951 resultados para P-matrix


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Adherence of uropathogenic Escherichia coli to host tissue is required for infection and is mediated by fimbriae, such as pyelonephritis-associated pili (Pap). Expression of P fimbriae is regulated by phase variation, and to date, phase transition frequencies have been measured only for pap regulatory region constructs integrated into the E. coli K-12 chromosome. The aim of this work was to measure P phase transition frequencies in clinical isolates for the first time, including frequencies for the sequenced strain E. coli CFT073. P fimbriation and associated phase transition frequencies were measured for two E. coli clinical isolates and compared with levels for homologous pap constructs in E. coli K-12. Fimbriation and off-to-on transition frequencies were always higher in the clinical isolate. It was concluded that the regulatory inputs controlling papI expression are likely to be different in E. coli CFT073 and E. coli K-12 as (i) phase variation could be stimulated in E. coli K-12 by induction of papI and (ii) the level of expression of a papI::gfp+ fusion was higher in E. coli CFT073 than in E. coli K-12. Furthermore, phase transition frequencies for the two E. coli CFT073 pap clusters were shown to be different depending on the culture conditions, indicating that there is a hierarchy of expression depending on signal inputs.

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The majority of Escherichia coli strains isolated from urinary tract infections have the potential to express multiple fimbriae. Two of the most common fimbrial adhesins are type 1 fimbriae and pyelonephritis-associated pili (Pap). Previous research has shown that induced, plasmid-based expression of a Pap regulator, papB, and its close homologues can prevent inversion of the fim switch controlling the expression of type 1 fimbriae. The aim of the present study was to determine if this cross-regulation occurs when PapB is expressed from its native promoter in the chromosome of E. coli K-12 and clinical isolates. The regulation was examined in three ways: (1) mutated alleles of the pap regulatory region, including papB and papI, that maintain the pap promoter in either the off or the on phase were exchanged into the chromosome of both E. coli K-12 and the clinical isolate E. coli CFT073, and the effect on type 1 fimbrial expression was measured; (2) type 1 fimbrial expression was determined using a novel fimS : : gfp+ reporter system in mutants of the clinical isolate E. coli 536 in which combinations of complete fimbrial clusters had been deleted; (3) type 1 fimbrial expression was determined in a range of clinical isolates and compared with both the number of P clusters and their expression. All three approaches demonstrated that P expression represses type 1 fimbrial expression. Using a number of novel genetic approaches, this work extends the initial finding that PapB inhibits FimB recombination to the impact of this regulation in clinical isolates.

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The importance of the isoform CYP2E1 of the human cytochrome P-450 superfamily of enzymes for occupational and environmental medicine is derived from its unique substrate spectrum that includes a number of highly important high-production chemicals, such as aliphatic and aromatic hydrocarbons, solvents and industrial monomers (i.a. alkanes, alkenes, aromatic and halogenated hydrocarbons). Many polymorphic genes, such as CYP2E1, show considerable differences in allelic distribution between different human populations. The polymorphic nature of the human CYP2E1 gene is significant for inter-individual differences in toxicity of its substrates. Since the substrate spectrum of CYP2E1 includes many compounds of basic relevance to industrial toxicology, a rationale for metabolic interactions of different CYP2E1 substrates is provided. In-depth research into the inter-individual phenotypic differences of human CYP2E1 enzyme activities was enabled by the recognition that the 6-hydroxylation of the drug chlorzoxazone is mediated by CYP2E1. Studies on CYP2E1 phenotyping have pointed to inter-individual variations in enzyme activities. There are consistent ethnic differences in CYP2E1 enzyme expression, mostly demonstrated between European and Japanese populations, which point to a major impact of genetic factors. The most frequently studied genetic polymorphisms are the restriction fragment length polymorphisms PstI/RsaI (mutant allele: CYP2E1*5B) located in the 5′-flanking region of the gene, as well as the DraI polymorphism (mutant allele: CYP2E1*6) located in intron 6. These polymorphisms are partly related, as they form the common allele designated CYP2E1*5A. Striking inter-ethnic differences between Europeans and Asians appear with respect to the frequencies of the CYP2E1*5A allele (only approximately 5% of Europeans are heterozygous, but 37% of Asians are, whilst 6% of Asians are homozygous). Available studies indicate a wide variation in human CYP2E1 expression, which are very likely based on complex gene-environment interactions. Major inter-ethnic differences are apparent on the genotyping and the phenotyping levels. Selected cases are presented where inter-ethnic variations of CYP2E1 may provide likely explanations for unexplained findings concerning industrial chemicals that are CYP2E1 substrates. Possible consequences of differential inter-individual and inter-ethnic susceptibilities are related to individual expressions of clinical symptoms of chemical toxicity, to results of biological monitoring of exposed workers, and to the interpretation of results of epidemiological or molecular-epidemiological studies.

