Background: During the racial justice protests of 2020 and 2021, crowd control chemical irritants (referred to as “tear gas”) were deployed against protesters, after which there were anecdotal reports of altered menstrual cycles. Methods: Data from 82 women (ages 18-45) who attended protests/demonstrations in 2020-2021 were collected through an online questionnaire. The associations between proxy measurements of tear gas exposure (number of protests attended, number of acute symptoms of exposure, and number of tear gas exposures) and menstrual cycle outcomes were determined through linear regression, adjusted for potential confounders. Menstrual cycle outcomes were light bleeding, heavy bleeding, period pains, irregular cycles, long bleeding, short bleeding, short cycles, or long cycles. Results: High protest attendance (> 9 times) (β: 2.60, 95% CI: 1.41, 3.80), and exposure to tear gas at least three times (β: 2.18, 95% CI: 0.63, 3.72) were significantly associated with a greater number of menstrual cycle outcomes. The strongest association was observed for irregular cycles. The number of acute symptoms was not significantly associated with menstrual cycle outcomes. Conclusions: Our findings support the hypothesis that tear gas exposure causes menstrual cycle disturbances. Additional research is needed to determine whether there are longer-term menstrual cycle disturbances or other adverse effects on reproductive health.

Background Living in areas with more greenness has been associated with beneficial health outcomes. However, few studies have examined associations of greenness with incidence of stroke, and it is unclear how these associations may vary with the type of vegetation and surrounding ecology. This study evaluated associations between greenness and incidence of stroke by the major ecological regions in the United States. Methods and Results We assessed the incidence of stroke in 27 369 participants from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study, a prospective cohort recruited across the contiguous United States. Greenness was estimated by the normalized difference vegetation index and enhanced vegetation index (EVI) at multiple buffers around home addresses. Participants were assigned to ecoregions using their baseline residence. We estimated the association between residential greenness and incidence of stroke using covariate‐adjusted Cox proportional hazards models. Models were stratified by ecoregions to assess how associations varied by areas with unique vegetation and ecology. We observed 1581 incident cases of stroke during the study period. In the full study population, there was suggestive evidence of a protective association between greenness and stroke incidence (hazard ratio [HR], 0.989 [95% CI, 0.946–1.033]) for a 0.1 increase in normalized difference vegetation index within 250 m. Similar results were obtained using enhanced vegetation index and larger radii. In our analysis by ecoregions, we found greenness was associated with lower stroke risk in the Eastern Temperate Forests region (HR, 0.946 [95% CI, 0.898–0.997]), but higher risk in the Great Plains (HR, 1.442 [95% CI, 1.124–1.849]) and Mediterranean California regions (HR, 1.327 [95% CI, 1.058–1.664]). Conclusions Vegetation may lower the risk of stroke; however, benefits may be limited to certain contexts of the natural environment.

Hypertension (HT) and chronic kidney diseases (CKD) are complex conditions having both genetic and environmental contributions, disproportionately affecting African American (AA) individuals. Recent evidence is contradictory regarding the directionality of the relationship between the two conditions. This study investigates the relationship between CKD and blood pressure (BP)-related traits with CKD and BP by generating polygenic risk scores (PRSs) for CKD and BP-related traits in 2,995 participants of the Jackson Heart Study, a prospective cohort study of AA individuals from the Jackson, Mississippi metropolitan area. We used multivariable regression models to evaluate associations of each PRS with CKD, HT, systolic blood pressure (SBP) and diastolic blood pressure (DBP), adjusting for age, sex, and genetic ancestry. We observed positive associations for the CKD PRS (CKD-PRS) with both CKD (OR per standard deviation increase, 95

