The first were questions included only in the postintervention survey. In these cases, reference 4 the responses by the control and the intervention groups were compared in a manner similar to the baseline data. The second included questions for which the categorical responses to a question were not dichotomous. For these, chi-square tests for both the control villages and the intervention villages were presented. Both the pre- and postintervention surveys included questions in which respondents were asked for their positions, for example, ��reasons to stop smoking��: health��yes or no, money��yes or no, and so forth. The percentage of ��yes�� responses for each subvariable of the series was evaluated to compare the differences between the groups at both the pre- and postintervention stages.

The statistical analysis also explored changes in responses from before and after for each group. Finally, the general estimating equation, using a binomial family and logit link, was employed to adjust for variables such as age or gender when looking for differences between the control and intervention groups in relation to the target-dependent variable. The adjusted odds ratios were reported for predictor variables in the model. Stata 12 software was used for statistical analysis. RESULTS Study Population In the intervention villages, 88.9% of the randomly selected households were contacted (i.e., residents were present and agreed to talk to the team); the refusal rate among these households was 1.3%. The contact rate in the control villages was 91.3%, with a refusal rate of 0.9%.

The total number of respondents who participated in the preintervention study was 7,657. Each of the households that participated in the preintervention was approached following the intervention. In this phase of the study, there was a contact rate of 98.1% in the intervention villages, and of those households, a 98.6% participation rate. In the control villages, there was a contact rate of 98.6%, and of those households, a 99.0% participation rate. In some cases, there were new families or new members of the household at these addresses; therefore, the study population includes some individuals who participated in the postintervention but not the preintervention. However, for the purposes of this study, the analysis included only the 5,934 individuals who participated in both surveys for an overall 77.

5% retention rate. This same retention rate was also seen in both the control and intervention subgroups. Preintervention Characteristics and Smoking Behavior The demographic profiles and baseline smoking behaviors (Table 1) of both the control and intervention groups were very similar. There was no statistically significant difference in the median age, gender ratio, education, Drug_discovery employment, and marital status, although there were slightly more married respondents in the control group.

Smoking abstinence Smoking abstinence was assessed using biochemically verified www.selleckchem.com/products/Abiraterone.html self-report at Week 7 and Week 26. If participants reported that they had not smoked ��any cigarettes at all�� in the past seven days, 7-day point prevalence smoking abstinence was confirmed using tests for salivary cotinine (<15ng/ml). Baseline measures Participants provided demographic, psychosocial, tobacco-specific, and health information at baseline. Standardized demographic questionnaires were used to document age, gender, relationship status, income, employment, and education. Participants reported cpd, whether they smoked menthol or non-menthol cigarettes, and number of quit attempts in the past year. Nicotine dependence was assessed using the Fagerstr?m Test for Nicotine Dependence (FTND; Heatherton, Kozlowski, Frecker, & Fagerstr?m, 1991).

The FTND consists of six items including time to first cigarette after waking and cpd; scores range from 0�C10 with higher scoring indicating greater dependence. Smoking within 30 mins of waking is indicative of significant nicotine dependence. Additionally, participants�� salivary cotinine levels were assessed. Symptoms of depression were assessed using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10; Cole, Rabin, Smith, & Kaufman, 2004). Analyses Statistical analyses were conducted using SPSS version 18. Participant characteristics and smoking history are summarized in Table 1.

Two sample t tests and chi-square analyses were conducted to determine if there were significant demographic, psychosocial, and tobacco-specific differences between participants who perceived their treatment group assignment to be bupropion and those who perceived their assignment to be placebo. A chi-square test was conducted to determine whether there were significant differences between the bupropion assigned group and the placebo assigned group on their perceptions of treatment assignment. The association between perceived treatment assignment (assessed at Week 7) and smoking abstinence at Week 7 and Week 26 was investigated using multiple logistic regression. Initially actual treatment assignment was regressed on verified smoking abstinence, followed by adding perceived group assignment, then adding the interaction between perceived group assignment and actual treatment assignment. Table 1.

