The inflammatory biomarker YKL-40 is elevated in the serum, but not the sputum, of E-cigarette users.

METHODS: We conducted a cross-sectional study of adults between 18 and 55 years old. Inclusion criteria were: exclusive e-cigarette use or cigarette smoking for ≥ 1 year or no history of tobacco use. Participants with a history of pulmonary illness, atopy, medications (except birth control pills), marijuana, and illegal substance use were excluded. Custom Multiplex ELISA was used to measure YKL-40 and other biomarker levels in the serum and induced sputum of the participants. Multivariable linear regression was used to compare the levels of YLK-40 in healthy participants, e-cigarette, and cigarette users after adjusting for age, sex, and BMI. RESULTS: We recruited 20 healthy controls, 23 cigarette smokers, and 22 exclusive e-cigarette users. Serum YKL-40 (ng/ml) was significantly higher in e-cigarette users (Median 21.2 [IQR 12.1-24.0] ng/ml) when compared to controls (12.2 [IQR 8.7-18.1] ng/ml, p = 0.016) but comparable to cigarette smokers (21.6 [IQR 11.62-51.7] ng/ml, p = 0.31). No significant differences were found in the serum or sputum of the other biomarkers tested. CONCLUSION: The inflammatory biomarker, YKL-40 is elevated in the serum but not the sputum of e-cigarette users with no reported pulmonary disease. Further research is necessary to characterize this association.

Prospective predictors of electronic nicotine delivery system initiation in tobacco naive young adults: A machine learning approach.

The use of electronic nicotine delivery systems (ENDS) is increasing among young adults. However, there are few studies regarding predictors of ENDS initiation in tobacco-naive young adults. Identifying the risk and protective factors of ENDS initiation that are specific to tobacco-naive young adults will enable the creation of targeted policies and prevention programs. This study used machine learning (ML) to create predictive models, identify risk and protective factors for ENDS initiation for tobacco-naive young adults, and the relationship between these predictors and the prediction of ENDS initiation. We used nationally representative data of tobacco-naive young adults in the U.S drawn from the Population Assessment of Tobacco and Health (PATH) longitudinal cohort survey. Respondents were young adults (18-24 years) who had never used any tobacco products in Wave 4 and who completed Waves 4 and 5 interviews. ML techniques were used to create models and determine predictors at 1-year follow-up from Wave 4 data. Among the 2,746 tobacco-naive young adults at baseline, 309 initiated ENDS use at 1-year follow-up. The top five prospective predictors of ENDS initiation were susceptibility to ENDS, increased days of physical exercise specifically designed to strengthen muscles, frequency of social media use, marijuana use and susceptibility to cigarettes. This study identified previously unreported and emerging predictors of ENDS initiation that warrant further investigation and provided comprehensive information on the predictors of ENDS initiation. Furthermore, this study showed that ML is a promising technique that can aid ENDS monitoring and prevention programs.

Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy.

Diffusion-weighted magnetic resonance imaging data obtained early in the course of therapy can be used to estimate tumor proliferation rates, and the estimated rates can be used to predict tumor cellularity at the conclusion of therapy. Six patients underwent diffusion-weighted magnetic resonance imaging immediately before, after one cycle, and after all cycles of neoadjuvant chemotherapy. Apparent diffusion coefficient values were calculated for each voxel and for a whole tumor region of interest. Proliferation rates were estimated using the apparent diffusion coefficient data from the first two time points and then used with the logistic model of tumor growth to predict cellularity after therapy. The predicted number of tumor cells was then correlated to the corresponding experimental data. Pearson's correlation coefficient for the region of interest analysis yielded 0.95 (P = 0.004), and, after applying a 3 × 3 mean filter to the apparent diffusion coefficient data, the voxel-by-voxel analysis yielded a Pearson correlation coefficient of 0.70 ± 0.10 (P < 0.05).

Electronic Cigarettes Associated With Incident and Polysubstance Use Among Youth.