Background & Aims: Intra and inter-pathologist variability poses a significant challenge in metabolic dysfunction-associated steatohepatitis (MASH) biopsy evaluation, leading to suboptimal selection of patients and confounded assessment of histological response in clinical trials. We evaluated the utility of an artificial intelligence (AI) digital pathology (DP) platform to help pathologists improve the reliability of fibrosis staging. Methods: A total of 120 digitized histology slides from two trials (NCT03517540, NCT03912532) were analyzed by four expert hepatopathologists, with and without AI assistance in a randomized, crossover design. We utilized an AI DP platform consisting of unstained second harmonic generation/two photon excitation fluorescence (SHG/TPEF) images and AI quantitative fibrosis (qFibrosis) values. Results: AI assistance significantly improved inter-pathologist kappa for fibrosis staging, particularly for early fibrosis (F0-F2), with reduced variance around the median reads. Intra-pathologist kappa was unchanged. AI assistance increased pathologist concordance for identifying clinical trial inclusion cases (F2-F3) from 45% to 71%, exclusion cases (F0/F1/F4) from 38% to 55%, and evaluation of fibrosis response to treatment from 49% to 61%. SHG/TPEF images, qFibrosis continuous values, and qFibrosis stage were considered useful by at least three out of four pathologists in 83%, 55%, and 38% of cases, respectively. In the context of a clinical trial, the increase in inter-pathologist concordance was modeled to result in a X25% reduction in the potential need for adjudication as well as a X45% increase in the study power for a kappa improvement from X0.4 to X0.7. Conclusions: The use of AI DP enhances inter-rater reliability of fibrosis staging for MASH. This indicates that the SHG/TPEFbased AI DP tool is useful for assisting pathologists in assessing fibrosis, thereby enhancing clinical trial efficiency and reliability of fibrosis readouts in response to treatments. (c) 2024 Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA , The Author(s), HistoIndex Pte Ltd. Published by Elsevier B.V on behalf of European Association for the Study of the Liver (EASL). This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Living near green areas with high vegetation may contribute to an active lifestyle, stress reduction, and enhance social cohesion, and consequently may be associated with improved cancer survival. Epidemiologic studies focused on greenness exposure and cancer-specific mortality, specifically colorectal cancer (CRC)-specific mortality, are sparse. We examined the association between residential greenness at diagnosis and CRC-specific mortality.Using the Kentucky Cancer Registry database, 35,868 primary CRC cases diagnosed between January 2000 and December 2018 were followed until death or November 2020. Residential greenness exposure at diagnosis was estimated using Normalized Difference Vegetation Index (NDVI) with 270m and 1230m buffers. Using Cox models, we estimated adjusted hazard ratios (aHRs) of CRC-specific mortality and 95 % confidence intervals (CIs) by quartiles of greenness. We assessed effect modification using the Likelihood Ratio Test (LRT).We observed 9805 CRC-specific deaths over 208,098 person-years of follow-up. While crude assessments suggested an inverse relationship between NDVI quartiles and CRC-specific mortality, the association was null after adjustment for individual- and neighborhood-level characteristics (270m Buffer aHR Quartile 4 versus Quartile 1: 1.01, 95 % CI: 0.95, 1.08, p-trend = 0.65; 1230m Buffer aHR Quartile 4 versus Quartile 1: 1.03, 95 % CI: 0.96, 1.10, p-trend = 0.26). No effect modification was observed by urban/rural status, race, or neighborhood-level income, while modification by stage was observed for the 270m buffer (LRT p-value = 0.03).Our findings suggest that residing in greener areas was not associated with CRC-specific mortality. Future studies should consider additional greenness measures, greenness interactions, and residential changes over follow-up.