Participant Baseline Characteristics Results Participants Of the 540 participants enrolled in the clinical trial and randomized to the treatment and placebo groups, 393 participants returned for the end-of-treatment (Week 7) survey, with 390 reporting their perceived treatment assignment. The sample was predominantly female (63.1%), AV-951 48.1 years of age (SD = 11.2), and smoked 8 cpd (SD = 2.5). Further, 70.3% smoked within 30 mins of waking, and 81.3% smoked mentholated cigarettes.

In recent years, especially in the United States, the industry has shifted the focus to economic claims that smoke-free environments are disastrous for the hospitality industry, investing millions of dollars in restaurant associations (Dearlove et al., 2002) and Tubacin alpha-tubulin in the gaming industry (Mandel & Glantz, 2004). These claims continue to be pressed vigorously to oppose legislation, despite the fact that all high-quality independently funded and peer-reviewed research regarding the effects of smoke-free policies on the hospitality industry has consistently shown no effect or a positive effect on revenue (Hahn, 2010; Scollo, Lal, Hyland, & Glantz, 2003). It remains a challenge to educate restaurateurs and others in the hospitality industry, who often become (unknowingly) the foot soldiers for the tobacco industry in its effort to halt or delay the smoke-free movement.

RESEARCH TO SUPPORT THE IMPLEMENTATION AND ENFORCEMENT OF SMOKE-FREE ENVIRONMENTS Many countries and subnational entities have assessed the extent of SHS exposure in their efforts to advance and evaluate the development, implementation, and enforcement of smoke-free legislation (Breysse & Navas-Acien, 2010). SHS exposure can be measured using questionnaires, environmental markers (personal or area monitoring), and biomarkers (Samet, 1999). Objective measures of SHS (in the environment or in the human body) are excellent tools to quantify exposure and its health effects, educate policy makers and the public about the importance of smoke-free legislation, and evaluate the impact of legislation after implementation.

Environmental Measures of SHS The most widely used methods for determining SHS exposure in indoor public places and workplaces are airborne nicotine and PM <2.5 ��m (PM2.5) (Barnoya et al., 2007; Hyland, Travers, Dresler, Higbee, & Cummings, 2008; Liu et al., 2010; Lopez et al., 2008; Navas-Acien et al., 2004; Nebot et al., 2005). Airborne SHS studies generally measure nicotine for several days, reflecting time-weighted average concentrations over the period of assessment. PM2.5 studies generally measure air PM during short periods of time (minutes or hours), reflecting concentrations during actual occupancy. The main advantage of nicotine over PM2.5 is that it is tobacco specific. Measuring PM2.5 concentrations has the advantage of providing immediate information on SHS levels and allowing comparisons with safety standards.

Additionally, no permission is required to measure PM2.5 as it can be done discretely using a portable machine. On the other hand, air nicotine measurements require the establishments�� permission to place the monitor. In addition to direct environmental SHS measurements, mathematical models can be used to estimate exposure according to different patterns of cigarette smoking Anacetrapib as well as to compare different control measures.

In addition, early symptoms may serve as ��cues to action�� (Abraham & Sheeran, 2005), motivating smoking moreover cessation. Early physical symptoms associated with smoking occur in people with limited smoking histories and worsen over time (USDHHS, 1990, 1994). Arday et al. (1995) found that smoking-related symptoms, including coughing, wheezing, and shortness of breath without exercising, were greater in adolescents with a 4-year history of regular smoking than in never-smokers. Generally, the negative health consequences of smoking exhibit a dose�Cresponse relationship (Arday et al., 1995; Newcomb & Bentler, 1987). Psychosocial factors may worsen smoking-related symptoms Two prominent psychosocial factors have been associated with both smoking and long-term health outcomes: stress and depression (cf.

, Kassel, Stroud, & Paronis, 2003). Stress. Smokers experience greater stress levels than do nonsmokers (Parrott, 1999), a relationship also found among Black smokers (Romano, Bloom, & Syme, 1991; Webb & Carey, 2008). The elevated stress levels among urban Blacks (Ewart & Suchday, 2002) who are exposed to multiple sources of daily stress (Geronimus, 1992) also may affect the experience of smoking-related physical symptoms. Stress is an important consideration, given its relationship with disease onset and course (Harris, 2001) and cardiovascular health (Black & Garbutt, 2002). Depression. Substantial evidence supports the association between smoking and depression. The likelihood of depression among smokers is threefold that among nonsmokers (Murphy et al., 2003).