PURPOSE: Electronic cigarette (e-cigarette) use has increased exponentially among the youth in the United States and may increase the incidence of substance use. METHODS: Youth participants (12-17 years) were surveyed through the Population Assessment of Tobacco and Health study over a three-year time period. Youth with any baseline substance use or diagnosis of an attention deficit disorder were excluded from the analysis. Multivariable logistic regressions were used to assess the association between e-cigarette use at Wave 1 and incident substance use (marijuana, painkillers, sedatives, or tranquilizers and Ritalin/Adderall) and polysubstance use at Wave 2 or 3, and marijuana use in the electronic nicotine device at Wave 3. RESULTS: Baseline ever e-cigarette users who had no history of marijuana, nonprescribed drugs and illicit substance use in Wave 1 had increased odds of reporting incident use of marijuana (odds ratio 2.59, 95% confidence interval: 1.90-3.52), nonprescribed Ritalin/Adderall use (1.89, 1.09-3.28), or polysubstance use (2.09, 1.43-3.05) in Wave 2 or 3 compared to never e-cigarette users. They were also more likely to report use of marijuana in the electronic nicotine product (2.26, 1.56-3.27) in Wave 3 compared to never e-cigarette users. There was no statistically significant association between baseline e-cigarette use and incident use of painkillers, sedatives, or tranquilizers in Wave 2 or 3 (1.21, .79-1.87). CONCLUSIONS: E-cigarette use is associated with incident use of marijuana, marijuana in electronic nicotine devices, Ritalin/Adderall, and polysubstance use but not painkillers, sedatives, or tranquilizers. Results indicate that e-cigarettes are associated with subsequent additional risky health behaviors in youth.

Biomarkers of Toxicant Exposure and Inflammation Among Women of Reproductive Age Who Use Electronic or Conventional Cigarettes.

Background: Electronic cigarettes (e-cigarettes) generally have a more favorable toxicant profile than conventional cigarettes; however, limited information exists for women of reproductive age (WRA). Our aim was to compare biomarkers of toxicant exposure, inflammation, and oxidative stress among WRA who self-report exclusive e-cigarette use, exclusive cigarette smoking, or never tobacco use (controls). Methods: Multivariable linear regression models were used to compare the geometric means of urinary biomarkers of toxicant exposure and their metabolites, serum markers of inflammation [highly sensitive C-reactive protein, soluble intercellular adhesion molecule (sICAM), interleukin 6, fibrinogen], and a measurement of oxidative stress [prostaglandin F2a-8-isoprostane (F2PG2a)] among WRA from the Population Assessment of Tobacco and Health survey. Results: E-cigarette users had higher levels of lead, tobacco-specific nitrosamines, nicotine metabolites, and some volatile organic compounds (VOCs) than controls. Except for cadmium and lead, e-cigarette users had lower levels of the analyzed urinary toxicant biomarkers compared with cigarette smokers. Cigarette smokers had higher levels of all the biomarkers of toxicant exposure than controls. There were no significant differences in the levels of markers of inflammation and oxidative stress between e-cigarette users and controls. E-cigarette users and controls had lower levels of sICAM and F2PG2a than cigarette smokers. Conclusion: WRA who use e-cigarettes had lower levels of some of the evaluated urinary biomarkers of toxicant exposure and serum biomarkers of inflammation and oxidative stress than those who smoke cigarettes, but higher lead, nicotine metabolites, and some VOCs than controls, which can increase health risks.

E-Cigarette Use in Young Adult Never Cigarette Smokers with Disabilities: Results from the Behavioral Risk Factor Surveillance System Survey.

Young adult never cigarette smokers with disabilities may be at particular risk for adopting e-cigarettes, but little attention has been paid to these people. This study examines the associations between different types of disability and e-cigarette use in this population. Young adult never-smokers from the 2016-2017 Behavioral Risk Factor Surveillance System (BRFSS) survey who were either never or current e-cigarette users (n = 79,177) were selected for the analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select confounders for multivariable logistic regression models. Multivariable logistic regression models were used to determine the associations between current e-cigarette use and different types of disability after incorporating BRFSS survey design and adjusting for confounders. Young adult never-smokers who reported any disability had increased odds (OR 1.44, 95% CI 1.18-1.76) of e-cigarette use compared to those who reported no disability. Young adult never-smokers who reported self-care, cognitive, vision, and independent living disabilities had higher odds of e-cigarette use compared to those who reported no disability. There was no statistically significant difference in the odds of e-cigarette use for those reporting hearing and mobility disabilities compared to those who reported no disability. This study highlights the need for increased public education and cessation programs for this population.

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach.

E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18-34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis (n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

Adult E-Cigarettes Use Associated with a Self-Reported Diagnosis of COPD.

