The Benefits of Quitting Smoking at Different Ages.

INTRODUCTION: Quantifying the impact of smoking on life expectancy and the potential benefits of smoking cessation is crucial for motivating people who smoke to quit. While previous studies have attempted to estimate these effects, they were conducted more than a decade ago and did not include a significant demographic, people over 65 years old who smoke. METHODS: Mortality rates by age and smoking status were calculated using mortality relative risks derived from Cancer Prevention Study II, 2018 National Health Interview Survey smoking prevalence data, 2018 US population census data, and 2018 US mortality rates. Subsequently, life tables by smoking status - never, current, and former - were constructed. Life expectancies for all three smoking statuses, including those of individuals who had quit smoking at various ages ranging from 35 to 75, were then compared. Additionally, probability distributions of years lost due to smoking and years gained by quitting smoking at different ages were generated. Analyses were conducted in 2023. RESULTS: Compared to people who never smoked, those who smoke currently, aged 35, 45, 55, 65 or 75 years, and who have smoked throughout adulthood until that age, will lose, on average, 9.1, 8.3, 7.3, 5.9, and 4.4 years of life, respectively, if they continue to smoke for the rest of their lives. However, if they quit smoking at each of these ages, they will avoid an average loss of 8.0, 5.6, 3.4, 1.7, and 0.7 years. The chances of gaining at least 1 year of life among those who quit at age 65 and 75 are 23.4% and 14.2%, respectively. CONCLUSIONS: Quitting smoking early will avoid most years otherwise lost due to smoking. Even those who quit at ages 65 and above can still meaningfully increase their life expectancy.

Sex Differences in Clustering Unhealthy Lifestyles Among Survivors of COVID-19: Latent Class Analysis.

BACKGROUND: The COVID-19 pandemic has underscored the significance of adopting healthy lifestyles to mitigate the risk of severe outcomes and long-term consequences. OBJECTIVE: This study focuses on assessing the prevalence and clustering of 5 unhealthy lifestyle behaviors among Vietnamese adults after recovering from COVID-19, with a specific emphasis on sex differences. METHODS: The cross-sectional data of 5890 survivors of COVID-19 in Vietnam were analyzed from December 2021 to October 2022. To examine the sex differences in 5 unhealthy lifestyle behaviors (smoking, drinking, unhealthy diet, physical inactivity, and sedentary behavior), the percentages were plotted along with their corresponding 95% CI for each behavior. Latent class analysis was used to identify 2 distinct classes of individuals based on the clustering of these behaviors: the "less unhealthy" group and the "more unhealthy" group. We examined the sociodemographic characteristics associated with each identified class and used logistic regression to investigate the factors related to the "more unhealthy" group. RESULTS: The majority of individuals (male participants: 2432/2447, 99.4% and female participants: 3411/3443, 99.1%) exhibited at least 1 unhealthy behavior, with male participants being more susceptible to multiple unhealthy behaviors. The male-to-female ratio for having a single behavior was 1.003, but it escalated to 25 for individuals displaying all 5 behaviors. Male participants demonstrated a higher prevalence of combining alcohol intake with sedentary behavior (949/2447, 38.8%) or an unhealthy diet (861/2447, 35.2%), whereas female participants tended to exhibit physical inactivity combined with sedentary behavior (1305/3443, 37.9%) or an unhealthy diet (1260/3443, 36.6%). Married male participants had increased odds of falling into the "more unhealthy" group compared to their single counterparts (odds ratio [OR] 1.45, 95% CI 1.14-1.85), while female participants exhibited lower odds (OR 0.65, 95% CI 0.51-0.83). Female participants who are underweight showed a higher likelihood of belonging to the "more unhealthy" group (OR 1.11, 95% CI 0.89-1.39), but this was not observed among male participants (OR 0.6, 95% CI 0.41-0.89). In both sexes, older age, dependent employment, high education, and obesity were associated with higher odds of being in the "more unhealthy" group. CONCLUSIONS: The study identified notable sex differences in unhealthy lifestyle behaviors among survivors of COVID-19. Male survivors are more likely to engage in unhealthy behaviors compared to female survivors. These findings emphasize the importance of tailored public health interventions targeting sex-specific unhealthy behaviors. Specifically, addressing unhealthy habits is crucial for promoting post-COVID-19 health and well-being.