In addition, a history of depression is related to increased withdrawal symptoms (Breslau, Kilbey, & Andreski, 1992) and a reduced likelihood of cessation (Anda et al. 1990; Hall, Mu?oz, Reus, & Sees, 1993). However, the relationship between depression and smoking abstinence is still under debate, with a recent meta-analysis demonstrating no relationship (Hitsman, Borrelli, McChargue, Spring, & Niaura, 2003) and research among Blacks showing a negative association (Catley et al., 2005). Among Black women, depression is associated with both smoking and high blood pressure (Artinian, Washington, Flack, Hockman, & Jen, 2006). Alcohol use and smoking Alcohol use also has been implicated as a correlate of smoking (e.g., Dierker et al., 2006) and as a risk factor for negative health consequences, such as cancer and cardiac disease (Schlecht et al.

, 1999). Alcohol use also is positively associated Dacomitinib with smoking among Black women (Webb & Carey, 2008). Previous research has not examined the influence of alcohol use on short-term, smoking-related symptoms among Blacks. Theoretical model The biopsychosocial model provides a framework for examining relationships among physical symptoms and psychosocial and behavioral factors.

The results for sociodemographic variables are mixed with the exception of marital status: Being married increases SKI 606 the probability of expected quitting and/or lower cigarette consumption. Table 3 examines predictors of Model C. In generalized ordered logit models, variables can have differential impact across quit intention stages. The default generalized ordered logit model is similar to a series of binary logistic regressions. Each model contrasts the value of the dependent variable (and any lower value) with all values above. A positive coefficient means that a higher value of the explanatory variable is associated with a higher likelihood that a smoker is further up on the quit continuum scale.

For variables that satisfy the parallel lines assumption and have the same coefficients across the stages of our dependent variable, we present their coefficients only in the first two columns. For variables that have different coefficients across the stages of the dependent variable, we report all their coefficients. Table 3. Generalized Ordered Logit models for quit intention scale �C all countries The self-reported price has a differential impact on the quit intention scale with its impact being the largest at the end of the scale leading to quitting (Table 3). The larger the price increase (in absolute terms) that an individual faces, given his/her current brand/price choice, the higher the likelihood that s/he leans toward quitting as opposed to no behavioral change or change in behavior that excludes quitting.

The effect of the other price measure is also positive but similar across the stages of quitting: Higher state prices/taxes motivate smokers to move up along the quit intention scale toward quitting. The level of addiction, as measured by number of cigarettes smoked per day, pushes a smoker away from quitting. Compared with U.S. smokers, Canadian and Australian smokers are significantly more likely to respond with either no change in smoking behavior or with quitting (the two extreme responses), while U.K. smokers are significantly more likely to respond with no change in smoking behavior. Being married encourages smokers to move toward quitting, but high level of education and income seems to work in the opposite direction. Table 4 presents results of Model C when the sample is reduced to U.S. and Canadian respondents only.

The findings confirm that a larger cigarette price increase. and a higher price level motivates a smoker to move up along the quit intention scale. The impact of both price measures is similar across stages, with the self-reported Brefeldin_A price having a larger impact. In all specifications, Canadian smokers tend to migrate toward both extremes of the cessation scale: either no change in smoking behavior or ��trying to quit�� only. Canadian smokers are therefore less likely to engage in compensatory behavior compared with U.S. smokers. Table 4.

Data were obtained from 704 organizations (83.7% response rate), which received a $75 honorarium. These procedures were approved promotion by the Institutional Review Boards of the University of Georgia and the University of Kentucky. Given the substantive focus on sustainability, analyses were restricted to 153 organizations that offered a counseling-based smoking cessation program at baseline (Figure 1). Follow-up data were obtained from 135 administrators (88.2% response rate) in 33 states and the District of Columbia. The average duration between waves of data collection was 2.7 years (SD = 0.6); additional analyses (not shown) indicated that duration was not associated with sustainment of smoking cessation programs. Measures The primary-dependent variable was sustainment of a counseling-based smoking cessation program.