The use of electronic cigarettes (e-cigarettes) has increased in the US, but little is known about the effects of these products on lung health. The main purpose of this study was to examine the association between e-cigarette use and a participant's report of being diagnosed with chronic obstructive pulmonary disease (COPD) in a nationally representative sample of adults. Methods: The first wave of the Population Assessment of Tobacco and Health (PATH) survey adult data was used (N = 32,320). Potential confounders between e-cigarette users and non-users were balanced using propensity score matching. Odds ratios (OR) were calculated to examine the association between e-cigarette use and COPD in the propensity-matched sample, the entire sample, different age groups, and in nonsmokers. Replicate weights and balanced repeated replication methods were utilized to account for the complex survey design. Results: Of the 3642 participants who met the criteria for e-cigarette use, 2727 were propensity matched with 2727 non e-cigarette users. In the propensity-matched sample, e-cigarette users were more likely to report being diagnosed with COPD (OR 1.43, 95% confidence interval [CI] 1.12-1.85) than non-e-cigarette users after adjusting for confounders. The result was similar in the entire sample and in the different age subgroups. Among nonsmokers, the odds of reporting a COPD diagnosis were even greater among e-cigarette users (OR 2.94, 95% CI 1.73-4.99) compared to non-e-cigarette users. Conclusion: Our findings demonstrate that e-cigarette use was associated with a reported diagnosis of COPD among adults in the US. Further research is necessary to characterize the nature of this association and on the long-term effects of using e-cigarettes.

Impact of Cirrhosis on Pneumonia-Related Outcomes in Hospitalized Older Veterans.

BACKGROUND: Prior research has demonstrated high mortality rates in patients with cirrhosis who contract bacterial infections. The purpose of our study was to explore clinical outcomes such as 90-day mortality, rehospitalization, and intensive care unit (ICU) admission in older veterans with pneumonia and cirrhosis. METHODS: We conducted a retrospective cohort study of hospitalized patients with community-acquired pneumonia at any Departments of Veterans Affairs (VA) hospital over a 10-year period. We included patients 65 years or older who consistently received VA care and who were diagnosed with community-acquired pneumonia. There were 103,997 patients who met the inclusion criteria, and 1,246 patients with cirrhosis. We used multilevel regression models to examine the association between cirrhosis and the outcomes of interest after controlling for potential confounders. RESULTS: Cirrhosis was associated with significantly increased odds of 90-day mortality (odds ratio 1.79, 95% confidence interval, 1.57-2.04). There were also significantly increased odds of rehospitalization within 90-days (1.30, 1.16-1.47). No significant association was found with ICU admission (1.00, 0.83-1.19). CONCLUSIONS: We found an association between cirrhosis and 90-day mortality and rehospitalization in older patients with pneumonia. We suggest that physicians should carefully monitor patients with cirrhosis who develop pneumonia.

Association of atypical antipsychotics and mortality for patients hospitalised with pneumonia.

INTRODUCTION: Atypical antipsychotics are commonly used in patients with psychiatric conditions and dementia. They are also frequently used in patients being admitted with pneumonia; however, there are few safety data. The purpose of this study was to examine whether atypical antipsychotic use prior to admission is associated with increased mortality in patients with pneumonia. METHODS: We conducted a retrospective cohort study of hospitalised patients with pneumonia over a 10-year period. We included patients 65 years or older and hospitalised with pneumonia. For our primary analysis, we used propensity score matching to balance confounders between atypical antipsychotic users and nonusers. RESULTS: There were 102 897 patients and 5977 were taking atypical antipsychotics. After matching there were 5513 users and 5513 nonusers. Atypical antipsychotic use was associated with increased odds of 30-day (OR 1.20, 95% CI 1.11-1.31) and 90-day mortality (1.19, 1.09-1.30). CONCLUSION: In patients 65 years or older that are hospitalised with pneumonia, we found an association between atypical antipsychotic use and increased odds of mortality. This was particularly pronounced for patients with pre-existing psychiatric or cardiac conditions. We suggest closely monitoring patients who use these medications and minimising their use in older adult patients.

E-cigarette use is associated with a self-reported diagnosis of prediabetes in never cigarette smokers: Results from the behavioral risk factor surveillance system survey.