Abstract Background: Prior studies have reported a significant association between Adverse Childhood Experiences (ACEs) and various health outcomes, including cancer; however, these have been limited to descriptive and cross-sectional studies. These studies reported that resilience, or the ability to adapt to challenging life experiences, is inversely associated with ACEs and cancer history. To date, there are no studies evaluating the interaction of ACEs and a cancer diagnosis on resilience among adult survivors. The primary aim of this study is to estimate the prevalence of ACEs among RURAL Alabama participants and to determine if the presence of ≥ 4 ACEs and a cancer history influences the odds of reporting self-perceived low and medium (vs. high) resilience compared to those with 0-3 ACEs and no cancer history. Methods: We utilized data from Alabama participants of the RURAL (Risk Underlying Rural Areas Longitudinal) Cohort Study, a prospective study designed to evaluate risk factors for heart and lung disorders in four rural southeastern states. Individuals aged 25-64 years and residents of two counties in Alabama were enrolled and completed a baseline and 3-month follow-up survey. The 30-item Early Trauma Inventory was used to assess the prevalence of ACEs, and the 10-item Connor Davidson-10 Resilience Scale was used to measure self-perceived resilience. Participants were categorized as having high resilience if their total resilience score was ≥75th percentile, medium resilience for scores ≥25th and <75th percentile, and low resilience for scores <25th percentile. Weighted multinomial logistic regression models were used to evaluate the association between number of ACEs (0-3 ACEs vs. ≥ 4 ACEs) and resilience categories and tested interactions between ACEs and cancer history; a p-value <0.05 was considered statistically significant. Results: Of the 556 participants, 38.9% reported having 0-3 ACEs, and 61.1% reported ≥ 4 ACEs. In adjusted models among participants with no cancer history, those experiencing ≥ 4 ACEs had 2.38 times the odds of reporting low (vs. high) resilience (95% CI: 1.93, 2.94) and 1.18 times the odds of reporting medium (vs. high) resilience (95% CI: 1.04, 1.35) compared to those experiencing 0-3 ACEs. Among participants with a cancer history, those experiencing ≥ 4 ACEs had 1.82 times odds of reporting low (vs. high) resilience (95% CI: 1.01, 3.28) and 0.76 times odds of reporting medium (vs. high) resilience (95% CI: 0.49, 1.18) compared to those experiencing 0-3 ACEs. Interactions between ACEs and cancer history were not statistically significant for low and medium (vs. high) resilience (p-values: 0.4 and 0.06, respectively). Conclusion: Those with a cancer history and ≥ 4 ACEs were more likely to report self-perceived low resilience. Significant interactions between ACEs and cancer history were not observed in predicting low and medium resilience (vs. high). To better understand the cumulative effects of ACEs, it is necessary to longitudinally evaluate individuals’ ability to adapt, especially among cancer survivors. Citation Format: Stephie Abraham, Kathy B. Baumgartner, Richard Baumgartner, Jesse Hsu, Joanna Walsh, Mahasin S Mujahid, Viola Vaccarino, Stephanie Boone. The association of adverse childhood experiences (ACEs) and cancer history with resilience: Results from Alabama sites of the RURAL cohort study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C031.

BACKGROUND:A treatment's overall favorable benefit-risk profile does not imply that every individual patient will benefit from the treatment. OBJECTIVES:To describe a statistical methodology for quantifying the benefit-risk trade-off in individual patients. METHODS:The method requires a large randomized controlled trial containing a primary efficacy outcome and a primary safety outcome, for instance, the Thrombin Receptor Antagonist in Secondary Prevention of Atherothrombotic Ischemic Events-Thrombolysis in Myocardial Infarction 50 placebo-controlled trial of vorapaxar in 17 779 patients following myocardial infarction. Multivariate regression models predict each individual patient's risk of ischemic events (benefit) and major bleeding events (harm) based on their profile. Hence, each patient's predicted benefit from vorapaxar (reduction in ischemic events) and predicted risk (increase in bleeding events) were estimated. The relative importance of ischemic and bleeding events based on links to all-cause mortality was quantified, although the limitations of such weightings are noted. RESULTS:Overall results demonstrated both clear benefit and harm from vorapaxar. Substantial interindividual variation in both benefit and risk facilitated distinguishing patients with a favorable benefit-risk trade-off from those who did not. Such findings were applied to recommend vorapaxar in as many as 98.3% of patients in which a favorable mortality-weighted benefit-risk trade-off was present, in 77.2% of patients with ischemic benefit 20% greater than bleeding risk, or in as few as 45.5% of patients if an annual decrease in ischemic risk of ≥0.5% was also required. CONCLUSION:While overall randomized controlled trials of treatment benefit vs risk are valuable, models determining each individual patient's estimated absolute benefit and risk provide more useful insight regarding patient-specific benefit-risk trade-offs to better enable personalized therapeutic decision-making.