New Estimates of Smoking-Attributable Mortality in the U.S. From 2020 Through 2035.

INTRODUCTION: The often-cited Centers for Disease Control and Prevention (CDC) estimate of 480,000 annual U.S. smoking-attributable deaths (SADs), including 439,000 first-hand smoke deaths, derives from 2005 to 2009 data. Since then, adult smoking prevalence has decreased by 40%, while the population has grown and the smoking population aged. An updated estimate is presented to determine whether the CDC figure remains accurate or has changed substantially. In addition, the likely annual smoking-related mortality toll is projected through 2035. METHODS: A well-established model of smoking prevalence and health effects is employed to estimate annual SADs among individuals exposed to first-hand smoke in the U.S. for two distinct periods: 2005-2009 and 2020-2035. The estimate for 2005-2009 serves as a benchmark to evaluate the reliability of the model's estimate in comparison to CDC's. The projections for 2020-2035 provide up-to-date figures for SADs, predicting how annual SADs are likely to change in the coming years. Data were collected between 2005 and 2020. The analysis was conducted in 2023. RESULTS: This study's estimate of 420,000 first-hand smoke deaths over 2005-2009 is 95.7% of CDC's estimate during the same period. The model projections indicate that SADs among individuals who currently smoke or formerly smoked have increased modestly since 2005-2009. Beginning in 2020, annual SADs will remain relatively stable at approximately 450,000 before starting to decline around 2030. CONCLUSIONS: These findings suggest that the CDC estimate of the annual mortality burden of smoking remains valid. Despite U.S. population growth and the aging of the smoking population, substantial reductions in smoking will finally produce a steady, if gradual, decline in SADs beginning around 2030.

The evolution of age-specific smoking cessation rates in the United States from 2009 to 2017: a Kalman filter based approach.

BACKGROUND: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. Recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided annual estimates of the cessation rate by age group. Hence, the primary objective of this study is to estimate annual smoking cessation rates specific to different age groups in the US from 2009 to 2017. METHODS: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2017 period using data from the 2009-2018 National Health Interview Surveys. We focused on cessation rates in the 25-44, 45-64 and 65 + age groups. RESULTS: The findings show that cessation rates followed a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% to 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. CONCLUSIONS: The Kalman filter approach offers a real-time estimation of cessation rates that can be helpful for monitoring smoking cessation behavior.

Key Risk Factors Associated With Electronic Nicotine Delivery Systems Use Among Adolescents.

Importance: The prevalence of electronic nicotine delivery systems (ENDS) use among US youths has increased significantly during the past decade. Identifying key factors highly associated with ENDS use is essential in monitoring and preventing this harmful behavior among youths. Objective: To identify the most important risk factors in wave 4.5 (ie, December 2017 to December 2018) of the Population Assessment of Tobacco and Health Study (PATH) data that are associated with ENDS use in wave 5 (ie, December 2018 to November 2019) among adolescents who were tobacco-naive at baseline. Design, Setting, and Participants: This prognostic study examined data from waves 4.5 and 5 of the PATH youth data set using machine learning techniques. The PATH study is a nationally representative longitudinal cohort study of tobacco use and health in the United States among individuals aged 12 years and older. The data analysis was carried out between January and April 2023. Main Outcomes and Measures: Wave 5 current ENDS use status of wave 4.5 adolescents who were tobacco-naive. Results: The analyzed data set comprised 7943 individuals who were tobacco-naive in wave 4.5. Among this group, 332 participants (4.2%) indicated their present use of ENDS in wave 5, 5047 (63.5%) were aged 12 to 14 years, 4066 (51.2%) were male, and 2455 (30.9%) were Hispanic. The most important risk factors of ENDS use in wave 5 among adolescents who were tobacco-naive in wave 4.5 were the likelihood of using ENDS if offered by a best friend (mean SHAP value, 0.184), the number of best friends using e-cigarettes (mean SHAP value, 0.167), household tobacco usage (mean SHAP value, 0.161), curiosity about ENDS use (mean SHAP value, 0.088), future intention to use ENDS (mean SHAP value, 0.068), youth's total average weekly earnings (mean SHAP value, 0.060), and perceptions of tobacco product safety (mean SHAP value, 0.026). Conclusions and Relevance: The findings of this study suggest that family and friends play an important role in ENDS use among adolescents. The top-ranking factors associated with ENDS use in this study are areas for further exploration, given the increasing prevalence of ENDS use among youths in recent years. Additionally, these findings highlight the important role of families and schools in shaping adolescents' tobacco-related knowledge, which can protect them from using ENDS.

