation was not affected by concentrations of 1.0 μM imatinib and lower, but was reduced by 75% at 3.0 μM imatinib. In contrast, both the area of resorption and the number of resorption lacunae were reduced by 80% at 0.3 μM imatinib, and no resorption was observed at concentrations above 3.0 μM. A dose-dependent decrease in receptor activator of nuclear factor κB (RANK) expression was observed in OCs when cultured in the presence of imatinib, providing a mechanism for the decrease in OC function. In vivo analysis of the effect of imatinib on OC activity in adult mice following 8 weeks of imatinib treatment also demonstrated a decrease in OC activity. These results suggest that imatinib may have therapeutic value as an antiosteolytic agent in diseases such as osteoporosis, metastatic bone disease, and multiple myeloma.
Determinants of mortality for adults with cystic fibrosis admitted in Intensive Care Unit: a multicenter study
Authors: Texereau Joëlle , Jamal Dany , Choukroun Gérald , Burgel Pierre-Régis , Diehl Jean-Luc , Rabbat Antoine , Loirat Philippe , Parrot Antoine , Duguet Alexandre , Coste Joël , Dusser Daniel , Hubert Dominique , Mira Jean-Paul 
Published: February 2006

Abstract

Background
Intensive care unit (ICU) admission of adults with cystic fibrosis (CF) is controversial because of poor outcome. This appraisal needs re-evaluation following recent changes in both CF management and ICU daily practice. Objectives were to determine long-term outcome of adults with CF admitted in ICU and to identify prognostic factors.

Methods
Retrospective multicenter study of 60 ICU hospitalizations for 42 adult CF patients admitted between 2000 and 2003. Reason for ICU admission, ventilatory support provided and one-year survival were recorded. Multiple logistic analysis was used to determine predictors of mortality.

Results
Prior to ICU admission, all patients (mean age 28.1 ± 8 yr) had a severe lung disease (mean FEV1 28 ± 12% predicted; mean PaCO2 47 ± 9 mmHg). Main reason for ICU hospitalization was pulmonary infective exacerbation (40/60). At admission, noninvasive ventilation was used in 57% of cases and was successful in 67% of patients. Endotracheal intubation was implemented in 19 episodes. Overall ICU mortality rate was 14%. One year after ICU discharge, 10 of the 28 survivors have been lung transplanted. Among recognized markers of CF disease severity, only the annual FEV1 loss was associated with a poor outcome (HR = 1.47 [1.18–1.85], p = 0.001). SAPSII (HR = 1.08 [1.03–1.12], p < 0.001) and endotracheal intubation (HR = 16.60 [4.35–63.34], p < 0.001) were identified as strong independent predictors of mortality.

Conclusion
Despite advanced lung disease, adult patients with CF admitted in ICU have high survival rate. Endotracheal intubation is associated with a poor prognosis and should be used as the last alternative. Although efforts have to be made in selecting patients with CF likely to benefit from ICU resources, ICU admission of these patients should be considered.
Differential regulation of neurotrophin expression in human bronchial smooth muscle cells
Authors: Kemi Cecilia , Grunewald Johan , Eklund Anders , Olgart Höglund Caroline 
Published: February 2006

AbstractBackgroundHuman bronchial smooth muscle cells (HBSMC) may regulate airway inflammation by secreting cytokines, chemokines and growth factors. The neurotrophins, including nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3), have been shown to be elevated during airway inflammation and evoke airway hyperresponsiveness. We studied if HBSMC may be a source of NGF, BDNF and NT-3, and if so, how inflammatory cytokines may influence their production.MethodsBasal and cytokine (IL-1β, IFN-γ, IL-4)-stimulated neurotrophin expression in HBSMC culturedin vitrowas quantified. The mRNA expression was quantified by real-time RT-PCR and the protein secretion into the cell culture medium by ELISA.ResultsWe observed a constitutive NGF, BDNF and NT-3 expression. IL-1β stimulated a transient increase of NGF, while the increase of BDNF had a later onset and was more sustained. COX-inhibitors (indomethacin and NS-398) markedly decreased IL-1β-stimulated secretion of BDNF, but not IL-1β-stimulated NGF secretion. IFN-γ increased NGF expression, down-regulated BDNF expression and synergistically enhanced IL-1β-stimulated NGF expression. In contrast, IL-4 had no effect on basal NGF and BDNF expression, but decreased IL-1β-stimulated NGF expression. NT-3 was not altered by the tested cytokines.ConclusionTaken together, our data indicate that, in addition to the contractile capacity, HBSMC can express NGF, BDNF and NT-3. The expression of these neurotrophins may be differently regulated by inflammatory cytokines, suggesting a dynamic interplay that might have a potential role in airway inflammation.
GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease
Authors: Motsinger Alison A , Lee Stephen L , Mellick George , Ritchie Marylyn D 
Published: February 2006

