The chemopreventive polyphenol Curcumin prevents hematogenous breast cancer metastases in immunodeficient mice

Dissemination of metastatic cells probably occurs long before diagnosis of the primary tumor. Metastasis during early phases of carcinogenesis in high risk patients is therefore a potential prevention target. The plant polyphenol Curcumin has been proposed for dietary prevention of cancer. We therefore examined its effects on the human breast cancer cell line MDA-MB-231 in vitro and in a mouse metastasis model. Curcumin strongly induces apoptosis in MDA- MB- 231 cells in correlation with reduced activation of the survival pathway NF kappa B, as a consequence of diminished I kappa B and p65 phosphorylation. Curcumin also reduces the expression of major matrix metalloproteinases (MMPs) due to reduced NF kappa B activity and transcriptional downregulation of AP-1. NF kappa B/p65 silencing is sufficient to downregulate c-jun and MMP expression. Reduced NF kappa B/AP-1 activity and MMP expression lead to diminished invasion through a reconstituted basement membrane and to a significantly lower number of lung metastases in immunodeficient mice after intercardiac injection of 231 cells (p=0.0035). 68% of Curcumin treated but only 17% of untreated animals showed no or very few lung metastases, most likely as a consequence of down-regulation of NF kappa B/AP-1 dependent MMP expression and direct apoptotic effects on circulating tumor cells but not on established metastases. Dietary chemoprevention of metastases appears therefore feasible. Copyright (c) 2007 S. Karger AG, Basel

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

International audienceBackground: Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field

Risk of classic Kaposi sarcoma with exposures to plants and soils in Sicily

<p>Abstract</p> <p>Background</p> <p>Ecologic and in vitro studies suggest that exposures to plants or soil may influence risk of Kaposi sarcoma (KS).</p> <p>Methods</p> <p>In a population-based study of Sicily, we analyzed data on contact with 20 plants and residential exposure to 17 soils reported by 122 classic KS cases and 840 sex- and age-matched controls. With 88 KS-associated herpesvirus (KSHV) seropositive controls as the referent group, novel correlates of KS risk were sought, along with factors distinguishing seronegatives, in multinomial logistic regression models that included matching variables and known KS cofactors - smoking, cortisone use, and diabetes history. All plants were summed for cumulative exposure. Factor and cluster analyses were used to obtain scores and groups, respectively. Individual plants and soils in three levels of exposure with <it>P</it>_trend≤ 0.15 were retained in a backward elimination regression model.</p> <p>Results</p> <p>Adjusted for known cofactors, KS was not related to cumulative exposures to 20 plants [per quartile adjusted odds ratio (OR_adj) 0.96, 95% confidence interval (CI) 0.73 - 1.25, <it>P</it>_trend= 0.87], nor was it related to any factor scores or cluster of plants (<it>P </it>= 0.11 to 0.81). In the elimination regression model, KS risk was associated with five plants (<it>P</it>_trend= 0.02 to 0.10) and with residential exposure to six soils (<it>P</it>_trend= 0.01 to 0.13), including three soils (eutric regosol, chromic/pellic vertisol) used to cultivate durum wheat. None of the KS-associated plants and only one soil was also associated with KSHV serostatus. Diabetes was associated with KSHV seronegativity (OR_adj4.69, 95% CI 1.97 - 11.17), but the plant and soil associations had little effect on previous findings that KS risk was elevated for diabetics (OR_adj7.47, 95% CI 3.04 - 18.35) and lower for current and former smokers (OR_adj0.26 and 0.47, respectively, <it>P</it>_trend= 0.05).</p> <p>Conclusions</p> <p>KS risk was associated with exposure to a few plants and soils, but these may merely be due to chance. Study of the effects of durum wheat, which was previously associated with cKS, may be warranted.</p