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Dichloromethane (DCM) is thought to be metabolized in vivo by two independent pathways: a glutathione (GSH) dependent pathway that yields CO2 and a cytochrome P-450 mediated one that yields both CO and CO2 (Gargas et al 1986). With a physiologically based pharmacokinetic (PB-PK) model, Andersen et al (1987) calculate the quantitative parameters for both metabolic pathways. Using the kinetic parameters thus obtained and the results of two carcinogenicity studies with rodents (Serota et al 1986; NTP 1985), the authors then estimate the tumour risk for humans.

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The repair of bone defects that result from periodontal diseases remains a clinical challenge for periodontal therapy. β-tricalcium phosphate (β-TCP) ceramics are biodegradable inorganic bone substitutes with inorganic components that are similar to those of bone. Demineralized bone matrix (DBM) is an acid-extracted organic matrix derived from bone sources that consists of the collagen and matrix proteins of bone. A few studies have documented the effects of DBM on the proliferation and osteogenic differentiation of human periodontal ligament cells (hPDLCs). The aim of the present study was to investigate the effects of inorganic and organic elements of bone on the proliferation and osteogenic differentiation of hPDLCs using three-dimensional porous β-TCP ceramics and DBM with or without osteogenic inducers. Primary hPDLCs were isolated from human periodontal ligaments. The proliferation of the hPDLCs on the scaffolds in the growth culture medium was examined using a Cell‑Counting kit‑8 (CCK-8) and scanning electron microscopy (SEM). Alkaline phosphatase (ALP) activity and the osteogenic differentiation of the hPDLCs cultured on the β-TCP ceramics and DBM were examined in both the growth culture medium and osteogenic culture medium. Specific osteogenic differentiation markers were examined using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). SEM images revealed that the cells on the β-TCP were spindle-shaped and much more spread out compared with the cells on the DBM surfaces. There were no significant differences observed in cell proliferation between the β-TCP ceramics and the DBM scaffolds. Compared with the cells that were cultured on β-TCP ceramics, the ALP activity, as well as the Runx2 and osteocalcin (OCN) mRNA levels in the hPDLCs cultured on DBM were significantly enhanced both in the growth culture medium and the osteogenic culture medium. The organic elements of bone may exhibit greater osteogenic differentiation effects on hPDLCs than the inorganic elements.

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Soluble endoglin is an anti-angiogenic protein that is released from the placenta and contributes to both maternal endothelial dysfunction and the clinical features of severe preeclampsia. The mechanism through which soluble endoglin is released from the placenta is currently unknown; however, recent work in colorectal cancer identified matrix metalloproteinase 14 (MMP-14) as the cleavage protease of endoglin. To determine whether this is also the mechanism responsible for soluble endoglin release in preeclampsia, we investigated the expression of MMP-14 within the placenta and the effects of its inhibition on soluble endoglin release. Placentas were obtained from severe, early onset preeclamptic pregnancies (n = 8) and gestationally matched preterm controls (n = 8). MMP-14 was predominately localized to the syncytiotrophoblast. Results from a proximity ligation assay showed protein interactions between endogenous MMP-14 and endoglin within the preeclamptic placenta. To demonstrate that this interaction produces soluble endoglin, we treated trophoblastic BeWo cells with either a broad-spectrum MMP inhibitor (GM6001) or MMP-14 siRNA. Both treatments produced a decrease in soluble endoglin (P ≤ 0.05). Treatment of mice bearing BeWo xenografts with GM6001 decreased circulating soluble endoglin levels in mouse serum (P ≤ 0.05). These findings indicate that MMP-14 is the likely cleavage protease of endoglin in the setting of preeclampsia. This approach provides a novel method for the development of potential therapeutics to reduce circulating soluble endoglin and ameliorate the clinical features of severe preeclampsia.

