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ObjectiveTo report on demographic and tobacco use correlates of cessation behaviours across tobacco products (cigarettes, electronic nicotine delivery systems (ENDS), cigars, hookah and smokeless tobacco) among the US population.DesignData were drawn from the first three waves (2013–2016) of the Population Assessment of Tobacco and Health Study, a nationally representative, longitudinal cohort study of US youth (ages 12–17) and adults (ages 18+) . Past 30-day (P30D) tobacco users at Wave 1 (W1) or Wave 2 (W2) were included (n=1374 youth; n=14 389 adults). Generalised estimating equations were used to evaluate the association between demographic and tobacco use characteristics at baseline, with cessation behaviours at follow-up (discontinuing use, attempting to quit, quitting), over two 1-year periods (W1–W2, W2–Wave 3).ResultsAmong adult users of each type of tobacco product, frequency of use was negatively associated with discontinuing use. Among adult cigarette smokers, non-Hispanic white smokers, those with lower educational attainment and those with lower household income were less likely to discontinue cigarette use; ENDS use was positively associated with making quit attempts but was not associated with cigarette quitting among attempters; smokeless tobacco use was positively associated with quitting among attempters; tobacco dependence was negatively associated with quitting among attempters. Among youth cigarette smokers, tobacco dependence was negatively associated with making quit attempts.DiscussionDemographic correlates of tobacco cessation behaviours underscore tobacco use disparities in the USA. Use of ENDS and use of smokeless tobacco products are positively associated with some adult cigarette cessation behaviours.

ObjectiveThis study examines sociodemographic and tobacco use correlates of reuptake and relapse to tobacco use across a variety of tobacco products (cigarettes, electronic nicotine delivery systems, cigars, hookah and smokeless tobacco) among the US population.DesignData were drawn from the first three waves (2013–2016) of the Population Assessment of Tobacco and Health Study, a nationally representative, longitudinal cohort study of US youth (ages 12–17) and adults (ages 18+). Reuptake (past 30-day use among previous tobacco users) and relapse (current use among former established users; adults only) were examined among previous users of at least one type of tobacco product at Wave 1 (W1) or Wave 2 (W2) (n=19 120 adults, n=3039 youth). Generalised estimating equations were used to evaluate the association between demographic and tobacco use characteristics at baseline, with reuptake/relapse at follow-up, over two 1-year periods (W1–W2 and W2–Wave 3).ResultsAny tobacco product reuptake occurred in 7.8% of adult previous users and 30.3% of youth previous users. Correlates of any tobacco reuptake included being male, non-Hispanic black and bisexual in adults, but race and sexual orientation were not consistent findings in youth. Among recent former users, relapse rates were greater (32.9%). Shorter time since last use and greater levels of tobacco dependence showed the strongest association with any tobacco relapse.DiscussionContinued clinical and public health efforts to provide adults with tools to cope with tobacco dependence symptoms, especially within the first year or two after quitting, could help prevent relapse.

Deep learning (DL)-based retinal image quality assessment (RIQA) algorithms have been gaining popularity, as a solution to reduce the frequency of diagnostically unusable images. Most existing RIQA tools target retinal conditions, with a dearth of studies looking into RIQA models for optic nerve head (ONH) disorders. The recent success of DL systems in detecting ONH abnormalities on color fundus images prompts the development of tailored RIQA algorithms for these specific conditions. In this review, we discuss recent progress in DL-based RIQA models in general and the need for RIQA models tailored for ONH disorders. Finally, we propose suggestions for such models in the future.

Background Despite efforts to reduce disadvantages across society, widening health disparities have been observed in Minnesota. This research examined whether observed declines in state-wide smoking prevalence were experienced equally by all adults with varying educational attainment. Methods Serial cross-sectional data from the 2003, 2007, 2010 and 2014 Minnesota Adult Tobacco Survey (MATS) were analyzed. Weighted regression analyses for smoking status, time to first cigarette, cigarettes per day and non-cigarette other tobacco products (OTP) were conducted across education levels. Results Controlling for age and gender, a decreased rate of smoking among high and middle education groups was offset by an increase in the low education group. Dependence (time to first cigarette) was twice as high in the lowest education group compared to highest, yet dependence did not decline over time for any group. There was a decline in cigarettes per day in all education groups, but an increase in OTP use in the lowest and middle education groups. Conclusions Given existing smoking disparities, novel efforts are urgently needed. Complementing known population-level strategies with community and individual-level approaches will be necessary to eliminate the widening gap in smoking disparities and to end the burden of tobacco-related disease.