AbstractBackgroundThe identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.ResultsWe show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (<1%) or when interactions involved more than three loci. We tested GPNN on a real dataset comprised of Parkinson's disease cases and controls and found a two locus interaction between theDLSTgene and sex.ConclusionThese results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.
Protein structure similarity from principle component correlation analysis
Authors: Zhou Xiaobo , Chou James , Wong Stephen TC 
Published: February 2006

Abstract

Background
Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.

Results
We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins.

Conclusion
The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison.
An improved distance measure between the expression profiles linking co-expression and co-regulation in mouse
Authors: Kim Ryung S , Ji Hongkai , Wong Wing H 
Published: February 2006

Abstract

Background
Many statistical algorithms combine microarray expression data and genome sequence data to identify transcription factor binding motifs in the low eukaryotic genomes. Finding cis-regulatory elements in higher eukaryote genomes, however, remains a challenge, as searching in the promoter regions of genes with similar expression patterns often fails. The difficulty is partially attributable to the poor performance of the similarity measures for comparing expression profiles. The widely accepted measures are inadequate for distinguishing genes transcribed from distinct regulatory mechanisms in the complicated genomes of higher eukaryotes.

Results
By defining the regulatory similarity between a gene pair as the number of common known transcription factor binding motifs in the promoter regions, we compared the performance of several expression distance measures on seven mouse expression data sets. We propose a new distance measure that accounts for both the linear trends and fold-changes of expression across the samples.

Conclusion
The study reveals that the proposed distance measure for comparing expression profiles enables us to identify genes with large number of common regulatory elements because it reflects the inherent regulatory information better than widely accepted distance measures such as the Pearson's correlation or cosine correlation with or without log transformation.
On single and multiple models of protein families for the detection of remote sequence relationships
Authors: Casbon James A , Saqi Mansoor AS 
Published: February 2006

Abstract

Background
The detection of relationships between a protein sequence of unknown function and a sequence whose function has been characterised enables the transfer of functional annotation. However in many cases these relationships can not be identified easily from direct comparison of the two sequences. Methods which compare sequence profiles have been shown to improve the detection of these remote sequence relationships. However, the best method for building a profile of a known set of sequences has not been established. Here we examine how the type of profile built affects its performance, both in detecting remote homologs and in the resulting alignment accuracy. In particular, we consider whether it is better to model a protein superfamily using a single structure-based alignment that is representative of all known cases of the superfamily, or to use multiple sequence-based profiles each representing an individual member of the superfamily.

Results
Using profile-profile methods for remote homolog detection we benchmark the performance of single structure-based superfamily models and multiple domain models. On average, over all superfamilies, using a truncated receiver operator characteristic (ROC
5) we find that multiple domain models outperform single superfamily models, except at low error rates where the two models behave in a similar way. However there is a wide range of performance depending on the superfamily. For 12% of all superfamilies the ROC
5 value for superfamily models is greater than 0.2 above the domain models and for 10% of superfamilies the domain models show a similar improvement in performance over the superfamily models.