The Evolution of Age-Specific Smoking Cessation Rates in the United States From 2009 to 2018.

Objective: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. A couple of recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided recent annual estimates of the cessation rate by age group. Methods: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2018 period using data from the National Health Interview Survey. We focused on cessation rates in the 24-44, 45-64 and 65 + age groups. Results: The findings show that cessation rates follow a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% in 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. Conclusions: The Kalman filter approach offers a real-time estimation of cessation rates that would be helpful for monitoring smoking cessation behavior, of interest in general but also for tobacco control policymakers.

Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

Are the Relevant Risk Factors Being Adequately Captured in Empirical Studies of Smoking Initiation? A Machine Learning Analysis Based on the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Cigarette smoking continues to pose a threat to public health. Identifying individual risk factors for smoking initiation is essential to further mitigate this epidemic. To the best of our knowledge, no study today has used machine learning (ML) techniques to automatically uncover informative predictors of smoking onset among adults using the Population Assessment of Tobacco and Health (PATH) study. AIMS AND METHODS: In this work, we employed random forest paired with Recursive Feature Elimination to identify relevant PATH variables that predict smoking initiation among adults who have never smoked at baseline between two consecutive PATH waves. We included all potentially informative baseline variables in wave 1 (wave 4) to predict past 30-day smoking status in wave 2 (wave 5). Using the first and most recent pairs of PATH waves was found sufficient to identify the key risk factors of smoking initiation and test their robustness over time. The eXtreme Gradient Boosting method was employed to test the quality of these selected variables. RESULTS: As a result, classification models suggested about 60 informative PATH variables among many candidate variables in each baseline wave. With these selected predictors, the resulting models have a high discriminatory power with the area under the specificity-sensitivity curves of around 80%. We examined the chosen variables and discovered important features. Across the considered waves, two factors, (1) BMI, and (2) dental and oral health status, robustly appeared as important predictors of smoking initiation, besides other well-established predictors. CONCLUSIONS: Our work demonstrates that ML methods are useful to predict smoking initiation with high accuracy, identifying novel smoking initiation predictors, and to enhance our understanding of tobacco use behaviors. IMPLICATIONS: Understanding individual risk factors for smoking initiation is essential to prevent smoking initiation. With this methodology, a set of the most informative predictors of smoking onset in the PATH data were identified. Besides reconfirming well-known risk factors, the findings suggested additional predictors of smoking initiation that have been overlooked in previous work. More studies that focus on the newly discovered factors (BMI and dental and oral health status,) are needed to confirm their predictive power against the onset of smoking as well as determine the underlying mechanisms.

Synthetic Heparan Sulfate Mimetic Pixatimod (PG545) Potently Inhibits SARS-CoV-2 by Disrupting the Spike-ACE2 Interaction.

Heparan sulfate (HS) is a cell surface polysaccharide recently identified as a coreceptor with the ACE2 protein for the S1 spike protein on SARS-CoV-2 virus, providing a tractable new therapeutic target. Clinically used heparins demonstrate an inhibitory activity but have an anticoagulant activity and are supply-limited, necessitating alternative solutions. Here, we show that synthetic HS mimetic pixatimod (PG545), a cancer drug candidate, binds and destabilizes the SARS-CoV-2 spike protein receptor binding domain and directly inhibits its binding to ACE2, consistent with molecular modeling identification of multiple molecular contacts and overlapping pixatimod and ACE2 binding sites. Assays with multiple clinical isolates of SARS-CoV-2 virus show that pixatimod potently inhibits the infection of monkey Vero E6 cells and physiologically relevant human bronchial epithelial cells at safe therapeutic concentrations. Pixatimod also retained broad potency against variants of concern (VOC) including B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron). Furthermore, in a K18-hACE2 mouse model, pixatimod significantly reduced SARS-CoV-2 viral titers in the upper respiratory tract and virus-induced weight loss. This demonstration of potent anti-SARS-CoV-2 activity tolerant to emerging mutations establishes proof-of-concept for targeting the HS-Spike protein-ACE2 axis with synthetic HS mimetics and provides a strong rationale for clinical investigation of pixatimod as a potential multimodal therapeutic for COVID-19.

