6 resultados para Poison sumac.

em Queensland University of Technology - ePrints Archive


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As part of an anti-cancer natural product drug discovery program, we recently identified eusynstyelamide B (EB), which displayed cytotoxicity against MDA-MB-231 breast cancer cells (IC50 = 5 μM) and induced apoptosis. Here, we investigated the mechanism of action of EB in cancer cell lines of the prostate (LNCaP) and breast (MDA-MB-231). EB inhibited cell growth (IC50 = 5 μM) and induced a G2 cell cycle arrest, as shown by a significant increase in the G2/M cell population in the absence of elevated levels of the mitotic marker phospho-histone H3. In contrast to MDA-MB-231 cells, EB did not induce cell death in LNCaP cells when treated for up to 10 days. Transcript profiling and Ingenuity Pathway Analysis suggested that EB activated DNA damage pathways in LNCaP cells. Consistent with this, CHK2 phosphorylation was increased, p21CIP1/WAF1 was up-regulated and CDC2 expression strongly reduced by EB. Importantly, EB caused DNA double-strand breaks, yet did not directly interact with DNA. Analysis of topoisomerase II-mediated decatenation discovered that EB is a novel topoisomerase II poison.

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Mycotoxins – from the Greek μύκης (mykes, mukos) “fungus” and the Latin (toxicum) “poison” – are a large and growing family of secondary metabolites and hence natural products produced by fungi, in particular by molds (1). It is estimated that well over 1,000 mycotoxins have been isolated and characterized so far, but this number will increase over the next few decades due the availability of more specialized analytical tools and the increasing number of fungi being isolated. However, the most important classes of fungi responsible for these compounds are Alternaria, Aspergillus (multiple forms), Penicillium, and Stachybotrys. The biological activity of mycotoxins ranges from weak and/or sometimes positive effects such as antibacterial activity (e.g. penicillin derivatives derived from Penicillium strains) to strong mutagenic (e.g. aflatoxins, patulin), carcinogenic (e.g. aflatoxins), teratogenic, neurotoxic (e.g. ochratoxins), nephrotoxic (e.g. fumonisins, citrinin), hepatotoxic, and immunotoxic (e.g. ochratoxins, diketopiperazines) activities (1, 2), which are discussed in detail in this volume.

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In this paper, dynamic modeling and simulation of the hydropurification reactor in a purified terephthalic acid production plant has been investigated by gray-box technique to evaluate the catalytic activity of palladium supported on carbon (0.5 wt.% Pd/C) catalyst. The reaction kinetics and catalyst deactivation trend have been modeled by employing artificial neural network (ANN). The network output has been incorporated with the reactor first principle model (FPM). The simulation results reveal that the gray-box model (FPM and ANN) is about 32 percent more accurate than FPM. The model demonstrates that the catalyst is deactivated after eleven months. Moreover, the catalyst lifetime decreases about two and half months in case of 7 percent increase of reactor feed flowrate. It is predicted that 10 percent enhancement of hydrogen flowrate promotes catalyst lifetime at the amount of one month. Additionally, the enhancement of 4-carboxybenzaldehyde concentration in the reactor feed improves CO and benzoic acid synthesis. CO is a poison to the catalyst, and benzoic acid might affect the product quality. The model can be applied into actual working plants to analyze the Pd/C catalyst efficient functioning and the catalytic reactor performance.

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Background Pharmacists are considered medication experts but are underutilized and exist mainly at the periphery of the Malaysian primary health care team. Private general practitioners (GPs) in Malaysia are granted rights under the Poison Act 1952 to prescribe and dispense medications at their primary care clinics. As most consumers obtain their medications from their GPs, community pharmacists’ involvement in ensuring safe use of medicines is limited. The integration of a pharmacist into private GP clinics has the potential to contribute to quality use of medicines. This study aims to explore health care consumers’ views on the integration of pharmacists within private GP clinics in Malaysia. Methods A purposive sample of health care consumers in Selangor and Kuala Lumpur, Malaysia, were invited to participate in focus groups and semi-structured interviews. Sessions were audio recorded and transcribed verbatim and thematically analyzed using NVivo 10. Results A total of 24 health care consumers participated in two focus groups and six semi-structured interviews. Four major themes were identified: 1) pharmacists’ role viewed mainly as supplying medications, 2) readiness to accept pharmacists in private GP clinics, 3) willingness to pay for pharmacy services, and 4) concerns about GPs’ resistance to pharmacist integration. Consumers felt that a pharmacist integrated into a private GP clinic could offer potential benefits such as to provide trustworthy information on the use and potential side effects of medications and screening for medication misadventure. The potential increase in costs passed on to consumers and GPs’ reluctance were perceived as barriers to integration. Conclusion This study provides insights into consumers’ perspectives on the roles of pharmacists within private GP clinics in Malaysia. Consumers generally supported pharmacist integration into private primary health care clinics. However, for pharmacists to expand their capacity in providing integrated and collaborative primary care services to consumers, barriers to pharmacist integration need to be addressed.

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In this paper we present a new method for performing Bayesian parameter inference and model choice for low count time series models with intractable likelihoods. The method involves incorporating an alive particle filter within a sequential Monte Carlo (SMC) algorithm to create a novel pseudo-marginal algorithm, which we refer to as alive SMC^2. The advantages of this approach over competing approaches is that it is naturally adaptive, it does not involve between-model proposals required in reversible jump Markov chain Monte Carlo and does not rely on potentially rough approximations. The algorithm is demonstrated on Markov process and integer autoregressive moving average models applied to real biological datasets of hospital-acquired pathogen incidence, animal health time series and the cumulative number of poison disease cases in mule deer.