A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents

<p>Abstract</p> <p>Background</p> <p>A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. The increasing volume of the scientific literature overwhelms health care professionals trying to be kept up-to-date with all published studies on DDI.</p> <p>Methods</p> <p>This paper describes a hybrid linguistic approach to DDI extraction that combines shallow parsing and syntactic simplification with pattern matching. Appositions and coordinate structures are interpreted based on shallow syntactic parsing provided by the UMLS MetaMap tool (MMTx). Subsequently, complex and compound sentences are broken down into clauses from which simple sentences are generated by a set of simplification rules. A pharmacist defined a set of domain-specific lexical patterns to capture the most common expressions of DDI in texts. These lexical patterns are matched with the generated sentences in order to extract DDIs.</p> <p>Results</p> <p>We have performed different experiments to analyze the performance of the different processes. The lexical patterns achieve a reasonable precision (67.30%), but very low recall (14.07%). The inclusion of appositions and coordinate structures helps to improve the recall (25.70%), however, precision is lower (48.69%). The detection of clauses does not improve the performance.</p> <p>Conclusions</p> <p>Information Extraction (IE) techniques can provide an interesting way of reducing the time spent by health care professionals on reviewing the literature. Nevertheless, no approach has been carried out to extract DDI from texts. To the best of our knowledge, this work proposes the first integral solution for the automatic extraction of DDI from biomedical texts.</p

Development of Resistance towards Artesunate in MDA-MB-231 Human Breast Cancer Cells

Breast cancer is the most common cancer and the second leading cause of cancer death in industrialized countries. Systemic treatment of breast cancer is effective at the beginning of therapy. However, after a variable period of time, progression occurs due to therapy resistance. Artesunate, clinically used as anti-malarial agent, has recently revealed remarkable anti-tumor activity offering a role as novel candidate for cancer chemotherapy. We analyzed the anti-tumor effects of artesunate in metastasizing breast carcinoma in vitro and in vivo. Unlike as expected, artesunate induced resistance in highly metastatic human breast cancer cells MDA-MB-231. Likewise acquired resistance led to abolishment of apoptosis and cytotoxicity in pre-treated MDA-MB-231 cells. In contrast, artesunate was more cytotoxic towards the less tumorigenic MDA-MB-468 cells without showing resistance. Unraveling the underlying molecular mechanisms, we found that resistance was induced due to activation of the tumor progression related transcription factors NFκB and AP-1. Thereby transcription, expression and activity of the matrix-degrading enzyme MMP-1, whose function is correlated with increased invasion and metastasis, was up-regulated upon acquisition of resistance. Additionally, activation of the apoptosis-related factor NFκB lead to increased expression of ant-apoptotic bcl2 and reduced expression of pro-apoptotic bax. Application of artesunate in vivo in a model of xenografted breast cancer showed, that tumors growth was not efficiently abolished as compared to the control drug doxorubicin. Taken together our in vitro and in vivo results correlate well showing for the first time that artesunate induces resistance in highly metastatic breast tumors

Structural Basis for c-di-GMP-Mediated Inside-Out Signaling Controlling Periplasmic Proteolysis

X-ray crystallographic structural analyses of the bacterial transmembrane receptor LapD identify conserved molecular mechanisms that control biofilm formation in response to changes in the intracellular levels of the second messenger c-di-GMP

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods

Rural-to-Urban Labor Migration, Household Livelihoods, and the Rural Environment in Chongqing Municipality, Southwest China

Rural migration and its relationship to the rural environment have attracted increasing research interest in recent decades. Rural migration constitutes a key component of human population movement, while rural areas contain most of the world’s natural resources such as land and forests. This study empirically evaluates a conceptual framework incorporating rural household livelihoods as an integrative mediating factor between rural migration and the rural environment in the context of rural-to-urban labor migration in Chongqing Municipality, Southwest China. The analysis draws on data collected through household surveys and key informant interviews from four villages. Results confirm the hypothesis that labor-migrant and non-labor-migrant households differ significantly in livelihood activities including agricultural production, agricultural technology use, income and consumption, and resource use and management. Implications for the subsequent environmental outcomes of rural labor out-migration and corresponding natural resource management and policy in rural origin areas are discussed