Conclusion
Using a sensitive profile-profile method we have investigated the performance of single structure-based models and multiple sequence models (domain models) in detecting remote superfamily members. We find that overall, multiple models perform better in recognition although single structure-based models display better alignment accuracy.
Effects of filtering by Present call on analysis of microarray experiments
Authors: McClintick Jeanette N , Edenberg Howard J 
Published: February 2006

AbstractBackgroundAffymetrix GeneChips®are widely used for expression profiling of tens of thousands of genes. The large number of comparisons can lead to false positives. Various methods have been used to reduce false positives, but they have rarely been compared or quantitatively evaluated. Here we describe and evaluate a simple method that uses the detection (Present/Absent) call generated by the Affymetrix microarray suite version 5 software (MAS5) to remove data that is not reliably detected before further analysis, and compare this with filtering by expression level. We explore the effects of various thresholds for removing data in experiments of different size (from 3 to 10 arrays per treatment), as well as their relative power to detect significant differences in expression.ResultsOur approach sets a threshold for the fraction of arrays called Present in at least one treatment group. This method removes a large percentage of probe sets called Absent before carrying out the comparisons, while retaining most of the probe sets called Present. It preferentially retains the more significant probe sets (p ≤ 0.001) and those probe sets that are turned on or off, and improves the false discovery rate. Permutations to estimate false positives indicate that probe sets removed by the filter contribute a disproportionate number of false positives. Filtering by fraction Present is effective when applied to data generated either by the MAS5 algorithm or by other probe-level algorithms, for example RMA (robust multichip average). Experiment size greatly affects the ability to reproducibly detect significant differences, and also impacts the effect of filtering; smaller experiments (3–5 samples per treatment group) benefit from more restrictive filtering (≥50% Present).ConclusionUse of a threshold fraction of Present detection calls (derived by MAS5) provided a simple method that effectively eliminated from analysis probe sets that are unlikely to be reliable while preserving the most significant probe sets and those turned on or off; it thereby increased the ratio of true positives to false positives.
Assessing stability of gene selection in microarray data analysis
Authors: Qiu Xing , Xiao Yuanhui , Gordon Alexander , Yakovlev Andrei 
Published: February 2006

AbstractBackgroundThe number of genes declared differentially expressed is a random variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess stability and some other properties of several gene selection procedures with biological and simulated data.ResultsUsing resampling techniques we have found that some genes are selected much less frequently (across sub-samples) than other genes with the same adjustedp-values. The extent to which this type of instability manifests itself can be assessed by a method introduced in this paper. The effect of correlation between gene expression levels on the performance of multiple testing procedures is studied by computer simulations.ConclusionResampling represents a tool for reducing the set of initially selected genes to those with a sufficiently high selection frequency. Using resampling techniques it is also possible to assess variability of different performance indicators. Stability properties of several multiple testing procedures are described at length in the present paper.
Effect of genotyping error in model-free linkage analysis using microsatellite or single-nucleotide polymorphism marker maps
Authors: Thompson Cheryl L , Baechle Dan , Lu Qing , Mathew George , Song Yeunjoo , Iyengar Sudha K , Gray-McGuire Courtney , Goddard Katrina AB 
Published: February 2006

Abstract
Errors while genotyping are inevitable and can reduce the power to detect linkage. However, does genotyping error have the same impact on linkage results for single-nucleotide polymorphism (SNP) and microsatellite (MS) marker maps? To evaluate this question we detected genotyping errors that are consistent with Mendelian inheritance using large changes in multipoint identity-by-descent sharing in neighboring markers. Only a small fraction of Mendelian consistent errors were detectable (e.g., 18% of MS and 2.4% of SNP genotyping errors). More SNP genotyping errors are Mendelian consistent compared to MS genotyping errors, so genotyping error may have a greater impact on linkage results using SNP marker maps. We also evaluated the effect of genotyping error on the power and type I error rate using simulated nuclear families with missing parents under 0, 0.14, and 2.8% genotyping error rates. In the presence of genotyping error, we found that the power to detect a true linkage signal was greater for SNP (75%) than MS (67%) marker maps, although there were also slightly more false-positive signals using SNP marker maps (5 compared with 3 for MS). Finally, we evaluated the usefulness of accounting for genotyping error in the SNP data using a likelihood-based approach, which restores some of the power that is lost when genotyping error is introduced.
Single-nucleotide polymorphism versus microsatellite markers in a combined linkage and segregation analysis of a quantitative trait
Authors: Daw E Warwick , Heath Simon C , Lu Yue 
Published: February 2006