Background and Aim: Gynura segetum (Tusanqi or Jusanqi) is widely used in China as a herbal remedy, however, it has often been associated with hepatic sinusoidal obstruction syndrome (HSOS). Its extent in inducing hepatotoxicity is not sufficiently understood. Hence, we aimed to identify the characteristic features of Gynura segetum associated HSOS. Methods: A total of 64 patients diagnosed with HSOS induced by gynura segetum were enrolled from eight Chinese tertiary care hospitals between 2008 and 2018. General information regarding diagnosis, disease history, suspected drug use, symptoms and signs, biochemical index, imaging data, liver histology, treatment methods, severity and prognosis were collected and analyzed. Results: The mean age of the enrolled patients were 58.07±11.44 years. Male patients accounted for 64.1% of HSOS patients. The median latency period was 75 days. The number of patients with a definite diagnosis from the eight hospitals was 5 (7.81%), with a misdiagnosis rate of 92.18%. Hepatomegaly, splenomegaly, ascites and lower limbs edema were present in 89.1%, 76.6%, 81.3% and 43.8% of the patients, respectively. The imaging characteristic changes were liver parenchyma echo thickening, uneven density, and hepatic vein stenosis and occlusion. Liver biopsies had characteristic pathological changes. Except for ALT and D-Dimer, liver function and coagulation index at admission and before discharge were not significantly different (p>0.05). The 6-month mortality rate was 77.55%, with upper-gastrointestinal bleeding being the leading cause of death (42.11%). The second leading cause of death was a secondary infection (36.84%), while the third was hepatorenal syndrome (21.05%). Conclusion: Gynura segetum related HSOS often presents as progressive hepatic congestion, portal hypertension and liver failure, and has a high mortality and misdiagnosis rate.

The era of big data and precision medicine has led to accumulation of massive datasets of gene expression data and clinical information of patients. For a new patient, we propose that identification of a highly similar reference patient from an existing patient database via similarity matching of both clinical and expression data could be useful for predicting the prognostic risk or therapeutic efficacy.Here, we propose a novel methodology to predict disease/treatment outcome via analysis of the similarity between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). Our analyses revealed that Euclidean distance rather correlation distance measure was effective in defining an unbiased similarity measure calculated between two PSVs.We implemented our methods to high-grade serous ovarian cancer (HGSC) based on a 36-mRNA predictor that was previously shown to stratify patients into 3 distinct prognostic subgroups. We studied and revealed that patient's age, when converted into binary variable, was positively correlated with the overall risk of succumbing to the disease. When applied to an independent testing dataset, the inclusion of age into the molecular predictor provided more robust personalized prognosis of overall survival correlated with the therapeutic response of HGSC and provided benefit for treatment targeting of the tumors in HGSC patients.Finally, our method can be generalized and implemented in many other diseases to accurately predict personalized patients' outcomes. ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version

As cigarette smoking rates decline, an important policy question is whether increasing cigarette taxes will continue to encourage smoking cessation. We tested this question following recent tobacco tax increases. Data were from the Minnesota Adult Tobacco Survey, a serial cross-sectional telephone survey conducted statewide, and was limited to past-year cigarette smokers in 2010 (n = 1029) and 2014 (n = 1382). Weighted estimates were calculated of the prevalence of past year smokers, smokers who attempted to quit smoking, and those who successfully quit by demographics, tobacco use, use of evidence-based cessation assistance to quit, and smoker perceptions of the tax increases. Among past year smokers, almost 60% reported a quit attempt in both years, 12.8% successfully quit in 2010 and 15.6% in 2014. Although older age, daily smoking, mean cigarettes per day, and more days of e-cigarette use, were associated with quit attempts in unadjusted models, only the perceived tax increase effect (AOR = 8.9; 95% CI 6.3–12.5) and low nicotine dependence (AOR = 1.9, 95% CI 1.3–2.7) were associated with making a quit attempt in adjusted models. Successful 12-month quits were predicted by college education (AOR = 3.2, 95% CI 1.3–7.8), the use of cessation support (AOR = 2.1, 95% CI 1.3–3.6), and reporting the tax increase helped maintain a quit (AOR = 12.3, 95% CI 7.5–20.1). These findings suggest that a large tax increase is effective in promoting quitting even in the presence of strong tobacco control measures such as indoor smoking bans and other smoking restrictions, mass media campaigns, and universal access to cessation support.