BACKGROUND: The use of e-cigarettes is increasing in the US but there is still a paucity of research on the metabolic effects of e-cigarette use. The goal of this work was to determine the association between e-cigarette use and self-reported prediabetes in adult never cigarette smokers. METHOD: The 2017 cross sectional Behavioral Risk Factor Surveillance System (BRFSS) survey data was used for the analysis. Current e-cigarette users reported daily or someday use of e-cigarettes and former e-cigarette users reported no current use of e-cigarettes. Participants who reported a history of diabetes, gestational prediabetes/ diabetes were excluded. Odds ratios were calculated to determine the association between e-cigarette use and self-reported prediabetes in never cigarette smokers after adjusting for potential confounders. RESULTS: There were a total of 154,404 participants that met the inclusion criteria. Of those participants, there were 143,952 never, 1339 current and 7625 former e-cigarette users. Current e-cigarette users had an increased odds of reporting a diagnosis of prediabetes 1.97 (95% CI 1.25-3.10) compared to never e-cigarette users. After stratifying by gender, men and women had an increased odds ratio of reporting a diagnosis of prediabetes 2.36 (95% CI 1.26-4.40) and 1.88 (95% CI 1.00-3.53) respectively when compared to never e-cigarette users. There was no association between former e-cigarette use and a self-reported diagnosis of prediabetes. CONCLUSION: Our findings show that e-cigarette use may be associated with self-reported prediabetes. Further evaluation is needed in prospective studies.

Association between Regular Electronic Nicotine Product Use and Self-reported Periodontal Disease Status: Population Assessment of Tobacco and Health Survey.

Electronic nicotine product use is increasing in the U.S., but few studies have addressed its effects on oral health. The goal of this work was to determine the association between electronic nicotine product use and periodontal disease. Population Assessment of Tobacco and Health adult survey data from 2013⁻2016 (waves 1, 2 and 3) was used for the analysis. Longitudinal electronic nicotine product users used electronic nicotine products regularly every day or somedays in all three waves. Participants with new cases of gum disease reported no history of gum disease in wave 1 but reported being diagnosed with gum disease in waves 2 or 3. Odds ratios (OR) were calculated to determine the association between electronic nicotine product use and new cases of gum disease after controlling for potential cofounders. Compared to never users, longitudinal electronic nicotine product users had increased odds of being diagnosed with gum disease (OR 1.76, 95% Confidence Interval (CI) 1.12⁻2.76) and bone loss around teeth (OR 1.67, 95% CI 1.06⁻2.63). These odds were higher for participants with a history of marijuana and a history of illicit or non-prescribed drug use. Our findings show that e-cigarettes may be harmful to oral health.

Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results.

PURPOSE: The authors propose a method whereby serially acquired DCE-MRI, DW-MRI, and FDG-PET breast data sets can be spatially and temporally coregistered to enable the comparison of changes in parameter maps at the voxel level. METHODS: First, the authors aligned the PET and MR images at each time point rigidly and nonrigidly. To register the MR images longitudinally, the authors extended a nonrigid registration algorithm by including a tumor volume-preserving constraint in the cost function. After the PET images were aligned to the MR images at each time point, the authors then used the transformation obtained from the longitudinal registration of the MRI volumes to register the PET images longitudinally. The authors tested this approach on ten breast cancer patients by calculating a modified Dice similarity of tumor size between the PET and MR images as well as the bending energy and changes in the tumor volume after the application of the registration algorithm. RESULTS: The median of the modified Dice in the registered PET and DCE-MRI data was 0.92. For the longitudinal registration, the median tumor volume change was -0.03% for the constrained algorithm, compared to -32.16% for the unconstrained registration algorithms (p = 8 × 10(-6)). The medians of the bending energy were 0.0092 and 0.0001 for the unconstrained and constrained algorithms, respectively (p = 2.84 × 10(-7)). CONCLUSIONS: The results indicate that the proposed method can accurately spatially align DCE-MRI, DW-MRI, and FDG-PET breast images acquired at different time points during therapy while preventing the tumor from being substantially distorted or compressed.

Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity during Neoadjuvant Chemotherapy.

Diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging (MRI) data of 28 patients were obtained pretreatment, after one cycle, and after completion of all cycles of neoadjuvant chemotherapy (NAC). For each patient at each time point, the tumor cell number was estimated using the apparent diffusion coefficient and the extravascular extracellular (v e) and plasma volume (v p) fractions. The proliferation/death rate was obtained using the number of tumor cells from the first two time points in conjunction with the logistic model of tumor growth, which was then used to predict tumor cellularity at the conclusion of NAC. The Pearson correlation coefficient between the predicted and the experimental number of tumor cells measured at the end of NAC was 0.81 (P = .0043). The proliferation rate estimated after the first cycle of therapy was able to separate patients who went on to achieve pathologic complete response from those who did not (P = .021) with a sensitivity and specificity of 82.4% and 72.7%, respectively. These data provide preliminary results indicating that incorporating readily available quantitative MRI data into a simple model of tumor growth can lead to potentially clinically relevant information for predicting an individual patient's response to NAC.

Simultaneous PET-MRI in oncology: a solution looking for a problem?

With the recent development of integrated positron emission tomography-magnetic resonance imaging (PET-MRI) scanners, new possibilities for quantitative molecular imaging of cancer are realized. However, the practical advantages and potential clinical benefits of the ability to record PET and MRI data simultaneously must be balanced against the substantial costs and other requirements of such devices. In this review, we highlight several of the key areas where integrated PET-MRI measurements, obtained simultaneously, are anticipated to have a significant impact on clinical and/or research studies. These areas include the use of MR-based motion corrections and/or a priori anatomical information for improved reconstruction of PET data, improved arterial input function characterization for PET kinetic modeling, the use of dual-modality contrast agents, and patient comfort and practical convenience. For widespread acceptance, a compelling case could be made if the combination of quantitative MRI and specific PET biomarkers significantly improves our ability to assess tumor status and response to therapy, and some likely candidates are now emerging. We consider the relative advantages and disadvantages afforded by PET-MRI and summarize current opinions and evidence as to the likely value of PET-MRI in the management of cancer.

The integration of quantitative multi-modality imaging data into mathematical models of tumors.

Quantitative imaging data obtained from multiple modalities may be integrated into mathematical models of tumor growth and treatment response to achieve additional insights of practical predictive value. We show how this approach can describe the development of tumors that appear realistic in terms of producing proliferating tumor rims and necrotic cores. Two established models (the logistic model with and without the effects of treatment) and one novel model built a priori from available imaging data have been studied. We modify the logistic model to predict the spatial expansion of a tumor driven by tumor cell migration after a voxel's carrying capacity has been reached. Depending on the efficacy of a simulated cytotoxic treatment, we show that the tumor may either continue to expand, or contract. The novel model includes hypoxia as a driver of tumor cell movement. The starting conditions for these models are based on imaging data related to the tumor cell number (as estimated from diffusion-weighted MRI), apoptosis (from 99mTc-Annexin-V SPECT), cell proliferation and hypoxia (from PET). We conclude that integrating multi-modality imaging data into mathematical models of tumor growth is a promising combination that can capture the salient features of tumor growth and treatment response and this indicates the direction for additional research.

Modeling tumor growth and treatment response based on quantitative imaging data.

We review current approaches to predicting tumor growth and treatment response that combine non-invasive imaging data with mathematical models of cancer progression, and propose some new directions for integrating quantitative imaging measurements with such numerical analyses. Historically, tumor modeling has been described by parameters that are measurable by invasive methods only or in isolated in vitro or ex vivo systems. This limits the practical usefulness of such models because it is not possible to test their predictions experimentally. Recent advances in three-dimensional magnetic resonance imaging, single photon emission computed tomography, and positron emission tomography techniques provide new opportunities to acquire measurements of relevant molecular and cellular features of tumors non-invasively and with high spatial resolution. Such data can be incorporated into mathematical models of tumors. We highlight some recent examples of this approach and identify several simple examples that allow for conventional mathematical models of tumor growth to be recast in terms of parameters that can be measured by imaging, thus raising the possibility of designing and constraining models that can be tested in clinical practice. It is our hope that this Perspective will stimulate further work in this evolving and exciting field.

Transorbital endoscopic image guidance.

Endoscopic orbital procedures are hindered by the difficulty in differentiating between orbital structures during those procedures. Image guidance during endoscopic orbital procedures may improve the outcome of orbital endoscopic procedures because real time image and physical space tracking information can be provided to the surgeons to help in the delivery of therapy to the orbit. The research plan proposes to study the feasibility of image guided endoscopic orbital procedures. Specifically this research will characterize both the random and spatial fiducial localization error of the magnetic tracker. We will also determine and optimal fiducial placement that will minimize the TRE at the optic nerve junction and also demonstrate and validate the use of the magnetic tracker for transorbital endoscopic image guidance.