Abstract
Increasingly, single-nucleotide polymorphism (SNP) markers are being used in preference to microsatellite markers. However, methods developed for microsatellites may be problematic when applied to SNP markers. We evaluated the results of using SNPs vs. microsatellites in Monte Carlo Markov chain (MCMC) oligogenic combined segregation and linkage analysis methods. These methods were developed with microsatellite markers in mind. We selected chromosome 7 from the Collaborative Study on the Genetics of Alcoholism dataset for analysis because linkage to an electrophysiological trait had been reported there. We found linkage in the same region of chromosome 7 with the Affymetrix SNP data, the Illumina SNP data, and the microsatellite marker data. The MCMC sampler appears to mix with both types of data. The sampler implemented in this MCMC oligogenic combined segregation and linkage analysis appears to handle SNP data as well as microsatellite data and it is possible that the localizations with the SNP data are better.
Sex and age specific effects of chromosomal regions linked to body mass index in the Framingham Study
Authors: Atwood Larry D , Heard-Costa Nancy L , Fox Caroline S , Jaquish Cashell E , Cupples L Adrienne 
Published: February 2006

Abstract

Background
Previously, we reported significant linkage of body mass index (BMI) to chromosomes 6 and 11 across six examinations, covering 28 years, of the Framingham Heart Study. These results were on all individuals available at each exam, thus the sample size varied from exam to exam. To remove any effect of sample size variation we have now constructed six subsets; for each exam individuals were only included if they were measured at every exam, i.e. for each exam, included individuals comprise the intersection of the original six exams. This strategy preferentially removed older individuals who died before reaching the sixth exam, thus the intersection datasets are smaller (n = 1114) and significantly younger than the full datasets. We performed variance components linkage analysis on these intersection datasets and on their sex-specific subsets.

Results
Results from the sex-specific genome scans revealed 11 regions in which a sex-specific maximum lodscore was at least 2.0 for at least one dataset. Randomization tests indicated that all 11 regions had significant (p < 0.05) differences in sex-specific maximum lodscores for at least three datasets. The strongest sex-specific linkage was for men on chromosome 16 with maximum lodscores 2.70, 3.00, 3.42, 3.61, 2.56 and 1.93 for datasets 1–6 respectively.
Results from the full genome scans revealed that linked regions on chromosomes 6 and 11 remained significantly and consistently linked in the intersection datasets. Surprisingly, the maximum lodscore on chromosome 10 for dataset 1 increased from 0.97 in the older original dataset to 4.23 in the younger smaller intersection dataset. This difference in maximum lodscores was highly significant (p < 0.0001), implying that the effect of this chromosome may vary with age. Age effects may also exist for the linked regions on chromosomes 6 and 11.

Conclusion
Sex specific effects of chromosomal regions on BMI are common in the Framingham study. Some evidence also exists for age-specific effects of chromosomal regions.
BLAST screening of chlamydial genomes to identify signature proteins that are unique for the Chlamydiales, Chlamydiaceae, Chlamydophila and Chlamydia groups of species
Authors: Griffiths Emma , Ventresca Michael S , Gupta Radhey S 
Published: February 2006

Abstract

Background
Chlamydiae species are of much importance from a clinical viewpoint. Their diversity both in terms of their numbers as well as clinical involvement are presently believed to be significantly underestimated. The obligate intracellular nature of chlamydiae has also limited their genetic and biochemical studies. Thus, it is of importance to develop additional means for their identification and characterization.

Results
We have carried out analyses of available chlamydiae genomes to identify sets of unique proteins that are either specific for all Chlamydiales genomes, or different Chlamydiaceae family members, or members of the Chlamydia and Chlamydophila genera, or those unique to Protochlamydia amoebophila, but which are not found in any other bacteria. In total, 59 Chlamydiales-specific proteins, 79 Chlamydiaceae-specific proteins, 20 proteins each that are specific for both Chlamydia and Chlamydophila and 445 ORFs that are Protochlamydia-specific were identified. Additionally, 33 cases of possible gene loss or lateral gene transfer were also detected.

Conclusion
The identified chlamydiae-lineage specific proteins, many of which are highly conserved, provide novel biomarkers that should prove of much value in the diagnosis of these bacteria and in exploration of their prevalence and diversity. These conserved protein sequences (CPSs) also provide novel therapeutic targets for drugs that are specific for these bacteria. Lastly, functional studies on these chlamydiae or chlamydiae subgroup-specific proteins should lead to important insights into lineage-specific adaptations with regards to development, infectivity and pathogenicity.