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The aim of this study was to evaluate the ex vivo oestrogen responsiveness of human proliferative phase endometrium using short-term explant cultures. The effects of oestrogen (17beta-E2) on proliferation and the expression of oestrogen-responsive genes known to be involved in regulating endometrial function were evaluated. Three distinct response patterns could be distinguished: (1) the menstrual (M) phase pattern (cycle days 2-5), which is characterised by a complete lack in the proliferative response to 17beta-E2, while an increased expression of AR (2.6-fold, P<0.01), PR (2.7-fold, P<0.01) and COX-2 (3.5-fold, P<0.01) at the mRNA level was observed and a similar upregulation was also found for AR, PR and COX-2 at the protein level; (2) the early proliferative (EP) phase pattern (cycle days 6-10) with 17beta-E2 enhanced proliferation in the stroma (1.7-fold, P<0.05), whereas the expression of AR, PR and COX-2 were not affected at the mRNA and protein levels and ER-a mRNA and protein levels were significantly reduced by 17beta-E2; (3) the late proliferative (LP) phase pattern (cycle days 11-14), which is characterised by a moderate stimulation of proliferation (1.4-fold, P<0.05) and PR mRNA expression (1.7-fold, P<0.01) by 17beta-E2. In conclusion, three distinct response patterns to 17beta-E2 could be identified with respect to proliferation and the expression of known oestrogen-responsive genes in human proliferative phase endometrium explant cultures.

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To identify key regulatory mechanisms in the growth and development of the human endometrium, microarray analysis was performed on uncultured human endometrium collected during menstruation (M) and the late-proliferative (LATE-P)-phase of the menstrual cycle, as well as after 24 h incubation in the presence of oestradiol (17beta-E2). We demonstrate the expression of novel gene transcripts in the human endometrium. i.e. mucin-9, novel oestrogen-responsive gene transcripts, i.e. gelsolin and flotillin-1, and genes known to be expressed in human endometrium but not yet shown to be oestrogen responsive, i.e. connexin-37 and TFF1/pS2. Genes reported to be expressed during the implantation window and implicated in progesterone action, i.e. secretoglobin family 2A, member 2 (mammaglobin) and homeobox-containing proteins, were up-regulated in uncultured LATE-P-phase endometrium compared to M-phase endometrium. Some gene transcripts are regulated directly by 17beta-E2 alone, others are influenced by the in vivo environment as well. These observations emphasise that the regulation of endometrium maturation by oestrogen entails more then just stimulation of cell proliferation.

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Regeneration and growth of the human endometrium after shedding of the functional layer during menstruation depends on an adequate angiogenic response. We analysed the mRNA expression levels of all known vascular endothelial growth factor (VEGF) ligands and receptors in human endometrium collected in the menstrual and proliferative phases of the menstrual cycle. In addition, we evaluated the expression of VEGF-A, VEGF-R2 and NRP-1 at the protein level. Two periods of elevated mRNA expression of ligands and receptors were observed, separated by a distinct drop at cycle days (CDs) 9 and 10. Immunohistochemical staining showed that VEGF and VEGF-R2 were expressed in epithelial, stromal and endothelial cells. NRP-1 was mainly confined to stroma and blood vessels; only in late-proliferative endometrium, epithelial staining was also observed. Except for endothelial VEGF-R2 expression in CDs 6-8, there were no significant differences in the expression of VEGF, VEGF-R2 or NRP-1 in any of the cell compartments. In contrast, VEGF release by cultured human endometrium explants decreased during the proliferative phase. This output was significantly reduced in menstrual and early-proliferative endometrium by estradiol (E2) treatment. Western blot analysis indicated that part of the VEGF-A was trapped in the extracellular matrix (ECM). Changes in VEGF ligands and receptors were associated with elevated expression of the hypoxia markers HIF1 alpha and CA-IX in the menstrual and early proliferative phases. HIF1 alpha was also detected in late-proliferative phase endometrium. Our findings indicate that VEGF-A exerts its actions mostly during the first half of the proliferative phase. Furthermore, VEGF-A production appears to be triggered by hypoxia in the menstrual phase and subsequently suppressed toy estrogen during the late proliferative phase.