Several cohort studies have found associations between long-term exposure to air pollution and stroke risk. However, it is unclear whether the surrounding ecology may modify these associations. This study evaluates associations of air pollution with stroke risk by ecoregions, which are areas of similar type, quality, and quantity of environmental resources in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. We assessed the incidence of stroke in 26,792 participants (45+ yrs) from the REGARDS study, a prospective cohort recruited across the contiguous United States. One-yr and 3-yr means of PM2.5, PM10, O3, NO2, SO2, and CO were estimated at baseline using data from the Center for Air, Climate, & Energy Solution, and assigned to participants at the census block group level. Incident stroke was ascertained through September 30, 2020. Relations of air pollutants with the risk of incident stroke were estimated using Cox proportional hazards models, adjusting for relevant demographics, behavioral risk factors, and neighborhood urbanicity. Models were stratified by EPA designated ecoregions. A 5.4 mu g/m3 (interquartile range) increase in 1-yr PM10 was associated with a hazard ratio (95 %CI) for incident stroke of 1.07 (1.003, 1.15) in the overall study population. We did not find evidence of positive associations for PM2.5, O3, NO2, SO2, and CO in the fully adjusted models. In our ecoregion-specific analysis, associations of PM2.5 with stroke were stronger in the Great Plains ecoregion (HR = 1.44) than other ecoregions, while associations for PM10 were strongest in the Eastern Temperate Forests region (HR = 1.15). The associations between long-term exposure to air pollution and risk of stroke varied by ecoregion. Our results suggests that the type, quality, and quantity of the surrounding ecology can modify the effects of air pollution on risk of stroke.

Variable importance plays a pivotal role in interpretable machine learning as it helps measure the impact of factors on the output of the prediction model. Model agnostic methods based on the generation of "null" features via permutation (or related approaches) can be applied. Such analysis is often utilized in pharmaceutical applications due to its ability to interpret black-box models, including tree-based ensembles. A major challenge and significant confounder in variable importance estimation however is the presence of between-feature correlation. Recently, several adjustments to marginal permutation utilizing feature knockoffs were proposed to address this issue, such as the variable importance measure known as conditional predictive impact (CPI). Assessment and evaluation of such approaches is the focus of our work. We first present a comprehensive simulation study investigating the impact of feature correlation on the assessment of variable importance. We then theoretically prove the limitation that highly correlated features pose for the CPI through the knockoff construction. While we expect that there is always no correlation between knockoff variables and its corresponding predictor variables, we prove that the correlation increases linearly beyond a certain correlation threshold between the predictor variables. Our findings emphasize the absence of free lunch when dealing with high feature correlation, as well as the necessity of understanding the utility and limitations behind methods in variable importance estimation.

To determine the associations between ethnicity, age at diagnosis, obesity, multimorbidity, and odds of experiencing breast cancer (BC) treatment-related side effects among long-term Hispanic and non-Hispanic white (NHW) survivors from New Mexico and explore differences by tamoxifen use. Lifestyle and clinical information including self-reported tamoxifen use and presence of treatment- related side effects were collected at follow-up interviews (12–15 years) for 194 BC survivors. Multivariable logistic regression models were used to examine associations between predictors and odds of experiencing side effects overall and by tamoxifen use. Women ranged in age at diagnosis (30–74, M = 49.3, SD = 9.37), most were NHW (65.4