Monitoring the Increase in the U.S. Smoking Cessation Rate and Its Implication for Future Smoking Prevalence.

INTRODUCTION: We calculate the U.S. adult smoking cessation rate for 2014-2019, compare it to the historical trend, and estimate the implication for future smoking prevalence. METHODS: We repeated an earlier analysis, which examined the cessation rate from 1990 to 2014, extending the period to 2019. Employing National Health Interview Survey (NHIS) and National Survey on Drug Use and Health (NSDUH) data, we estimated the adult cessation rate in 6-year intervals, using weighted nonlinear least squares. We then employed a meta-regression model to test whether the cessation rate has increased beyond expectation. We used cessation rate estimates and smoking initiation rate estimates to project smoking prevalence in 2030 and eventual steady-state prevalence. RESULTS: The annual cessation rate increased 29% using NHIS data (from 4.2% in 2008-2013 to 5.4% in 2014-2019) and 33% with NSDUH data (4.2%-5.6%). The cessation rate increase accounts for 60% of a smoking prevalence decline in the most recent period exceeding the 1990-2013 predicted trend. The remaining 40% owes to declining smoking initiation. With current initiation and cessation rates, smoking prevalence should fall to 8.3% in 2030 and eventually reach a steady state of 3.53%. CONCLUSIONS: The smoking cessation rate continued to increase during 2014-2019. NHIS and NSDUH results are practically identical. The larger share (60%) of the smoking prevalence decrease, beyond expectation, attributable to the increased cessation rate is encouraging since the positive health effects of cessation occur much sooner than those derived from declining initiation. IMPLICATIONS: The smoking cessation rate in the United States continues to increase, accelerating the decline in smoking prevalence. This increase suggests that the Healthy People 2030 goal of 5% adult smoking prevalence, while ambitious, is attainable. Our findings can be used in simulation and statistical models that aim to predict future prevalence and population health effects due to smoking under various scenarios.

The association between smoking behaviors and prices and taxes per cigarette pack in the United States from 2000 through 2019.

OBJECTIVE: The conclusions on how tax and price increases affect smoking behaviors are mixed. This work is devoted to re-evaluating the relationship between cigarette prices and taxes and smoking behaviors. METHODS: Using 2000-2019 Behavioral Risk Factor Surveillance System data, we employed linear mixed-effect models to re-examine the impact of cigarette prices and taxes on smoking prevalence and the proportion of current smokers having tried to quit smoking in the past 12 months. All the analyses were conducted for the general population, then by age group, gender, race/ethnicity, and income level. RESULTS: The results indicate that higher cigarette prices and taxes were associated with a decrease in smoking prevalence and an increased likelihood of quitting smoking. Cigarette tax and price increases produced the most powerful impact on the smoking prevalence of 18- to 24-year-olds. The estimates also show that males tended to be more price-sensitive than females. Raising cigarette prices and taxes was estimated to be more effective in reducing the smoking prevalence among non-Hispanic Blacks and Hispanics when compared to non-Hispanic whites. Cigarette price and tax changes were likely to have a smaller effect on individuals with annual income under $25,000 relative to individuals with higher income levels. CONCLUSIONS: Increases in cigarette prices and taxes are significantly associated with a reduction in smoking prevalence and an increased likelihood of quitting smoking among adults across different demographic and socioeconomic groups. However, as cigarette price and tax changes disproportionately affect low-income individuals, raising cigarette prices and taxes may deepen income disparities.