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Feminist research on girlhood has drawn extensively on Butler's conceptual work in order to theorise the normative forces of heterosexuality in the everyday construction of gender. This chapter explores girlhood by drawing on memories and artwork generated in a collective biography workshop held in Australian on the topic of girlhood and sexuality. We are interested in thinking through Butler's notion of the heterosexual matrix. Following Renold and Ringrose (2008) we do so with the help of Deleuze and Guattari, who invite us to think about difference as differenciation or continuous becoming, where difference is an evolutionary multiplicity.

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To identify specific markers of rectovaginal endometriotic nodule vasculature, highly enriched preparations of vascular endothelial cells and pericytes were obtained from endometriotic nodules and control endometrial and myometrial tissue by laser capture microdissection (LCM), and gene expression profiles were screened by microarray analysis. Of the 18 400 transcripts on the arrays, 734 were significantly overexpressed in vessels from fibromuscular tissue and 923 in vessels from stromal tissue of endometriotic nodules, compared with vessels dissected from control tissues. The most frequently expressed transcripts included known endothelial cell-associated genes, as well as transcripts with little or no previous association with vascular cells. The higher expression in blood vessels was further corroborated by immunohistochemical staining of six potential markers, five of which showed strong expression in pericytes. The most promising marker was matrix Gla protein, which was found to be present in both glandular epithelial cells and vascular endothelial cells of endometriotic lesions, although it was barely expressed at all in normal endometrium. LCM, combined with microarray analysis, constitutes a powerful tool for mapping the transcriptome of vascular cells. After immunohistochemical validation, markers of vascular endothelial and perivascular cells from endometriotic nodules could be identified, which may provide targets to improve early diagnosis or to selectively deliver therapeutic agents.

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Olfactomedin-4 (OLFM-4) is an extracellular matrix protein that is highly expressed in human endometrium. We have examined the regulation and function of OLFM-4 in normal endometrium and in cases of endometriosis and endometrial cancer. OLFM-4 expression levels are highest in proliferative-phase endometrium, and 17 beta-estradiol up-regulates OLFM-4 mRNA in endometrial explant cultures. Using the luciferase reporter under control of the OLFM-4 promoter, it was shown that both 17 beta-estradiol and OH-tamoxifen induce luciferase activity, and epidermal growth factor receptor-1 is required for this estrogenic response. In turn, EGF activates the OLFM-4 promoter, and estrogen receptor-alpha is needed for the complete EGF response. The cellular functions of OLFM-4 were examined by its expression in OLFM-4-negative HEK-293 cells, which resulted in decreased vimentin expression and cell adherence as well as increased apoptosis resistance. In cases of endometriosis and endometrial cancer, OLFM-4 expression correlated with the presence of epidermal growth factor receptor-1 and estrogen receptor-alpha (or estrogen signaling). An increase of OLFM-4 mRNA was observed in the endometrium of endometriosis patients. No change in OLFM-4 expression levels were observed in patients with endometrial cancer relative with controts. In conclusion, cross-talk between estrogen and EGF signaling regulates OLFM-4 expression. The role of OLFM-4 in endometrial tissue remodeling before the secretory phase and during the predisposition and early events in endometriosis can be postulated but requires additional investigation. (Am J Pathol 2010, 177:2495-2508: DOI: 10.2353/ajpath.2010.100026

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This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

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Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.