Abstract Background: Rural and Appalachian cancer survivors have higher rates of invasive stage of disease, increased challenges in accessing health care services, and report poorer health outcomes and greater psychological distress compared to urban cancer survivors. Factors such as resilience-the ability of patients to bounce back after the trauma of a diagnosis- may be an important predictor of well-being, overall cancer experience, and treatment outcomes. In this exploratory study we assess whether residing in urban or rural geographic areas at diagnosis predicts level of self-perceived resilience and correlate relationships with mental and emotional health and social support. Methods: LADDER ‘Life After Diagnosis and Descriptors of Experience and Responses’ (LADDER) Study is an online pilot survey to evaluate experiences of Kentucky adult cancer survivors within the first year of diagnosis. Self-perceived resilience was measured by the 10-item Connor-Davidson Resilience Scale; higher scores indicate better resilience (range 0-40). Multivariable logistic regression was used to estimate Odds Ratios (OR) and 95% Confidence Intervals (CI) for likelihood of low resilience, dichotomized at median score. Spearman Rank Correlation Coefficients (ρ) measured the strength and direction of associations between continuous measures of anxiety, depression, social support, and resilience. Results: Among 46 participants, most were female aged 65 and older, living in non-Appalachian KY with 33% reporting a history of breast cancer. Rural cancer survivors scored 4 points lower in resilience on average than urban cancer survivors. In multivariable models, compared to urban KY survivors, rural KY survivors had a 2-fold higher likelihood of self-perceived low resilience (OR=2.70:95% 0.58-12.5)). Adjusting for age, sex, Appalachian region, and employment partially attenuated the difference in resilience scores; however, estimates were not significant (OR=2.20: 95% 0.24-20.7). Social support, anxiety, and depression were moderately correlated with resilience among both urban (ρ= 0.36 to 0.64) and rural cancer survivors (ρ range: 0.21 to 0.54) Conclusion: The first year after a cancer diagnosis marks the transition into survivorship with major emotional adjustments. Additional recruitment of survivors will allow examination of diverse experiences post-diagnosis which may lead to interventions to improve quality of life in cancer survivors. Citation Format: Hoa Nguyen, Stephie Abraham, Kathy Baumgartner, Richard Baumgartner, Stephanie Boone. Associations between geographic residence, social support and resilience among Kentucky cancer survivors [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B016.

Abstract Introduction: Despite efforts to improve patient follow-up care, cancer survivors still face challenges in addressing their mental, physical, and social well-being. Resilience, the patients’ ability to thrive and adapt following cancer diagnosis and treatment, is dynamic and variable within and between populations. Previous literature has reported a low level of resilience among those with higher levels of unmet needs. However, there is a need to assess whether the effect on resilience differs by domain to help tailor interventions. The objective of this exploratory analysis is to measure prevalent unmet needs among cancer survivors in Kentucky and to analyze the association between specific domains of unmet needs and resilience. Methods: LADDER (Life After Diagnosis and Descriptors of Experience and Responses) Study is a pilot project to evaluate the feasibility of online methods of collecting preliminary data for psychosocial factors in cancer survivors. Kentucky residents 18 years and older with a cancer diagnosis within 12 months were eligible to enroll. The CaSUN instrument was used among those who completed active cancer treatment to identify prevalent unmet needs across five domains: Information, Existential Survivorship, Comprehensive cancer care, Quality of Life (QOL), and Relationships. Multivariable logistic regression was used to calculate Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusting for age, sex, education, employment, rural residence, cancer type, and time since diagnosis to estimate the likelihood of self-perceived low resilience, measured by the brief 10-item Connor Davidson Resilience Scale. Results: Of the 57 eligible participants, 46 were included in the analysis (80.7%) after accounting for missing data. A majority (67.4%) reported unmet needs for available information and access to the best medical care. Approximately 88% of those who reported unmet needs regarding cancer care were college graduates. Moreover, the majority who reported unmet needs in all five domains were younger (20-44 years), females, employed, and urban residents. Cancer survivors who reported unmet needs in information (OR: 1.14; 95% CI: 0.19, 6.97), existential survivorship (OR: 2.77; 95% CI: 0.58, 13.16), relationship (OR: 1.17; 95% CI: 0.24, 5.73) and QOL (OR: 2.08; 95% CI: 0.40, 10.82) domains had higher odds of reporting self-perceived low resilience; however, these results were not statistically significant. Negative confounding in adjusted models was observed, where survivors who reported unmet needs related to existential survivorship were 4.9 times more likely to report self-perceived low resilience. Conclusions: Based on this exploratory analysis, unmet needs in the existential survivorship domain, which includes stress reduction, coping mechanisms, and social support, is associated with low resilience. Employment and college education explained more of the likelihood of self-perceived low resilience. Understanding specific needs and factors that affect survivors’ resilience is essential to ongoing survivorship care. Citation Format: Stephie Abraham, Hoa Nguyen, Kathy B. Baumgartner, Richard Baumgartner, Stephanie Boone. Association between unmet needs and resilience among cancer survivors in Kentucky [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B006.