Negative Impacts of COVID-19 Induced Lockdown on Changes in Eating Behavior, Physical Activity, and Mental Health as Modified by Digital Healthy Diet Literacy and eHealth Literacy.

Background: The COVID-19-induced lockdown has been implemented in many countries, which may cause unfavorable changes in lifestyles and psychological health. People's health literacy, healthy diet, and lifestyles play important roles in mitigating the negative impacts of the pandemic. Therefore, we aimed to examine associations of COVID-19 lockdown with changes in eating behavior, physical activity, and mental health; and the modification effects by digital healthy diet literacy (DDL) and eHealth literacy (eHEALS) on the associations. Methods: We conducted an observational study on 4,348 outpatients from 7th April to 31st May 2020. Data from 11 hospitals in Vietnam included demographic characteristics, DDL, eHEALS, eating behavior, physical activity, and mental health changes. Multiple logistic regression and interaction models were performed to examine associations. Results: Patients under lockdown had a lower likelihood of having "unchanged or healthier" eating behavior (odds ratio, OR, 0.38; 95% confidence interval, 95%CI, 0.29 to 0.51; p < 0.001), "unchanged or more" physical activity (OR, 0.79; 95% CI, 0.69 to 0.90; p < 0.001), and "stable or better" mental health (OR, 0.77; 95% CI, 0.67 to 0.89; p < 0.001), as compared to those after lockdown. In interaction models, as compared to patients after lockdown and with the lowest DDL score, those under lockdown and with a one-score increment of DDL had a higher likelihood of having "unchanged or healthier" eating behavior (OR, 1.05; 95% CI, 1.02 to 1.07; p < 0.001), and "stable or better" mental health (OR, 1.02; 95% CI, 1.01 to 1.04; p < 0.001). Similarly, as compared to patients after lockdown and with the lowest eHEALS score, those under lockdown and with a one-score increment of eHEALS had a higher likelihood of having an "unchanged or more" physical activity (OR, 1.03; 95% CI, 1.01 to 1.05; p < 0.001). Conclusion: The COVID-19 lockdown measure could negatively affect eating behavior, physical activity, and mental health among outpatients. Better DDL and eHEALS were found to mitigate the negative impacts of the lockdown, which may empower outpatients to maintain healthy lifestyles and protect mental health. However, this study holds several limitations that may undermine the certainty of reported findings.

Two-Component Nanoparticle Vaccine Displaying Glycosylated Spike S1 Domain Induces Neutralizing Antibody Response against SARS-CoV-2 Variants.

Vaccines pave the way out of the SARS-CoV-2 pandemic. Besides mRNA and adenoviral vector vaccines, effective protein-based vaccines are needed for immunization against current and emerging variants. We have developed a virus-like particle (VLP)-based vaccine using the baculovirus-insect cell expression system, a robust production platform known for its scalability, low cost, and safety. Baculoviruses were constructed encoding SARS-CoV-2 spike proteins: full-length S, stabilized secreted S, or the S1 domain. Since subunit S only partially protected mice from SARS-CoV-2 challenge, we produced S1 for conjugation to bacteriophage AP205 VLP nanoparticles using tag/catcher technology. The S1 yield in an insect-cell bioreactor was ∼11 mg/liter, and authentic protein folding, efficient glycosylation, partial trimerization, and ACE2 receptor binding was confirmed. Prime-boost immunization of mice with 0.5 µg S1-VLPs showed potent neutralizing antibody responses against Wuhan and UK/B.1.1.7 SARS-CoV-2 variants. This two-component nanoparticle vaccine can now be further developed to help alleviate the burden of COVID-19. IMPORTANCE Vaccination is essential to reduce disease severity and limit the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Protein-based vaccines are useful to vaccinate the world population and to boost immunity against emerging variants. Their safety profiles, production costs, and vaccine storage temperatures are advantageous compared to mRNA and adenovirus vector vaccines. Here, we use the versatile and scalable baculovirus expression vector system to generate a two-component nanoparticle vaccine to induce potent neutralizing antibody responses against SARS-CoV-2 variants. These nanoparticle vaccines can be quickly adapted as boosters by simply updating the antigen component.

Consequences of a match made in hell: the harm caused by menthol smoking to the African American population over 1980-2018.