At baseline, administrators indicated whether their organization offered a formal smoking cessation program. Affirmative responses prompted additional questions measuring whether the program included individual and/or group counseling sessions dedicated to smoking cessation. Organizations offering a smoking cessation program with at least one type of counseling were considered baseline adopters. These same items were utilized at follow-up to categorize organizations on sustainment (1 = sustained adopters, 0 = discontinuers). The independent variables were administrators�� attitudes and their reports of organizational barriers regarding smoking cessation, measured at baseline and follow-up. These items appear in Table 1.

A final cultural measure was the administrators�� perception of the percentage of clinical staff who were tobacco users. Table 1. Descriptive Statistics of Substance Use Disorder (SUD) Programs Offering a Counseling-Based Smoking Cessation Program at Baseline To control for organizational structure, these analyses drew upon data collected when programs were initially recruited into the NTCS. Control variables included sample type (publicly funded, TCs, or privately funded sample), location in a health care setting (1 = hospital or community mental health center, 0 = freestanding), ownership (1 = government owned, 0 = privately owned), profit status (1= for profit, 0 = nonprofit), accreditation by the Joint Commission or the Commission on the Accreditation of Rehabilitation Facilities (1 = accredited, 0 = not accredited), and delivery of only outpatient SUD treatment services (1= outpatient only, 0 = not outpatient only).

Organizational size was measured by the number of full-time equivalent employees (FTEs) and was natural log transformed to adjust for skew. Finally, availability of NRT, bupropion-SR, and varenicline was measured at baseline and follow-up. Each variable was dichotomous. Data Analysis In addition to descriptive statistics, paired t tests and Wilcoxon��s sign-rank tests for administrator attitudes Anacetrapib and organizational barriers at baseline and follow-up were calculated.

O. di Malattie Infettive e Tropicali; Carmela Pinnetti, S. Gerardo Hospital. Surgeons. Guido Basile, University of Catania; Gabriele Sganga, Universita selleck catalog Cattolica Sacro Cuore; Mauro Pittiruti, Universita Cattolica Sacro Cuore; Enzo Carlo Farina, Molinette hospital; Osvaldo Chiara, University of Milano; Alberto Marvaso, A Rizzoli Hospital; Sergio Colizza, Fatebenefratelli Isola Tiberina; Emmanuele Scarano, Universita Cattolica Sacro Cuore; Vinicio Fiorini, Azienda Ospedaliera di Mantova. Serbia Infection control physicians.

Lijliana Markovic-Denic, Institute of cardiovascular diseases of Vojvodina, Sremska Kamenica; Andrea Uzelac-Skoric, University hospital ��Dr Dragisa Misovic-Dedinje��, Belgrade, Vesna Mioljevi?, Clinical Center of Serbia, Belgrade; Milorad Sari?, Institute of public health-Pozarevac; Zorana Djordjevi?, University Clinical Center, Kragujevac; Vladan Saponji?, Institute of public health-Kraljevo; Zorana Kuli?, Institute of public health-Leskovac; Ivana ?iri?, Institute of public health-Zajecar; Branislav Tiodorovi?, Faculty of Medicine, Ni?; Biljana Mijovi?, Institute of public health U?ice. Surgeons. Ivan Mati?, Aleksinac-General Hospital; Vladimir Zivanovi?, ��Belgrade University hospital ��Dr Dragisa Misovi?-Dedinje��; Aleksandar Simic, Clinical Center of Serbia, Digestive surgery, Belgrade; ?edomir Vu?ini?, Clinical Center of Serbia-Clinic for Orthopedy, Belgrade; Bogoljub Mihajlovic, Institute of cardiovascular diseases of Vojvodina, Sremska Kamenica; Ivica Zarev, Kraljevo-General hospital; Ivan Stefanovi?, Ni?-University Clinical Center, Clinic for neurosurgery; Milorad Mitkovi?, Ni?-University Clinical Center, Clinic for orthopedy; Milorad Marinkovi?, Zaje?ar-General hospital; Zoran Kuja?i?, Zrenjanin-General hospital.