The increasing importance of uncertainty quantification in the regulatory evaluation of pharmaceutical products has triggered an explosion of Bayesian methods in recent years. In biomarker discovery and early clinical development, Bayesian methods have established a foothold in developing new drugs, in part due to the increasing availability of greater computational power, often complementing both traditional pharmaceutical statistics and classical statistical methods. In this paper, we present a selective survey of the recent efforts that have been made toward the development and application of effective statistical and computational models in early development statistics. The survey introduces four such case studies and methods that can be used for end-to-end biomarker discovery that includes pre-clinical and early clinical development, from unsupervised clustering to supervised machine learning to modern statistical inference, including but not limited to Bayesian clustering, Bayesian additive regression trees, Bayesian neural networks, and empirical Bayes procedures with the overarching goal of promoting their use and applications among pharmaceutical statisticians. Finally, we present some open issues in Bayesian early clinical methods to help guide the future advancement and wide adoption of Bayesian applications in early clinical pharmaceutical statistics.

A treatment regime is a sequence of decision rules, one per decision point, that maps accumulated patient information to a recommended intervention. An optimal treatment regime maximises expected cumulative utility if applied to select interventions in a population of interest. As a treatment regime seeks to improve the quality of healthcare by individualising treatment, it can be viewed as an approach to formalising precision medicine. Increased interest and investment in precision medicine has led to a surge of methodological research focusing on estimation and evaluation of optimal treatment regimes from observational and/or randomised studies. These methods are becoming commonplace in biomedical research, although guidance about how to choose among existing methods in practice has been somewhat limited. The purpose of this review is to describe some of the most commonly used methods for estimation of an optimal treatment regime, and to compare these estimators in a series of simulation experiments and applications to real data. The results of these simulations along with the theoretical/methodological properties of these estimators are used to form recommendations for applied researchers.

Background: At the beginning of the opioid overdose epidemic, overdose mortality rates were higher in urban than in rural areas. We examined the association between residence in an urban or rural county and subsequent opioid overdose mortality in Kentucky, a state highly impacted by the opioid epidemic, and whether this was modified by the COVID-19 pandemic.Methods: We captured hospitalizations in Kentucky from 2016 to 2020, involving an opioid using ICD-10-CM codes T40.0-T40.4 and T40.6. Patient's county was classified as urban or rural based on the NCHS Urban-Rural Classification Scheme. Multivariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of opioid overdose mortality, adjusted for demographics, hospitalization severity, and zip code SES. We assessed effect modification by the COVID-19 pandemic.Results: Overall, patients living in urban counties had 46% higher odds of opioid overdose death than patients residing in rural counties (adjusted OR=1.46; 95% CI=1.22, 1.74). Before the pandemic, patients in urban counties had 63% increased odds of opioid overdose death (adjusted OR=1.63; 95% CI=1.34, 1.97); however, during the COVID-19 pandemic, patients in urban and rural counties became more similar in regard to opioid overdose mortality (adjusted OR=0.72; 95% CI=0.45, 1.16; p-value for interaction =0.02).Conclusion: Before the pandemic, living in urban counties was associated with higher opioid overdose mortality among Kentucky hospitalizations; however, during the COVID-19 pandemic, opioid overdose mortality in rural areas increased, approaching rates in urban areas. COVID-19 posed social, economic, and healthcare challenges that may be contributing to worsening mortality trends affecting both urban and rural patients.