BACKGROUND: For many years, national surveys have shown a consistently disproportionately high prevalence of menthol smokers among African Americans compared with the general population. However, to our knowledge, no prior study has quantified the harm that menthol smoking has caused on that population. In this work, we estimate the public health harm that menthol cigarettes have caused to the African American community over the last four decades. METHODS: Using National Health Interview Survey data, we employed a well-established simulation model to reproduce the observed smoking trajectory over 1980-2018 in the African American population. Then, we repeat the experiment, removing the effects of menthol on the smoking initiation and cessation rates over that period, obtaining a new hypothetical smoking trajectory. Finally, we compared both scenarios to calculate the public health harm attributable to menthol cigarettes over 1980-2018. RESULTS: Our results show that menthol cigarettes were responsible for 1.5 million new smokers, 157 000 smoking-related premature deaths and 1.5 million life-years lost among African Americans over 1980-2018. While African Americans constitute 12% of the total US population, these figures represent, respectively, a staggering 15%, 41% and 50% of the total menthol-related harm. DISCUSSION: Our results show that menthol cigarettes disproportionally harmed African Americans significantly over the last 38 years and are responsible for exacerbating health disparities among that population. Removing menthol cigarettes from the market would benefit the overall US population but, particularly, the African American community.

Neuroinvasiveness of the MR766 strain of Zika virus in IFNAR-/- mice maps to prM residues conserved amongst African genotype viruses.

Zika virus (ZIKV) strains are classified into the African and Asian genotypes. The higher virulence of the African MR766 strain, which has been used extensively in ZIKV research, in adult IFNα/ß receptor knockout (IFNAR-/-) mice is widely viewed as an artifact associated with mouse adaptation due to at least 146 passages in wild-type suckling mouse brains. To gain insights into the molecular determinants of MR766's virulence, a series of genes from MR766 were swapped with those from the Asian genotype PRVABC59 isolate, which is less virulent in IFNAR-/- mice. MR766 causes 100% lethal infection in IFNAR-/- mice, but when the prM gene of MR766 was replaced with that of PRVABC59, the chimera MR/PR(prM) showed 0% lethal infection. The reduced virulence was associated with reduced neuroinvasiveness, with MR766 brain titers ≈3 logs higher than those of MR/PR(prM) after subcutaneous infection, but was not significantly different in brain titers of MR766 and MR/PR(prM) after intracranial inoculation. MR/PR(prM) also showed reduced transcytosis when compared with MR766 in vitro. The high neuroinvasiveness of MR766 in IFNAR-/- mice could be linked to the 10 amino acids that differ between the prM proteins of MR766 and PRVABC59, with 5 of these changes affecting positive charge and hydrophobicity on the exposed surface of the prM protein. These 10 amino acids are highly conserved amongst African ZIKV isolates, irrespective of suckling mouse passage, arguing that the high virulence of MR766 in adult IFNAR-/- mice is not the result of mouse adaptation.

Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality.

The benefits of mammography screening have been controversial, with conflicting findings from various studies. We hypothesize that unmeasured heterogeneity in tumor aggressiveness underlies these conflicting results. Based on published data from the Canadian National Breast Screening Study (CNBSS), we develop and parameterize an individual-based mechanistic model for breast cancer incidence and mortality that tracks five stages of breast cancer progression and incorporates the effects of age on breast cancer incidence and all-cause mortality. The model accurately reproduces the reported outcomes of the CNBSS. By varying parameters, we predict that the benefits of mammography depend on the effectiveness of cancer treatment and tumor aggressiveness. In particular, patients with the most rapidly growing or potentially largest tumors have the highest benefit and least harm from the screening, with only a relatively small effect of age. However, the model predicts that confining mammography to populations with a high risk of acquiring breast cancer increases the screening benefit only slightly compared with the full population.

