Connect: Cultivating Academic-Community Partnerships to Address Our Communities' Complex Needs During Public Health Crises.

BACKGROUND: Black and Latino communities have been disproportionately impacted by coronavirus disease 2019 and we sought to understand perceptions and attitudes in four heavily impacted New Jersey counties to develop and evaluate engagement strategies to enhance access to testing. OBJECTIVE: To establish a successful academic/community partnership team during a public health emergency by building upon longstanding relationships and using principles from community engaged research. METHODS: We present a case study illustrating multiple levels of engagement, showing how we successfully aligned expectations, developed a commitment of cooperation, and implemented a research study, with community-based and health care organizations at the center of community engagement and recruitment. LESSONS LEARNED: This paper describes successful approaches to relationship building including information sharing and feedback to foster reciprocity, diverse dissemination strategies to enhance engagement, and intergenerational interaction to ensure sustainability. CONCLUSIONS: This model demonstrates how academic/community partnerships can work together during public health emergencies to develop sustainable relationships.

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches.

BACKGROUND: Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at local scales. OBJECTIVE: To assess socioexposomic associations with COVID-19 outcomes across New Jersey and evaluate consistency of findings from multiple modeling approaches. METHODS: We retrieved data for COVID-19 cases and deaths for the 565 municipalities of New Jersey up to the end of the first phase of the pandemic, and calculated mortality rates with and without long-term-care (LTC) facility deaths. We considered 84 spatially heterogeneous environmental, demographic and socioeconomic factors from publicly available databases, including air pollution, proximity to industrial sites/facilities, transportation-related noise, occupation and commuting, neighborhood and housing characteristics, age structure, racial/ethnic composition, poverty, etc. Six geostatistical models (Poisson/Negative-Binomial regression, Poison/Negative-Binomial mixed effect model, Poisson/Negative-Binomial Bersag-York-Mollie spatial model) and two Machine Learning (ML) methods (Random Forest, Extreme Gradient Boosting) were implemented to assess association patterns. The Shapley effects plot was established for explainable ML and change of support validation was introduced to compare performances of different approaches. RESULTS: We found robust positive associations of COVID-19 mortality with historic exposures to NO2, population density, percentage of minority and below high school education, and other social and environmental factors. Exclusion of LTC deaths does not significantly affect correlations for most factors but findings can be substantially influenced by model structures and assumptions. The best performing geostatistical models involved flexible structures representing data variations. ML methods captured association patterns consistent with the best performing geostatistical models, and furthermore detected consistent nonlinear associations not captured by geostatistical models. SIGNIFICANCE: The findings of this work improve the understanding of how social and environmental disparities impacted COVID-19 outcomes across New Jersey.

Modeling past and future spatiotemporal distributions of airborne allergenic pollen across the contiguous United States.

Exposures to airborne allergenic pollen have been increasing under the influence of changing climate. A modeling system incorporating pollen emissions and atmospheric transport and fate processes has been developed and applied to simulate spatiotemporal distributions of two major aeroallergens, oak and ragweed pollens, across the contiguous United States (CONUS) for both historical (year 2004) and future (year 2047) conditions. The transport and fate of pollen presented here is simulated using our adapted version of the Community Multiscale Air Quality (CMAQ) model. Model performance was evaluated using observed pollen counts at monitor stations across the CONUS for 2004. Our analysis shows that there is encouraging consistency between observed seasonal mean concentrations and corresponding simulated seasonal mean concentrations (oak: Pearson = 0.35, ragweed: Pearson = 0.40), and that the model was able to capture the statistical patterns of observed pollen concentration distributions in 2004 for most of the pollen monitoring stations. Simulation of pollen levels for a future year (2047) considered conditions corresponding to the RCP8.5 scenario. Modeling results show substantial regional variability both in the magnitude and directionality of changes in pollen metrics. Ragweed pollen season is estimated to start earlier and last longer for all nine climate regions of the CONUS, with increasing average pollen concentrations in most regions. The timing and magnitude of oak pollen season vary across the nine climate regions, with the largest increases in pollen concentrations expected in the Northeast region.