INTRODUCTION:Soluble tumor necrosis factor receptor-II (sTNF-R2), a pro-inflammatory biomarker, is associated with obesity and breast cancer (BC). The association between sTNF-R2 and risk of mortality after BC has not been studied, specifically among Hispanic women, an at-risk population due to their high prevalence of obesity and poor prognosis. We examined the association between sTNF-R2 and mortality among Hispanic and non-Hispanic white (NHW) BC survivors. METHODS:A total of 397 invasive BC survivors (96 Hispanic, 301 NHW) contributed baseline interview data and blood samples. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox proportional hazards regression models adjusting for clinical factors including body mass index. RESULTS:After a median follow-up time of 13 years, 133 deaths occurred. The association between high vs low levels of plasma sTNF-R2 and mortality was not statistically significant overall (HR, 1.32; 95% CI 0.89-1.98). However, when stratified the mortality risk among Hispanic women was nearly 3-fold (HR, 2.83; 95% CI 1.21-6.63), while risk among NHW women was attenuated (HR, 0.99; 95% CI 0.61-1.61) (p-interaction=0.10). CONCLUSION:Our results suggest Hispanic BC survivors with high sTNF-R2 levels may have increased risk of mortality and could inform targeted interventions to reduce inflammation and improve outcomes.

This chapter concerns the progress of those artificial intelligence (AI) applications and the outlook of AI in drug discovery. Drug discovery is the first and crucial step of the value chain of drug development and involves target identification, optimization, and validation through preclinical testing through cell-based assays and animal models. Traditionally, compounds extracted from natural sources have played a central role in drug discovery. Like the small-molecule drugs, biologic drug discovery commences with the understanding of the disease, screening of a large number of compounds, and using both the traditional and rational approaches. By far, applications of AI in drug discovery have been largely focused on machine learning (ML) and deep learning (DL). ML and DL have been used in numerous cases for target identification and validation; compound property and activity prediction; de novo design; prediction of drug–target interactions; chemical synthesis planning; and computational pathology.

To explore the relationship between physical activity (PA) and quality of life (QOL) among Hispanic and non-Hispanic white breast cancer (BC) cases and population-based controls from the New Mexico ‘Long-Term Quality of Life Study’. Self-reported PA (low, moderate, vigorous MET hours/week) at baseline and follow-up interviews (12–15 years) were available for 391 cases and controls and modeled using multiple linear regressions with SF-36 mean composite scores for physical and mental health. The change in PA from baseline to follow-up and interactions with ethnicity were also examined. Models were adjusted for age at diagnosis/baseline interview, education, comorbidities, body mass index, and change in PA. PA intensities at each timepoint did not differ by case/control status; however, the change in vigorous PA was lower among cases (p = 0.03). At follow-up, low intensity PA increased mental health QOL scores among cases; however, the interaction between low intensity PA and ethnicity was statistically significant among controls indicating decreased mental health among Hispanics (p = 0.02). Change in moderate PA was associated with increased physical and mental health among cases (physical: β = 0.186, p = 0.008; mental: β = 0.225, p = 0.001) and controls (physical: β = 0.220, p < 0.0001; mental: β = 0.193, p = 0.002), when controlling for confounders. Our results demonstrate that all levels of PA are important for mental health among BC cases, while activities of higher intensity are important for physical health among women overall. The statistical interaction observed between ethnicity and low intensity PA among controls for mental health warrants further research to provide a meaningful interpretation.