Model-Based Simulation of Maintenance Therapy of Childhood Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia is the most common malignancy in childhood. Successful treatment requires initial high-intensity chemotherapy, followed by low-intensity oral maintenance therapy with oral 6-mercaptopurine (6MP) and methotrexate (MTX) until 2-3 years after disease onset. However, intra- and inter-individual variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of 6MP and MTX make it challenging to balance the desired antileukemic effects with undesired excessive myelosuppression during maintenance therapy. A model to simulate the dynamics of different cell types, especially neutrophils, would be a valuable contribution to improving treatment protocols (6MP and MTX dosing regimens) and a further step to understanding the heterogeneity in treatment efficacy and toxicity. We applied and modified a recently developed semi-mechanistic PK/PD model to neutrophils and analyzed their behavior using a non-linear mixed-effects modeling approach and clinical data obtained from 116 patients. The PK model of 6MP influenced the accuracy of absolute neutrophil count (ANC) predictions, whereas the PD effect of MTX did not. Predictions based on ANC were more accurate than those based on white blood cell counts. Using the new cross-validated mathematical model, simulations of different treatment protocols showed a linear dose-effect relationship and reduced ANC variability for constant dosages. Advanced modeling allows the identification of optimized control criteria and the weighting of specific influencing factors for protocol design and individually adapted therapy to exploit the optimal effect of maintenance therapy on survival.

A mathematical model of white blood cell dynamics during maintenance therapy of childhood acute lymphoblastic leukemia.

Acute lymphoblastic leukemia is the most common malignancy in childhood and requires prolonged oral maintenance chemotherapy to prevent disease relapse after remission induction with intensive intravenous chemotherapy. In maintenance therapy, drug doses of 6-mercaptopurine (6-MP) and methotrexate (MTX) are adjusted to achieve sustained antileukemic activity without excessive myelosuppression. However, uncertainty exists regarding timing and extent of drug dose responses and optimal dose adaptation strategies. We propose a novel comprehensive mathematical model for 6-MP and MTX pharmacokinetics, pharmacodynamics and myelosuppression in acute lymphoblastic maintenance therapy. We personalize and cross-validate the mathematical model using clinical data and propose a real-time algorithm to predict chemotherapy responses with a clinical decision support system as a potential future application.

A vaccinia-based single vector construct multi-pathogen vaccine protects against both Zika and chikungunya viruses.

Zika and chikungunya viruses have caused major epidemics and are transmitted by Aedes aegypti and/or Aedes albopictus mosquitoes. The "Sementis Copenhagen Vector" (SCV) system is a recently developed vaccinia-based, multiplication-defective, vaccine vector technology that allows manufacture in modified CHO cells. Herein we describe a single-vector construct SCV vaccine that encodes the structural polyprotein cassettes of both Zika and chikungunya viruses from different loci. A single vaccination of mice induces neutralizing antibodies to both viruses in wild-type and IFNAR-/- mice and protects against (i) chikungunya virus viremia and arthritis in wild-type mice, (ii) Zika virus viremia and fetal/placental infection in female IFNAR-/- mice, and (iii) Zika virus viremia and testes infection and pathology in male IFNAR-/- mice. To our knowledge this represents the first single-vector construct, multi-pathogen vaccine encoding large polyproteins, and offers both simplified manufacturing and formulation, and reduced "shot burden" for these often co-circulating arboviruses.

IL-1 Contributes to the Anti-Cancer Efficacy of Ingenol Mebutate.

Ingenol mebutate is approved for the topical treatment of actinic keratoses and may ultimately also find utility in treating skin cancers. Here we show that relapse rates of subcutaneous B16 melanoma tumours treated topically with ingenol mebutate were not significantly different in C57BL/6 and Rag1-/- mice, suggesting B and T cells do not play a major role in the anti-cancer efficacy of ingenol mebutate. Relapse rates were, however, significantly increased in MyD88-/- mice and in C57BL/6 mice treated with the anti-IL-1 agent, anakinra. Ingenol mebutate treatment induces a pronounced infiltration of neutrophils, which have been shown to have anti-cancer activity in mice. Herein we provide evidence that IL-1 promotes neutrophil recruitment to the tumour, decreases apoptosis of infiltrating neutrophils and increases neutrophil tumour killing activity. These studies suggest IL-1, via its action on neutrophils, promotes the anti-cancer efficacy of ingenol mebutate, with ingenol mebutate treatment causing both IL-1ß induction and IL-1α released from keratinocytes.