Visual Abstract, Highlights • Targeted profiling of ceramides identified a 12-ceramide plasma signature that predicted 12-month cardiovascular death, MI, and stroke in 2 prospective cohorts of AMI patients. • Among coronary artery bypass grafting patients, plasma ceramides were higher in those with recent AMI compared with those without recent acute MI. • Analysis of rat ischemic myocardium revealed a consistent increase in ceramide levels and overexpression of 3 enzymes in ceramide biosynthesis., Summary We identified a plasma signature of 11 C14 to C26 ceramides and 1 C16 dihydroceramide predictive of major adverse cardiovascular events in patients with acute myocardial infarction (AMI). Among patients undergoing coronary artery bypass surgery, those with recent AMI, compared with those without recent AMI, showed a significant increase in 5 of the signature’s 12 ceramides in plasma but not simultaneously-biopsied aortic tissue. In contrast, a rat AMI model, compared with sham control, showed a significant increase in myocardial concentrations of all 12 ceramides and up-regulation of 3 ceramide-producing enzymes, suggesting ischemic myocardium as a possible source of this ceramide signature.

Employment problems are common among low-income, substance abusing women. The present study links an empirically developed quantitative measure of gender-sensitive (GS) substance abuse treatment to employment outcomes among substance abusing women ( N = 5,109) treated in 13 mixed-gender intensive inpatient programs (IIPs) in the Washington State. Hierarchical linear models were used to test the relationship between GS treatment and subsequent employment. Propensity scores and receipt of public assistance were used to control for the preexisting differences among women. Men’s employment outcomes were used to control for potential confounding at the program level. The study found that women treated in more (vs. less) GS treatment programs were more likely to be employed 12 months after treatment admission, though not for the hypothesized 24 months. Treatment completion did not affect the relationship between GS treatment and employment. Findings point to recent progress in tailoring generic substance abuse treatment to women’s needs.

The field of personalized disease prognosis, risk prediction and disease management has always attracted the attention of scientists and clinicians worldwide. Translating current clinical research and emerging genome-scale molecular biomarkers of ovarian and other cancers is yielding promising new insights into predictive signatures and potential clinical targets, moving treatment in favor of more personalized therapeutic approaches. Integrative genomics, big data analysis and the disease prediction modelling have become the major driving factors in understanding how to reproducibly stop a disease for individual patients - a goal of precision medicine. In clinical practice, prognostic and predictive factors could be measured as continuous or discrete variables. Here, we proposed a novel methodology to predict disease/treatment outcome via analysis of the similarities between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). High-grade serous ovarian carcinoma (HGSC) is the most prevalent ovarian cancer, and is one of the most lethal gynecological diseases in the world today. Many molecular signatures for prognostic and disease prediction of HGSC have been proposed, but no been implemented in clinical practice yet. In our previous study, the highly-confidence prognostic classifier, comprising 36 mRNAs that can stratify the HGSC patients into low, intermediate or high-risk subgroups with significantly distinct overall survival outcomes and sensitivity to post-surgery chemotherapy, has been proposed [1]. In this work, we used this classifier as the discriminative model of prognosis of HGSC patients. We implemented our methods to personalized prognosis of HGSC patients of TCGA database based on our molecular predictor [1]. Our results revealed that patient's age, when converted into discrete binary values, was positively correlated with the overall risk of succumbing to the disease and can be used as independent prognostic factor. The inclusion of age into the molecular survival predictor provided more robust personalized prognosis of overall survival when applied to an independent testing metadata composing 359 HGSC patients. In summary, we propose that our personalized prognosis methods and results after including age as prognostic factor could provide clinical benefits in the context of personalized prognosis and therapeutic assignment of HGSC patients. Our method can be generalized and implemented in many other diseases to accurately predict personalized patients' outcomes. [1]. Tang Z, Ow GS, Thiery JP, Ivshina AV and Kuznetsov VA. Int J Cancer. 2013; 134(2):306-318. Citation Format: Ghim Siong Ow, Zhiqun Tang, Anna Vladimirovna Ivshina, Vladimir Andreevich Kuznetsov. Big data and computational biology method for personalized prognosis of high-grade serous ovarian carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr A75.

Gender-sensitive (GS) substance abuse treatment services have emerged in response to the multidimensional profile of problems that women display upon admission to substance abuse treatment. The present study examines the extent to which treatment programs vary in GS programming for women in real-world mixed-gender treatment settings, where most women are treated.Data were collected through site visits using semi-structured interviews with program directors, clinical directors, and counselors in 13 mixed-gender treatment programs from Washington State. Rasch modeling techniques were used to analyze the data.Naturally occurring variation was revealed within and across the treatment programs, and demonstrated that reliable measures of three GS domains (Grella, 2008) can be constructed despite a small number of programs.This is the first study to quantify GS treatment for substance abusing women. The identified treatment services and practices and the way they clustered together to form scales have practical implications for researchers, service providers, clinicians, and policy makers. The scales can be used to study treatment outcomes and to evaluate the effectiveness, cost-effectiveness, and cost-benefit of GS programming for women.