Community- Versus Health Care Organization-Based Approaches to Expanding At-Home COVID-19 Testing in Black and Latino Communities, New Jersey, 2021.

At-home COVID-19 testing offers convenience and safety advantages. We evaluated at-home testing in Black and Latino communities through an intervention comparing community-based organization (CBO) and health care organization (HCO) outreach. From May through December 2021, 1100 participants were recruited, 94% through CBOs. The odds of COVID-19 test requests and completions were significantly higher in the HCO arm. The results showed disparities in test requests and completions related to age, race, language, insurance, comorbidities, and pandemic-related challenges. Despite the popularity of at-home testing, barriers exist in underresourced communities. (Am J Public Health. 2022;112(S9):S918-S922. https://doi.org/10.2105/AJPH.2022.306989).

Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations.

3D-grid-based chemical transport models, such as the Community Multiscale Air Quality (CMAQ) modeling system, have been widely used for predicting concentrations of ambient air pollutants. However, typical horizontal resolutions of nationwide CMAQ simulations (12 × 12 km2) cannot capture local-scale gradients for accurately assessing human exposures and environmental justice disparities. In this study, a Bayesian ensemble machine learning (BEML) framework, which integrates 13 learning algorithms, was developed for downscaling CMAQ estimates of ozone daily maximum 8 h averages to the census tract level, across the contiguous US, and was demonstrated for 2011. Three-stage hyperparameter tuning and targeted validations were designed to ensure the ensemble model's ability to interpolate, extrapolate, and capture concentration peaks. The Shapley value metric from coalitional game theory was applied to interpret the drivers of subgrid gradients. The flexibility (transferability) of the 2011-trained BEML model was further tested by evaluating its ability to estimate fine-scale concentrations for other years (2012-2017) without retraining. To demonstrate the feasibility of using the BEML approach to strictly "data-limited" situations, the model was applied to downscale CMAQ outputs for a future-year scenario-based simulation that considers effects of variations in meteorology associated with climate change.

Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey.

COVID-19 created an unprecedented global public health crisis during 2020-2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted.

Biomonitoring: A tool to assess PFNA body burdens and evaluate the effectiveness of drinking water intervention for communities in New Jersey.

Elevated perfluorononanoic acid (PFNA) levels, one of many manmade per- and polyfluoroalkyl substances (PFAS), were detected in public water systems/private wells in New Jersey communities. Interventions to end exposure through drinking water were carried out from 2014 to 2016. To evaluate the effectiveness of interventions, a community biomonitoring study was conducted for the communities between 2017 and 2020. A convenience sampling design was used with 120 participants in Year 1 between ages of 20-74 who consumed PFNA-contaminated water. Three blood samples, one year apart, were drawn from each participant and completed for 99 participants. Separated serum samples were measured for 12 PFAS including PFNA. Questionnaires were administered to collect information on demographics and potential sources. Drinking water and house dust collected at the first visit were analyzed for 14 PFAS including PFNA. The PFNA sera levels (Year 1) found 84 out of 120 (70%) participants were higher than the 95th percentile of a nationally representative sample of US adults (NHANES2015-16). Current drinking water and house dust were not significant contributing sources for the study participants. On average, PFNA sera levels were 12 ± 16% (Year 2) and 27 ± 16% (Year 3) lower than the level measured in Year 1 (p < 0.01). The PFNA half-life was estimated around 3.52 years, using a mixed model from 68 high-exposed participants (>95th percentile of NHANES2015-16) with controlling for physiological covariates. The decline in adult serum PFNA levels seen in the years following a community drinking water intervention suggests the intervention effectively reduced PFNA exposure via drinking water.

A hybrid approach to predict daily NO2 concentrations at city block scale.

Estimating the ambient concentration of nitrogen dioxide (NO2) is challenging because NO2 generated by local fossil fuel combustion varies greatly in concentration across space and time. This study demonstrates an integrated hybrid approach combining dispersion modeling and land use regression (LUR) to predict daily NO2 concentrations at a high spatial resolution (e.g., 50 m) in the New York tri-state area. The daily concentration of traffic-related NO2 was estimated at the Environmental Protection Agency's NO2 monitoring sites in the study area for the years 2015-2017, using the Research LINE source (R-LINE) model with inputs of traffic data provided by the Highway Performance and Management System and meteorological data provided by the NOAA Integrated Surface Database. We used the R-LINE-predicted daily concentrations of NO2 to build mixed-effects regression models, including additional variables representing land use features, geographic characteristics, weather, and other predictors. The mixed model was selected by the Elastic Net method. Each model's performance was evaluated using the out-of-sample coefficient of determination (R2) and the square root of mean squared error (RMSE) from ten-fold cross-validation (CV). The mixed model showed a good prediction performance (CV R2: 0.75-0.79, RMSE: 3.9-4.0 ppb). R-LINE outputs improved the overall, spatial, and temporal CV R2 by 10.0%, 18.9% and 7.7% respectively. Given the output of R-LINE is point-based and has a flexible spatial resolution, this hybrid approach allows prediction of daily NO2 at an extremely high spatial resolution such as city blocks.

Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States.

BACKGROUND: Spatial linear Land-Use Regression (LUR) is commonly used for long-term modeling of air pollution in support of exposure and epidemiological assessments. Machine Learning (ML) methods in conjunction with spatiotemporal modeling can provide more flexible exposure-relevant metrics and have been studied using different model structures. There is however a lack of comparisons of methods available within these two modeling frameworks, that can guide model/algorithm selection in air quality epidemiology. OBJECTIVE: The present study compares thirteen algorithms for spatial/spatiotemporal modeling applied for daily maxima of 8-hour running averages of ambient ozone concentrations at spatial resolutions corresponding to census tracts, to support estimation of annual ozone design values across the contiguous US. These algorithms were selected from nine representative categories and trained using predictors that included chemistry-transport model predictions, meteorological factors, land use and land cover, and stationary and mobile emissions. METHODS: To obtain the best predictive performance, model structures were optimized through a repeated coarse/fine grid search with expert knowledge. Six target-oriented validation strategies were used to prevent overfitting and avoid over-optimistic model evaluation results. In order to take full advantage of the power of different algorithms, we introduced tuning sample weights in spatiotemporal modeling to ensure predictive accuracy of peak concentrations, that is crucial for exposure assessments. In spatial modeling, four interpretation and visualization tools were introduced to explain predictions from different algorithms. RESULTS: Nonlinear ML methods achieved higher prediction accuracy than linear LUR, and the improvements were more significant for spatiotemporal modeling (nearly 10%-40% decrease of predicted RMSE). By tuning the sample weights, spatiotemporal models can predict concentrations used to calculate ozone design values that are comparable or even better than spatial models (nearly 30% decrease of cross-validated RMSE). We visualized the underlying nonlinear relationships, heterogeneous associations and complex interactions from the two best performing ML algorithms, i.e., Random Forest and Extreme Gradient Boosting, and found that the complex patterns were relatively less significant with respect to model accuracy for spatial modeling. CONCLUSION: Machine Learning can provide estimates that are actually more interpretable and practical than linear regression to improve accuracy in modeling human exposures. A careful design of hyperparameter tuning and flexible data splitting and validations is crucial to obtain reliable and stable results. Desirable/successful nonlinear models are expected to capture similar nonlinear patterns and interactions using different ML algorithms.

Development of a semi-mechanistic allergenic pollen emission model.

Modeling pollen emission processes is crucial for studying the spatiotemporal distributions of airborne allergenic pollen. A semi-mechanistic emission model was developed based on mass balance of pollen grain fluxes in the surroundings of allergenic plants. The emission model considers direct emission and resuspension and accounts for influences of temperature, wind velocity, and relative humidity. Modules of this emission model have been developed and parameterized with multiple years of pollen count observations to provide pollen season onset and duration, hourly flowering likelihood, and vegetation coverage for oak and ragweed, as two examples. The simulated spatiotemporal pattern of pollen emissions generally follows the corresponding pattern of area coverage of allergenic plants and diurnal pattern of hourly flowering likelihood. It is found that oak pollen emissions start from the Southern part of the Contiguous United States (CONUS) in March and then shift gradually toward the Northern CONUS, with a maximum emission flux of 5.8 × 106 pollen/(m2 h). On the other hand, ragweed pollen emissions start from the Northern CONUS in August and then shift gradually toward the Southern CONUS. The mean ragweed emission flux during August to September can increase up to 2.4 × 106 pollen/(m2 h). This emission model is robust with respect to the input parameters for oak and ragweed. Qualitative evaluations of the model performance indicated that the simulated pollen emission is strongly correlated with the plant coverages and observed pollen counts. This model could also be applied to other pollen species given the relevant parameters.

Enhanced exposure assessment and genome-wide DNA methylation in World Trade Center disaster responders.

DNA methylation has emerged as a promising target linking environmental exposures and cancer. The World Trade Center (WTC) responders sustained exposures to potential carcinogens, resulting in an increased risk of cancer. Previous studies of cancer risk in WTC-exposed responders were limited by the deficiency in quantitative and individual information on exposure to carcinogens. The current study introduces a new exposure-ranking index (ERI) for estimating cancer-related acute and chronic exposures, which aimed to improve the ability of future analyses to estimate cancer risk. An epigenome-wide association study based on DNA methylation and a weighted gene co-expression network analysis were carried out to identify cytosine-phosphate-guanosine (CpG) sites, modules of correlated CpG sites, and biological pathways associated with the new ERI. Methylation was profiled on blood samples using Illumina 450K Beadchip. No significant epigenome-wide association was found for ERI at a false discovery rate of 0.05. Several cancer-related pathways emerged in pathway analyses for the top ranking genes from epigenome-wide association study as well as enriched module from the weighted gene co-expression network analysis. The current study was the first DNA methylation study that aimed to identify methylation signature for cancer-related exposure in the WTC population. No CpG sites survived multiple testings adjustment. However, enriched gene sets involved in cancer, were identified in both acute and chronic ERIs, supporting the view that multiple genes play a role in this complex exposure.

Per and polyfluoroalkyl substances (PFAS) blood levels after contamination of a community water supply and comparison with 2013-2014 NHANES.

INTRODUCTION: Per and polyfluoroalkyl substances (PFAS), including perfluorononanoic acid (PFNA) and perfluorooctanoic acid (PFOA), were detected in the community water supply of Paulsboro New Jersey in 2009. METHODS: A cross-sectional study enrolled 192 claimants from a class-action lawsuit, not affiliated with this study, who had been awarded a blood test for 13 PFAS. Study participants provided their blood test results and completed a survey about demographics; 105 participants also completed a health survey. Geometric means, 25th, 50th, 75th, and 95th percentiles of exposure of PFNA blood serum concentrations were compared to that of the 2013-2014 NHANES, adjusted for reporting level. Associations between PFNA, PFOA, PFOS, and PFHxS and self-reported health outcomes were assessed using logistic regression. RESULTS: PFNA serum levels were 285% higher in Paulsboro compared with U.S. residents. PFNA serum levels were higher among older compared with younger, and male compared to female, Paulsboro residents. After adjustment for potential confounding, there was a significant association between increased serum PFNA levels and self-reported high cholesterol (OR: 1.15, 95% CI: 1.02, 1.29). DISCUSSION/CONCLUSION: Further investigation into possible health effects of PFAS exposure in Paulsboro and other community settings is warranted. Since exposure has ceased, toxicokinetics of PFAS elimination should be explored.

Preparedness and response to chemical and biological threats: the role of exposure science.

There are multiple components to emergency preparedness and the response to chemical and biological threat agents. The 5Rs framework (rescue, reentry, recovery, restoration, and rehabitation) outlines opportunities to apply exposure science in emergency events. Exposure science provides guidance and refined tools for characterizing, assessing, and reducing risks from catastrophic events, such as the release of hazardous airborne chemicals or biological agents. Important challenges to be met include deployment of assets, including medications, before and after an emergency response situation. Assessment of past studies demonstrates the value of integrating exposure science methods into risk analysis and the management of catastrophic events.

Modulation of Human Macrophage Responses to Mycobacterium tuberculosis by Silver Nanoparticles of Different Size and Surface Modification.