Switzerland Infection control physicians. Nicolas Troillet, Central institute,Valais Hospital, Sion; Christiane Petignat, Cilengitide CHU Vaudois, Lausanne; Hugo Sax, University Hospital of Geneva; Philippe Erard, Hopital des Cadolles, Neuchatel; Gerhard Eich, Triemli Spital, Z��rich; Cristina Bellini, H?pital Riviera, Vevey; Christian Chuard, H?pital de Fribourg; Andreas Widmer, Universit?tsspital, Basel; Christian Ruef, Universit?tsspital, Z��rich; Alain Cometta, H?pital du Nord Vaudois, Yverdon. Surgeons.

4 to 4.4-fold in the former and 1.2 to 1.6-fold in the latter group, selleck bio P = 0.019). Altogether these findings confirm our original observation and other more recent reports on the major influence of the biochemical profile on LS in the setting of both acute and chronic liver damage[8-10]. Finally, we found that prolonged biochemical remissions were associated with progressive reductions of FS values. LS declined yearly at about 0.2-fold in treated patients followed-up prospectively for 48 mo, and a proportion of patients who maintained evidence of cirrhosis at US achieved values of FS < 11.8 kPa. This was responsible for the worse diagnostic performance of FS in treated patients in whom the sensitivity for detecting cirrhosis fell from 86.5% to 54.2% in untreated vs treated patients with fibrosis �� S5 (Table (Table6).

6). Altogether, these data suggest a non-linear correlation between the overall kinetics of LS and histological staging during antiviral treatment. Future studies should be addressed to understand the relations among the reductions of LS, necrosis, inflammation and fibrosis in the separate settings of different fibrosis stages (i.e. �� S3/< S3 and presence/absence of cirrhosis) and liver disease etiology (i.e. HBV and HCV). In fact, much of the LS changes depend on the different elastic relations among fine blocks of the liver structure. Thus, the interplay between the extent and structure of the collagen septa within the fine liver block, and the different type and extent of liver inflammatory infiltrate within them, might account for both the different FS cut-offs between CHB and CHC patients and for the different kinetics of FS and fibrosis decline during antiviral therapy.

In conclusion, our study suggests that the LS provides a useful non-invasive tool to monitor liver disease in the chronic HBV carrier. In the inactive carrier, it helps to identify non-HBV-related causes of liver damage and transient reactivation of HBV liver disease. In the CHB patient, provided that the pattern of biochemical activity is taken into account, LS values < 7.5 exclude Cilengitide the presence of significant fibrosis (�� S3) with a high NPV (97.3%) and low negative likelihood ratio (0.07). FS values �� 11.8 kPa are highly specific (96.3%) for cirrhosis and show good PPV (86.5%) and positive likelihood ratio (23.18). In the HBV carrier with LS values ranging from 7.5 to 11.8 kPa, which are indicative of significant liver disease, liver biopsy remains the gold standard for an accurate grading and staging of liver disease. Finally, in CHB patients the monitoring of LS appears useful to highlight major changes in intrahepatic liver disease and warrants a more appropriate timing for control liver biopsies.

aReasons for ineligibility were not mutually exclusive; therefore, a participant could be ineligible for more t
The Framework Convention on Tobacco Control (FCTC) calls upon countries to implement evidence-based strategies to reduce tobacco use and tobacco-attributable morbidity and mortality (World Health never Organization [WHO], 2003). Here, we review FCTC Articles 20, 21, and 22, which call for strong monitoring of the tobacco epidemic, information exchange, and collaboration among Parties and other relevant organizations (Tables 1�C3). Table 1. Article 20: Research, Surveillance, and Exchange of Information Table 3. Article 22: Cooperation in the Scientific, Technical, and Legal Fields and Provision of Related Expertise Table 2.

Article 21: Reporting and Exchange of Information More specifically, Article 20 calls initially for ��research that addresses determinants and consequences of tobacco consumption and exposure to tobacco smoke as well as research for identification of alternative crops.�� It also requires ��programmes for national, regional, and global surveillance of the magnitude, patterns, determinants, and consequences of tobacco consumption and exposure to tobacco smoke�� and for parties to establish and maintain ��an updated database of laws and regulations on tobacco control and, as appropriate, information about their enforcement, as well as pertinent jurisprudence, and cooperate in the development of programmes for regional and global tobacco control.�� Article 21 (section 1d) requires nations to provide regular updates on surveillance and research, as specified in Article 20.

When properly enacted, Articles 20 and 21 will ensure that data are available to provide feedback to countries on the relative effectiveness of programs and policies. Article 22 is fundamentally about knowledge transfer and capacity building within the network of FCTC Parties. Thus, Parties are required to cooperate and collaborate with each other in order to facilitate ��the transfer of technical, scientific and legal expertise and technology�� that will allow countries to effectively implement the Article. More specifically, the Article requires the ��facilitation of the development, transfer and acquisition of technology, knowledge, skills, capacity and expertise related to tobacco control,�� along with the provision of expertise and training needed to develop and implement FCTC policies.

In addition, specific mention is made of identifying and promoting tobacco control methods, including tobacco treatment. Policy makers may lack the scientific background to assess the quality and implications of scientific data (Koplan & Mackay, 2012). They can also be influenced by tobacco industry representatives and the general public, who can be misinformed. Drug_discovery Thus, while systems exist to generate high-quality data, we also need to understand if and how the information they generate can be optimally disseminated, as called for by Article 22.

However, in this small set of patients, the average concentration of miR-BART7 was only moderately increased in the plasma of NPC patients by comparison selleck chemicals Oligomycin A with non-NPC controls, either healthy EBV-carriers or patients affected by non-NPC tumors. More recently, Wong et al. have confirmed the consistent detection of BART microRNAs in serum samples from NPC patients suggesting a higher specificity of detection for a sub-group of miR-BARTs including miR-BART17-5p (hereafter called miR-BART17) [7]. Therefore, we started this novel study with two aims: 1) to substantiate the notion that miR-BART17 is detectable with high specificity in plasma samples from NPC patients of various geographic origins and 2) to better characterize the vesicular or non-vesicular carriers of miR-BART17 in human plasma.

We report that miR-BART17 is detected at a significantly higher concentration in the plasma of NPC patients by comparison with non-NPC donors and that its concentration is apparently not correlated to the EBV DNA load. In addition, we provide substantial evidence that circulating miR-BART17 molecules are associated with a protein-rich fraction of the plasma but not with circulating exosomes. Results High concentrations of miR-BART17 in plasma samples from NPC patients We first evaluated by RT-qPCR ebv-miR-BART17-5p (miR-BART17) and two cellular microRNAs – hsa-miR-146a and hsa-miR-16 – in a pilot series of plasma samples from 3 NPC patients and 2 control donors bearing non-NPC tumors (Figure1). A synthetic microRNA from Caenorhabditis Elegans, cel-miR-39, was used as an exogenous control (it has no homology with mammalian microRNAs).

The same amount of cel-miR-39 was ��spiked-in�� in all plasma samples at an early stage of RNA extraction in order to monitor RNA extraction bias. Hsa-miR-146a and hsa-miR-16 were used as endogenous references. They are known to be abundant in plasma samples from most human subjects although with wide inter-individual variations [8]. As shown in Figure1, cel-miR-39 amplifications were homogeneous in all five plasma samples, reflecting the good quality of RNA extraction. As expected, amplifications of miR-146a and miR-16 were heterogeneous but without specific distribution for the samples from NPC patients or control donors. In contrast, significant Cilengitide amplifications of miR-BART17 were only seen for NPC patients. Therefore, we decided to extend this series to 27 consecutive NPC patients and 10 control donors (9 donors bearing non-NPC tumors and 1 healthy EBV-carrier). NPC patients were of various ethnical origins with a majority from North Africa. They presented either with localized (from stage II to stage IV b; UICC 2009) or metastatic disease at first occurrence or following a tumor relapse (Table1).