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Background Social determinants of health (SDOH) play a significant role in the development of cardiovascular risk factors. We investigated SDOH associations with cardiovascular risk factors among Asian American subgroups. Methods and Results We utilized the National Health Interview Survey, a nationally representative survey of US adults, years 2013 to 2018. SDOH variables were categorized into economic stability, neighborhood and social cohesion, food security, education, and health care utilization. SDOH score was created by categorizing 27 SDOH variables as 0 (favorable) or 1 (unfavorable). Self‐reported cardiovascular risk factors included diabetes, high cholesterol, high blood pressure, obesity, insufficient physical activity, suboptimal sleep, and nicotine exposure. Among 6395 Asian adults aged ≥18 years, 22.1% self‐identified as Filipino, 21.6% as Asian Indian, 21.0% as Chinese, and 35.3% as other Asian. From multivariable‐adjusted logistic regression models, each SD increment of SDOH score was associated with higher odds of diabetes among Chinese (odds ratio [OR], 1.45; 95% CI, 1.04–2.03) and Filipino (OR, 1.24; 95% CI, 1.02–1.51) adults; high blood pressure among Filipino adults (OR, 1.28; 95% CI, 1.03–1.60); insufficient physical activity among Asian Indian (OR, 1.42; 95% CI, 1.22–1.65), Chinese (OR, 1.58; 95% CI, 1.33–1.88), and Filipino (OR, 1.24; 95% CI, 1.06–1.46) adults; suboptimal sleep among Asian Indian adults (OR, 1.20; 95% CI, 1.01–1.42); and nicotine exposure among Chinese (OR, 1.56; 95% CI, 1.15–2.11) and Filipino (OR, 1.50; 95% CI, 1.14–1.97) adults. Conclusions Unfavorable SDOH are associated with higher odds of cardiovascular risk factors in Asian American subgroups. Culturally specific interventions addressing SDOH may help improve cardiovascular health among Asian Americans.

Chitosan, a natural polysaccharide derived from chitin, possesses biocompatibility, biodegradability, and mucoadhesive characteristics, making it an attractive material for the delivery of mRNA payloads to the nasal mucosa and promoting their uptake by target cells such as epithelial and immune cells (e.g., dendritic cells and macrophages). In this project, we aimed at developing novel lipid-based nanoformulations for mRNA delivery to counteract the pandemic caused by SARS-CoV-2 virus. The formulations achieved a mRNA encapsulation efficiency of ~80.2% with chitosan-lipid nanoparticles, as measured by the RiboGreen assay. Furthermore, the evaluation of SARS-CoV-2 Spike (S) receptor-binding domain (RBD) expression via ELISA for our vaccine formulations showed transfection levels in human embryonic kidney cells (HEK 293), lung carcinoma cells (A549), and dendritic cells (DC 2.4) equal to 9.9 ± 0.1 ng/mL (174.7 ± 1.1 fold change from untreated cells (UT)), 7.0 ± 0.2 ng/mL (128.1 ± 4.9 fold change from UT), and 0.9 ± 0.0 ng/mL (18.0 ± 0.1 fold change from UT), respectively. Our most promising vaccine formulation was also demonstrated to be amenable to lyophilization with minimal degradation of loaded mRNA, paving the way towards a more accessible and stable vaccine. Preliminary in vivo studies in mice were performed to assess the systemic and local immune responses. Nasal bronchoalveolar lavage fluid (BALF) wash showed that utilizing the optimized formulation resulted in local antibody concentrations and did not trigger any systemic antibody response. However, if further improved and developed, it could potentially contribute to the management of COVID-19 through nasopharyngeal immunization strategies.

Objective: This cross-sectional study aims to better understand the heterogeneous associations of acculturation level on CV risk factors among disaggregated Asian subgroups. We hypothesize that the association between acculturation level and CV risk factors will differ significantly by Asian subgroup. Methods: We used the National Health Interview Survey (NHIS), a nationally representative US survey, years 2014–18. Acculturation was defined using: (a) years in the US, (b) US citizenship status, and (c) level of English proficiency. We created an acculturation index, categorized into low vs. high (scores of 0–3 and 4, respectively). Self-reported CV risk factors included diabetes, high cholesterol, hypertension, obesity, tobacco use, and sufficient physical activity. Rao-Scott Chi Square was used to compare age-standardized, weighted prevalence of CV risk factors between Asian subgroups. We used logistic regression analysis to assess associations between acculturation and CV risk factors, stratified by Asian subgroup. Results: The study sample consisted of 6,051 adults ≥ 18 years of age (53.9% female; mean age 46.6 [SE 0.33]). The distribution by race/ethnicity was Asian Indian 26.9%, Chinese 22.8%, Filipino 18.1%, and other Asian 32.3%. The association between acculturation and CV risk factors differed by Asian subgroups. From multivariable adjusted models, high vs. low acculturation was associated with: high cholesterol amongst Asian Indian (OR=1.57, 95% CI: 1.11, 2.37) and other Asian (OR=1.48, 95% CI: 1.10, 2.01) adults, obesity amongst Filipino adults (OR= 1.62, 95% CI: 1.07, 2.45), and sufficient physical activity amongst Chinese (OR= 1.54, 95% CI: 1.09, 2.19) and Filipino adults (OR=1.58, 95% CI: 1.10, 2.27). Conclusion: This study demonstrates that acculturation is heterogeneously associated with higher prevalence of CV risk factors among Asian subgroups. More studies are needed to better understand these differences that can help to inform targeted, culturally specific interventions.

As online content continues to grow, so does the spread of hate speech. We identify and examine challenges faced by online automatic approaches for hate speech detection in text. Among these difficulties are subtleties in language, differing definitions on what constitutes hate speech, and limitations of data availability for training and testing of these systems. Furthermore, many recent approaches suffer from an interpretability problem-that is, it can be difficult to understand why the systems make the decisions that they do. We propose a multi-view SVM approach that achieves near state-of-the-art performance, while being simpler and producing more easily interpretable decisions than neural methods. We also discuss both technical and practical challenges that remain for this task.