Characteristics of Sexual Abuse in Childhood and Adolescence Influence Sexual Risk Behavior in Adulthood

Childhood and adolescent sexual abuse has been associated with subsequent (adult) sexual risk behavior, but the effects of force and type of sexual abuse on sexual behavior outcomes have been less well-studied. The present study investigated the associations between sexual abuse characteristics and later sexual risk behavior, and explored whether gender of the child/adolescent moderated these relations. Patients attending an STD clinic completed a computerized survey that assessed history of sexual abuse as well as lifetime and current sexual behavior. Participants were considered sexually abused if they reported a sexual experience (1) before age 13 with someone 5 or more years older, (2) between the ages of 13 and 16 with someone 10 or more years older, or (3) before the age of 17 involving force or coercion. Participants who were sexually abused were further categorized based on two abuse characteristics, namely, use of penetration and force. Analyses included 1177 participants (n=534 women; n=643 men). Those who reported sexual abuse involving penetration and/or force reported more adult sexual risk behavior, including the number of lifetime partners and number of previous STD diagnoses, than those who were not sexually abused and those who were abused without force or penetration. There were no significant differences in sexual risk behavior between nonabused participants and those who reported sexual abuse without force and without penetration. Gender of the child/adolescent moderated the association between sexual abuse characteristics and adult sexual risk behavior; for men, sexual abuse with force and penetration was associated with the greatest number of episodes of sex trading, whereas for women, those who were abused with penetration, regardless of whether the abuse involved force, reported the most episodes of sex trading. These findings indicate that more severe sexual abuse is associated with riskier adult sexual behavior

Associations between tamoxifen, estrogens, and FSH serum levels during steady state tamoxifen treatment of postmenopausal women with breast cancer

<p>Abstract</p> <p>Background</p> <p>The cytochrome P450 (CYP) enzymes 2C19, 2D6, and 3A5 are responsible for converting the selective estrogen receptor modulator (SERM), tamoxifen to its active metabolites 4-hydroxy-tamoxifen (4OHtam) and 4-hydroxy-<it>N</it>-demethyltamoxifen (4OHNDtam, endoxifen). Inter-individual variations of the activity of these enzymes due to polymorphisms may be predictors of outcome of breast cancer patients during tamoxifen treatment. Since tamoxifen and estrogens are both partly metabolized by these enzymes we hypothesize that a correlation between serum tamoxifen and estrogen levels exists, which in turn may interact with tamoxifen on treatment outcome. Here we examined relationships between the serum levels of tamoxifen, estrogens, follicle-stimulating hormone (FSH), and also determined the genotypes of CYP2C19, 2D6, 3A5, and SULT1A1 in 90 postmenopausal breast cancer patients.</p> <p>Methods</p> <p>Tamoxifen and its metabolites were measured by liquid chromatography-tandem mass spectrometry. Estrogen and FSH levels were determined using a sensitive radio- and chemiluminescent immunoassay, respectively.</p> <p>Results</p> <p>We observed significant correlations between the serum concentrations of tamoxifen, <it>N</it>-dedimethyltamoxifen, and tamoxifen-<it>N</it>-oxide and estrogens (p < 0.05). The genotype predicted CYP2C19 activity influenced the levels of both tamoxifen metabolites and E1.</p> <p>Conclusions</p> <p>We have shown an association between tamoxifen and its metabolites and estrogen serum levels. An impact of CYP2C19 predicted activity on tamoxifen, as well as estrogen kinetics may partly explain the observed association between tamoxifen and its metabolites and estrogen serum levels. Since the role of estrogen levels during tamoxifen therapy is still a matter of debate further prospective studies to examine the effect of tamoxifen and estrogen kinetics on treatment outcome are warranted.</p

Loss of Cytoplasmic CDK1 Predicts Poor Survival in Human Lung Cancer and Confers Chemotherapeutic Resistance

The dismal lethality of lung cancer is due to late stage at diagnosis and inherent therapeutic resistance. The incorporation of targeted therapies has modestly improved clinical outcomes, but the identification of new targets could further improve clinical outcomes by guiding stratification of poor-risk early stage patients and individualizing therapeutic choices. We hypothesized that a sequential, combined microarray approach would be valuable to identify and validate new targets in lung cancer. We profiled gene expression signatures during lung epithelial cell immortalization and transformation, and showed that genes involved in mitosis were progressively enhanced in carcinogenesis. 28 genes were validated by immunoblotting and 4 genes were further evaluated in non-small cell lung cancer tissue microarrays. Although CDK1 was highly expressed in tumor tissues, its loss from the cytoplasm unexpectedly predicted poor survival and conferred resistance to chemotherapy in multiple cell lines, especially microtubule-directed agents. An analysis of expression of CDK1 and CDK1-associated genes in the NCI60 cell line database confirmed the broad association of these genes with chemotherapeutic responsiveness. These results have implications for personalizing lung cancer therapy and highlight the potential of combined approaches for biomarker discovery

Ebolavirus Is Internalized into Host Cells via Macropinocytosis in a Viral Glycoprotein-Dependent Manner

Ebolavirus (EBOV) is an enveloped, single-stranded, negative-sense RNA virus that causes severe hemorrhagic fever with mortality rates of up to 90% in humans and nonhuman primates. Previous studies suggest roles for clathrin- or caveolae-mediated endocytosis in EBOV entry; however, ebolavirus virions are long, filamentous particles that are larger than the plasma membrane invaginations that characterize clathrin- or caveolae-mediated endocytosis. The mechanism of EBOV entry remains, therefore, poorly understood. To better understand Ebolavirus entry, we carried out internalization studies with fluorescently labeled, biologically contained Ebolavirus and Ebolavirus-like particles (Ebola VLPs), both of which resemble authentic Ebolavirus in their morphology. We examined the mechanism of Ebolavirus internalization by real-time analysis of these fluorescently labeled Ebolavirus particles and found that their internalization was independent of clathrin- or caveolae-mediated endocytosis, but that they co-localized with sorting nexin (SNX) 5, a marker of macropinocytosis-specific endosomes (macropinosomes). Moreover, the internalization of Ebolavirus virions accelerated the uptake of a macropinocytosis-specific cargo, was associated with plasma membrane ruffling, and was dependent on cellular GTPases and kinases involved in macropinocytosis. A pseudotyped vesicular stomatitis virus possessing the Ebolavirus glycoprotein (GP) also co-localized with SNX5 and its internalization and infectivity were affected by macropinocytosis inhibitors. Taken together, our data suggest that Ebolavirus is internalized into cells by stimulating macropinocytosis in a GP-dependent manner. These findings provide new insights into the lifecycle of Ebolavirus and may aid in the development of therapeutics for Ebolavirus infection

A practical guide to multi-objective reinforcement learning and planning

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems