Procedures for assessing and validating high and low quit interes

Procedures for assessing and validating high and low quit interest at screening are described elsewhere (Perkins et al., 2008, 2010). Procedure As described new product below, expired-air CO and the number of cigarettes smoked were assessed daily (Mon�CFri) during the two 5-day quit attempt periods (i.e., a total of 10 daily assessments), separated by at least 1 week of ad lib smoking without any medication. These medication conditions are not particularly relevant in these analyses since our focus was on the sensitivity and specificity of CO level for verifying 24-hr abstinence, regardless of the method by which abstinence was obtained. Sessions were scheduled between noon and 4:00 p.m. at the same time of the day for each participant.

Daily expired-air CO was assessed at each session via Breathco CO monitor (Lenexa, KS) following a 20-s breath hold prior to slowly expiring air into the monitor mouthpiece (Javors et al., 2005; Raiff, Faix, Turturici, & Dallery, 2010). Subjects were not informed of their CO readings (in ppm), which were recorded away from the participants�� view. All 261 participants were randomly assigned to monetary reinforcement of daily abstinence ($12/day; n = 127) or to no reinforcement (n = 134) during both quit periods. Reinforcement was provided for those with CO <5 (��4 ppm) and who reported no smoking in the last 24hr, although none was informed of their CO values or this specific CO criterion for reinforcement. Rather, they were told they would be reinforced for ��being quit.

�� For this analysis, this manipulation enabled us to assess whether the presence or absence of reinforcement for quitting influenced the association of CO with abstinence (see Javors et al., 2005). These studies were approved by the University of Pittsburgh Institutional Review Board, and all participants provided written informed consent for participation after the nature and the consequences of the relevant study were explained. Other details of the study procedures are described in Perkins et al. (2008, 2010). Assessing Daily Abstinence or Smoking Abstinence was defined as zero cigarettes smoked in the 24hr prior to each weekday session (Monday�CFriday) during the quit attempt periods. To assess the amount of daily smoking, participants were given ��tally�� cards to monitor all cigarette use.

Tallies were returned at the next session and the number of cigarettes smoked over precisely the 24hr prior to the session was recorded, based on tallies for that day and the previous day. The rows of each tally card listed the 24 individual hours of the day (e.g., ��noon to 1:00 p.m.��), Drug_discovery with a space to mark the tallies every time they smoked a cigarette at that time. (The size and the shape of the card were intentionally designed to fit inside the cellophane wrapper of a cigarette pack, to facilitate compliance.

g , access to alternative reinforcers)

g., access to alternative reinforcers) learn more and pharmacological (e.g., NRT) treatments on the reinforcing efficacy of cigarette smoke and for investigating the nicotine and non-nicotine factors that contribute to smoking (Bickel, Madden, & DeGrandpre, 1997; Johnson & Bickel, 2003; Johnson, Bickel, & Kirshenbaum, 2004; Shahan, Bickel, Madden, & Badger, 1999; Shahan, Odum, & Bickel, 2000). Despite its frequent use in human smoking studies as well as its utility in animal studies examining the reinforcing efficacy of other drugs of abuse, very few animal studies have examined the elasticity of demand for nicotine (Diergaarde, van Mourik, Pattij, Schoffelmeer, & De Vries, 2011). Increasing the study of behavioral economic outcomes in animal nicotine self-administration research is important for several reasons.

First, behavioral economic analysis is specifically intended for modeling drug abuse policies in animals (Hursh, 1991). Second, it would provide a conceptual framework to facilitate translation of findings between preclinical studies, clinical trials, and public policy concerned with nicotine reduction. Third, it provides unique information that can complement the analysis of nicotine reinforcement thresholds by elucidating the behavioral mechanisms mediating changes in such thresholds. For instance, a decrease in threshold could reflect an increase in potency, an increase in the reinforcing efficacy of nicotine, or both. Normalized demand curve analysis can measure changes in reinforcing efficacy per se independent of dose and potency, allowing analysis of the relative contribution of these two factors and facilitating comparison of demand across species (Hursh et al.

, 2005). Finally, demand curve analysis provides a simple and precise quantitative approach to measuring changes in reinforcing efficacy across a wide range of conditions (Hursh et al., 2005). Models of Relapse Reduced nicotine content cigarettes could also reduce the health burden of tobacco by facilitating cessation amongst those who initially continue to smoke (cf. Hatsukami et al., 2010). That is, some individuals may not stop smoking as a direct result of nicotine reduction, possibly because the nicotine content in cigarettes remains above their individual threshold for reinforcement. However, they may be more likely to achieve abstinence when they make an active quit attempt.

Animal models of reacquisition and reinstatement, as well as other models pertinent to cessation (e.g., withdrawal, punished behavior), may be useful for assessing this effect (Panlilio, Thorndike, AV-951 & Schindler, 2005; Shaham, Shalev, Lu, De Wit, & Stewart, 2003). For example, the ability of cues to reinstate behavior might decline as a result of a history of nicotine reduction. Whether this effect occurs, differs across individuals, or is affected by the pattern of reduction is unknown.

05, OR=2 18 (95% CI=1 04�C4 59) In GLM analyses of the continuou

05, OR=2.18 (95% CI=1.04�C4.59). In GLM analyses of the continuous withdrawal slope, there was neither a significant haplotype effect nor a significant diplotype effect across age-at-onset conditions. We examined the possible effect of bupropion treatment on the relationship between haplotype and withdrawal severity. First, bupropion was entered in a logistic regression selleck inhibitor model along with haplotype coding and a haplotype �� bupropion interaction with dichotomous withdrawal as the dependent variable. No significant interaction effects were observed for either the HA versus HC, p=.23, or the HA versus HB, p=.40, contrast. In a second model, the interaction term was dropped but bupropion was retained as a covariate. In this second test, the HA versus HC contrast was significant, p=.03, OR=1.

56 (95% CI=1.04�C2.33). The bupropion effect did not reach nominal significance, p=.08, OR=0.76 (95% CI=0.56�C1.03). Thus, the protective effect of HA relative to HC on withdrawal severity was independent of any effect bupropion may have had on withdrawal severity. Smoking cessation In a logistic regression analysis in which the dependent variable was relapse versus no relapse over the first 90 days postquit, we found no haplotype �� age-at-onset interaction. Thus, further tests of smoking cessation were made across the combined age-at-onset conditions. As shown in Figure 2 and Table 3, HA was associated with a higher relapse rate than HC, and the HA versus HB contrast approached significance. Diplotype analyses, also shown in Figure 2, indicated that diplotype AA had a higher 90-day relapse rate than every other diplotype, p��s=.

006�C.03, OR��s<0.45 (95% CI��s=0.15�C0.93). Abstinence rates, expressed as Kaplan�CMeier survival functions, over the first 90 days postquit are shown as a function of haplotype and diplotype in Figure 4. A survival analysis revealed a significant haplotype effect, ��2(2, n=767)=7.45, p=.02. Pairwise comparisons were significant for both the HA versus HB test, ��2(1, n=629)=5.41, p=.02, and the HA versus HC test, ��2(1, n=638)=4.58, p<.04. The HB versus HC comparison was not significant. We also found a significant diplotype effect across all five diplotypes, ��2(4, n=629)=5.41, p=.02. Pairwise comparisons indicated that the relapse rate was higher for diplotype AA than for each of the other four diplotypes, p��s=.005�C.03 (see Figure 4).

Entering drug (bupropion vs. placebo) as a covariate in the haplotype or diplotype survival analyses did not alter any of these findings nor were there any significant interaction effects with drug. Figure 4. Abstinence following a smoking cessation trial by haplotype (top, n=767) and diplotype (bottom, n=356). Abstinence is shown as the Kaplan�CMeier probability of remaining abstinent for the first 90 days Drug_discovery postquit. …

Forty-one percent of the women reported working at a paid job for

Forty-one percent of the women reported working at a paid job for an average of 28.95 hrs a week (SD = 10.81 hrs). Measures Participants The initial sample consisted of 312 pregnant women who were first interviewed at the end of the first trimester between 12 and 20 weeks gestation. However, given that we were interested in partner EPZ-5676 Histone Methyltransferase smoking, we restricted our analyses to include only women who reported that they were in a relationship at the time of the first interview. Thus, our final sample consisted of 245 pregnant women who reported being in a serious relationship. All analyses were conducted with these 245 women with the exception of analyses regarding change in SHS exposure over pregnancy.

This is an ongoing longitudinal study, and to date, 106 women have completed all three prenatal interviews and the 2-month postnatal interview (in order to obtain information about smoking between the last prenatal interview and delivery). Accordingly, the sample for the change analysis was restricted to these 106 women. Potential differences between the 245 women with first trimester data and the 106 who have complete data at all three trimesters were examined using independent sample t tests. Results indicated that women with complete data for all three prenatal interviews did not differ from those with first trimester data with regard to gravidity, education, number of smokers living in their home, or any of our SHS measures, with the exception of frequency of smoke exposure in a car (t(242.85) = 2.09, p = .04, d = 0.27).

Women with first trimester data reported higher frequency of smoke exposure in a car compared to women with complete data. Chi-square difference tests further revealed that women who had complete data did not differ from those with first trimester data in terms of employment, smoking status, partner smoking status, race, or living status with their partner. Maternal Substance Use Participants were interviewed in a private setting by trained interviewers. The timeline followback interview (TLFB; Sobell, Sobell, Klajmer, Pavan, and Basian, 1986) was used to assess maternal substance use at each prenatal and the postnatal interview. Participants were provided a calendar and asked to identify events of personal interest (i.e., holidays, birthdays, vacations, etc.) as anchor points to aid recall.

This method has been established as a reliable and valid method of obtaining longitudinal data on substance-use patterns, has good test�Cretest reliability, and is highly correlated with other intensive self-report measures (Brown et al., 1998). At each prenatal appointment, TLFB was used to gather daily tobacco, alcohol, and cannabis use for the previous 3 months. Women who smoked blunts were asked how many joints they could have rolled from the amount of marijuana in the blunt. Thus, self-reported Brefeldin_A data spanned 3 months prior to conception through delivery.

, 1982) These were selected

, 1982). These were selected Ceritinib clinical trial because they had the highest factor loadings in a study with a comparable sample (Unger, unpublished data). A sample item was, ��Family members feel very close to each other.�� Higher scores represent more cohesion (Cronbach��s �� = .77). Family Conflict Family conflict was measured with six items from FACES II (Olson et al., 1982). These were selected because they had the highest factor loadings in a study with a comparable sample (Unger, unpublished data). A sample item was, ��We have difficulty thinking of things to do as a family.�� Response choices ranged from 1 = almost never to 6 = almost always. Higher scores represent more family conflict (Cronbach��s �� = .69). Familismo Four items assessed familismo.

Three of the items came from the familismo scale described by Sabogal, Mar��n, Otero-Sabogal, & Mar��n (1987), and one item from the familismo scale described by Cu��llar et al. (1995). The four items had the highest factor loadings in an earlier study (Unger et al., 2002). Youth indicated (1 = definitely no to 4 = definitely yes) the likelihood of their families engaging in family-oriented behaviors. Higher scores represent greater familismo (Cronbach��s �� = .79). Respeto Four items assessed respeto. A sample item included ��It is important to honor my parents.�� Youth indicated their agreement on a 4-point scale (1 = definitely no to 4 = definitely yes) (Cronbach��s �� = .89). Traditional Gender Roles Seven items assessed traditional gender roles. Items came from the MACCSF (Cu��llar et al., 1995).

They were selected because they had the highest factor loadings. Adolescents indicated (1 = strongly disagree to 4 = strongly agree) their agreement with statements such as, ��Boys should not be allowed to play with dolls and other girls�� toys.�� Higher scores reflect more traditional gender roles (Cronbach��s �� = .80). Fatalismo Four items assessed fatalismo. Items came from the MACCSF (Cu��llar et al., 1995). They were selected because they had the highest factor loadings on their respective scales and did not load highly on other scales. Youth indicated the degree (1 = definitely no to 4 = definitely yes) to which they endorsed fatalistic beliefs: ��It��s more important to enjoy life now than to plan for the future.�� Higher scores denote more fatalismo (Cronbach��s �� = .78).

Past-30-Day Smoking One item assessed youth��s smoking: ��During the past 30 days, on how many days did you smoke cigarettes?�� Responses were rated on a 7-point scale (1 = Anacetrapib 0 days, 2 = 1 or 2 days, 3 = 3 to 5 days, 4 = 6 to 9 days, 5 = 10 to 19 days, 6 = 20 to 29 days, 7 = all 30 days). We recoded responses to 0 days versus all other due to skewed distributions. Adult Smoking One question assessed adult smoking: ��Think of the two (2) adults that you spend the most time with. How many of them smoke cigarettes every day or most days?�� Response options included none or 0, 1 of them, and 2 of them.

Reduced estimated glomerular filtration rate (eGFR), the primary

Reduced estimated glomerular filtration rate (eGFR), the primary measure used to define CKD (eGFR<60 ml/min/1.73 m2) [4], is associated with an increased risk of cardiovascular morbidity and mortality [5], acute kidney injury [6], and end stage renal disease (ESRD) [6], [7]. Using genome-wide association studies (GWAS) in Tubacin clinical predominantly population-based cohorts, we and others have previously identified more than 20 genetic loci associated with eGFR and CKD [8]�C[11]. Although most of these genetic effects seem largely robust across strata of diabetes or hypertension status [9], evidence suggests that some of the loci such as the UMOD locus may have heterogeneous effects across these strata [11].

We thus hypothesized that GWAS in study populations stratified by four key CKD risk factors – age, sex, diabetes or hypertension status – may permit the identification of novel eGFR and CKD loci. We carried this out by extending our previous work [9] to a larger discovery sample of 74,354 individuals with independent replication in additional 56,246 individuals, resulting in a total of 130,600 individuals of European ancestry. To assess for potential heterogeneity, we performed separate genome-wide association analyses across strata of CKD risk factors, as well as in a more extreme CKD phenotype. Results Meta-analyses of GWAS on the 22 autosomes were performed for: 1) eGFR based on serum creatinine (eGFRcrea) and CKD (6,271 cases) in the overall sample, 2) eGFRcrea and CKD stratified by the four risk factors, and 3) CKD45, a more severe CKD phenotype defined as eGFRcrea <45 ml/min/1.

73 m2 in the overall sample (2,181 cases). For the stratified analyses, in addition to identifying loci that were significant within each stratum, we performed a genome-wide comparison of the effect estimates between strata of the four risk factors. A complete overview of the analysis workflow is given in Figure S1. All studies participating in the stage 1 discovery and stage 2 replication phases are listed in Tables S1 and S2. The characteristics of all stage 1 discovery samples by study are reported in Table S3, and information on study design and genotyping are reported in Table S4. Results of the eGFRcrea analyses are summarized in the Manhattan and quantile-quantile plots reported in Figures S2 and S3. A total of 21 SNPs from the discovery stage were carried forward for replication in an independent set of 56,246 individuals Anacetrapib (Tables S5 and S6).


selleck chem The dynamic patterns of the natural progression of ACHBLF could help determine appropriate medical interventions and/or liver transplantation as well as the best timing for liver transplantation. Acknowledgments This study was supported by the Natural Science Fund of Guangdong province (No. S2012010009084), the New Teacher Fund of the Ministry of Education (No. 20120171120103), and the National Grand Program on Key Infectious Diseases (AIDS and viral hepatitis), China (No. 2012ZX10002007). We thank all HBV-infected individuals in this study. This manuscript was edited and proofread by Medjaden Bioscience Limited.
AIM: To investigate the usefulness of transient elastography by Fibroscan (FS), a rapid non-invasive technique to evaluate liver fibrosis, in the management of chronic hepatitis B virus (HBV) carriers.

METHODS: In 297 consecutive HBV carriers, we studied the correlation between liver stiffness (LS), stage of liver disease and other factors potentially influencing FS measurements. In 87 chronic hepatitis B (CHB) patients, we monitored the FS variations according to the spontaneous or treatment-induced variations of biochemical activity during follow-up. RESULTS: FS values were 12.3 �� 3.3 kPa in acute hepatitis, 10.3 �� 8.8 kPa in chronic hepatitis, 4.3 �� 1.0 kPa in inactive carriers and 4.6 �� 1.2 kPa in blood donors. We identified the cut-offs of 7.5 and 11.8 kPa for the diagnosis of fibrosis �� S3 and cirrhosis respectively, showing 93.9% and 86.5% sensitivity, 88.5% and 96.3% specificity, 76.7% and 86.7% positive predictive value (PPV), 97.3% and 96.

3% negative predictive value (NPV) and 90.1% and 94.2% diagnostic accuracy. At multivariate analysis in 171 untreated carriers, fibrosis stage (t = 13.187, P < 0.001), active vs inactive HBV infection Carfilzomib (t = 6.437, P < 0.001), alanine aminotransferase (ALT) (t = 4.740, P < 0.001) and HBV-DNA levels (t = -2.046, P = 0.042) were independently associated with FS. Necroinflammation score (t = 2.158, > 10/18 vs �� 10/18, P = 0.035) and ALT levels (t = 3.566, P = 0.001) were independently associated with LS in 83 untreated patients without cirrhosis and long-term biochemical remission (t = 4.662, P < 0.001) in 80 treated patients. During FS monitoring (mean follow-up 19.9 �� 7.1 mo) FS values paralleled those of ALT in patients with hepatitis exacerbation (with 1.2 to 4.4-fold increases in CHB patients) and showed a progressive decrease during antiviral therapy. CONCLUSION: FS is a non-invasive tool to monitor liver disease in chronic HBV carriers, provided that the pattern of biochemical activity is taken into account. In the inactive carrier, it identifies non-HBV-related causes of liver damage and transient reactivations.

e , ��decline to answer��) responses to self-report instruments i

e., ��decline to answer��) responses to self-report instruments included in the analyses were imputed using best-set regression (StataCorp, 2009). All variables had less than 5% of data imputed. A variable reflecting never the number of imputed variables for each participant was included in all multivariate models to adjust for the impact that imputation may have had on the results. Statistical Analyses Baseline characteristics of participants were compared by the study arm using chi-square or t tests depending on the measurement��s scale. Logistic regression models were used to estimate the odds of quitting for the intervention versus control groups.

Analyses were conducted in two ways: intent-to-treat (ITT) such that all randomized individuals were included in the analysis (all participants lost to follow-up were assumed to still be smoking) and per-protocol analysis such that only those who completed the follow-up measures were included in the analysis. Models were adjusted for additional factors that may influence smoking outcomes in this population (denoted as ��aOR�� for adjusted odds ratio). Because of their influence on cessation rates (Centers for Disease Control and Prevention, 2001; Nordstrom et al., 2000), biological sex and baseline intensity of smoking were examined as effect modifiers. Given the study��s outreach focus to enroll young adults outside of higher education settings and those of minority race, these characteristics also were tested for effect modification. RESULTS As shown in Figure 1, 1,916 people expressed interest in participating, 585 (31%) of whom appeared eligible based upon the online screener form.

Of these 585, contact was not made with 49% (n = 284). Fifteen percent (n = 90) declined to participate: reasons included the incentives being too low and not having enough time to engage with the program. Seventy percent of eligible participants (n = 211) consented to participate and were randomized into the study. Forty seven of these participants did not complete the online baseline survey and therefore did not begin receiving text messages. The software program did not retain the randomization assignment of participants who did not begin to receive text messages. The randomization assignment of these 47 participants could not be recovered, precluding their inclusion in the ITT analyses. Thus, the final sample size (i.

e., the number of participants randomized and received at least one program message) was n = 164: 101 in the intervention and 63 in the control groups. Of the 1,331 ineligible participants, not seriously thinking about quitting in the next 30 days was the most Entinostat common reason (see Table A1). Figure 1. Stop My Smoking USA randomized controlled trial consort diagram. Four-week follow-up was conducted between April and June; and 3-month follow-up between June and August, 2011. Eighty-seven percent of participants responded at 4 weeks postquit and 80% at 3 months postquit.

0 �� 7 9 ng ml?1 (Equations 2] and 3]), significantly better than

0 �� 7.9 ng ml?1 (Equations 2] and 3]), significantly better than the baseline model (Equation 1]) (��OF > ?33, P = 9.2 �� 10?9). No influence of HSA using either linear more info or non-linear binding was observed (��OF> +26 compared with the baseline model) and Kd was much higher (4580 �� 144 ng ml?1). The interaction model (Equation 6] Appendix 1]) improved significantly the prediction of Cu compared with the model including non-linear binding to AGP solely (��OF = ?7) with a Kd for AGP and HSA of, respectively, 421 and 23 300 ng ml?1. Pharmacokinetic modelling Several models were then sequentially elaborated to describe the relationship between Cu and Ctot. The baseline model (Equation 1]) predicted a free fraction (Equation 1]) of 3.5% with a small non-significant inter-patient variability of 11%.

Allowing for a correlation between the residual errors for Cu and Ctot resulted in a marked improvement of the model fit (��OF = ?68.7, P = 1.1 �� 10?16), The predictions of Cu based on our previous non-linear model (Equation 3]) [17], using a fixed Kd of 90 ng ml?1, are presented in Figure 2A. This model was not able to predict imatinib free concentrations observed in our population, yielding a major bias of ?70% (MPE range ?66/?71%), and a poor precision of 250%. A difference of about three-fold was apparent between measured and predicted free imatinib concentrations. Refinement of the model by allowing Kd to be estimated markedly improved the fit (��OF = ?166, P = 5.5 �� 10?38), finally yielding a Kd of 319 ng ml?1. This model described the data much better that the one assuming a constant free fraction (Equation 1], ��OF = ?37.

7, P = 8.3 �� 10?10). This approach drastically decreased the bias (from ?70 to 4%, with 95% CI ?2, 10%) and improved the precision (43%) of the model, showing a good correlation between measured and estimated free imatinib concentrations (Figure 2B). A reduced model using a linear (Equation 2]) function of AGP described the data worse than the non-linear model (��OF = +12.9). The hyperbolic dependency of imatinib observed free fractions on plasma AGP concentrations depicted in Figure 3 is in accordance with this finding. Figure 2 (A)Individual predicted (predicted by Widmer et al. equation [17]) vs. measured free concentrations of imatinib. (B) Individual predictions (predicted by our final model, see text) vs. measured free imatinib concentrations. Anacetrapib The dotted line represents … Figure 3 Imatinibfree fractions vs.

As noted in Table 3, the ns and the ORs for the groups were as fo

As noted in Table 3, the ns and the ORs for the groups were as follows: non/low tobacco users and non/low marijuana selleck products users (n = 257, OR = 10.1; p < .001), chronic tobacco users and maturing-out marijuana users (n = 15, OR = 5.6; p < .001), late onset tobacco users and late onset marijuana users (n = 30, OR = 3.5; p < .001), and chronic tobacco users and chronic marijuana users (n = 16, OR = 4.8; p < .01). More than 66% of participants were in a comorbid trajectory pair with most in the non/low tobacco use and non/low marijuana use trajectory pair. Correlates of comorbidity Table 3 shows ORs when a single risk variable was controlled. The ORs of the comorbidity of the pairs of trajectories of tobacco and marijuana use were reduced with control on many of the psychosocial risk variables.

For the non/low tobacco use and non/low marijuana use trajectory pair, no single risk factor reduced the OR below 7.2 (OR reduced from 10.1 to 7.2, t = 3.0, p < .01). For the non/low tobacco use and non/low marijuana use trajectory pair, the greatest reduction was obtained by controlling for Delinquency. For each of the four comorbid trajectory pairs, controlling for Peer Marijuana Use generated the largest or next largest reduction in OR. The chronic tobacco use and chronic marijuana use trajectory pair controlling for Peer Tobacco use had the largest reduction in OR. For the chronic tobacco use and chronic marijuana use trajectory group, all of the psychosocial risk variables except the demographic variables (i.e., Gender and Ethnicity) significantly reduced the ORs (see Table 3).

Table 4 presents the results of six sets of multivariate logistic regression analyses (one for each row of the table) for the four selected pairs of tobacco and marijuana use trajectory groups (see Table 4). The leftmost column specifies the variables controlled for. As shown in Table 4, for the non/low tobacco use and non/low marijuana use trajectory pair, which included more than 50% of the sample, no set of risk factors reduced the OR below 6.6. Controlling for the set of externalizing personality attributes produced the greatest reduction in OR for the non/low tobacco use and non/low marijuana use trajectory pair and the late onset tobacco use and late onset marijuana use trajectory pair. It also reduced the OR significantly for the other pairs.

Controlling for the set of peer variables had the greatest reduction in OR for the chronic tobacco use and maturing-out marijuana use trajectory pair and the chronic tobacco use and chronic marijuana use trajectory pair. Controlling for the internalizing personality attributes AV-951 significantly reduced the OR only for the chronic tobacco use and chronic marijuana use trajectory pair. Controlling for all the variables at once significantly reduced the OR for each of the four comorbid trajectory pairs.