Exposure to silver nanoparticles (AgNP) used in consumer products carries potential health risks including increased susceptibility to infectious pathogens. Systematic assessments of antimicrobial macrophage immune responses in the context of AgNP exposure are important because uptake of AgNP by macrophages may lead to alterations of innate immune cell functions. In this study we examined the effects of exposure to AgNP with different particle sizes (20 and 110 nm diameters) and surface chemistry (citrate or polyvinlypyrrolidone capping) on cellular toxicity and innate immune responses against Mycobacterium tuberculosis (M.tb) by human monocyte-derived macrophages (MDM). Exposures of MDM to AgNP significantly reduced cellular viability, increased IL8 and decreased IL10 mRNA expression. Exposure of M.tb-infected MDM to AgNP suppressed M.tb-induced expression of IL1B, IL10, and TNFA mRNA. Furthermore, M.tb-induced IL-1ß, a cytokine critical for host resistance to M.tb, was inhibited by AgNP but not by carbon black particles indicating that the observed immunosuppressive effects of AgNP are particle specific. Suppressive effects of AgNP on the M.tb-induced host immune responses were in part due to AgNP-mediated interferences with the TLR signaling pathways that culminate in the activation of the transcription factor NF-κB. AgNP exposure suppressed M.tb-induced expression of a subset of NF-κB mediated genes (CSF2, CSF3, IFNG, IL1A, IL1B, IL6, IL10, TNFA, NFKB1A). In addition, AgNP exposure increased the expression of HSPA1A mRNA and the corresponding stress-induced Hsp72 protein. Up-regulation of Hsp72 by AgNP can suppress M.tb-induced NF-κB activation and host immune responses. The observed ability of AgNP to modulate infectious pathogen-induced immune responses has important public health implications.

Modeling In Vivo Interactions of Engineered Nanoparticles in the Pulmonary Alveolar Lining Fluid.

Increasing use of engineered nanomaterials (ENMs) in consumer products may result in widespread human inhalation exposures. Due to their high surface area per unit mass, inhaled ENMs interact with multiple components of the pulmonary system, and these interactions affect their ultimate fate in the body. Modeling of ENM transport and clearance in vivo has traditionally treated tissues as well-mixed compartments, without consideration of nanoscale interaction and transformation mechanisms. ENM agglomeration, dissolution and transport, along with adsorption of biomolecules, such as surfactant lipids and proteins, cause irreversible changes to ENM morphology and surface properties. The model presented in this article quantifies ENM transformation and transport in the alveolar air to liquid interface and estimates eventual alveolar cell dosimetry. This formulation brings together established concepts from colloidal and surface science, physics, and biochemistry to provide a stochastic framework capable of capturing essential in vivo processes in the pulmonary alveolar lining layer. The model has been implemented for in vitro solutions with parameters estimated from relevant published in vitro measurements and has been extended here to in vivo systems simulating human inhalation exposures. Applications are presented for four different ENMs, and relevant kinetic rates are estimated, demonstrating an approach for improving human in vivo pulmonary dosimetry.

Predicting Onset and Duration of Airborne Allergenic Pollen Season in the United States.

Allergenic pollen is one of the main triggers of Allergic Airway Disease (AAD) affecting 5% to 30% of the population in industrialized countries. A modeling framework has been developed using correlation and collinearity analyses, simulated annealing, and stepwise regression based on nationwide observations of airborne pollen counts and climatic factors to predict the onsets and durations of allergenic pollen seasons of representative trees, weeds and grass in the contiguous United States. Main factors considered are monthly, seasonal and annual mean temperatures and accumulative precipitations, latitude, elevation, Growing Degree Day (GDD), Frost Free Day (FFD), Start Date (SD) and Season Length (SL) in the previous year. The estimated mean SD and SL for birch (Betula), oak (Quercus), ragweed (Ambrosia), mugwort (Artemisia) and grass (Poaceae) pollen season in 1994-2010 are mostly within 0 to 6 days of the corresponding observations for the majority of the National Allergy Bureau (NAB) monitoring stations across the contiguous US. The simulated spatially resolved maps for onset and duration of allergenic pollen season in the contiguous US are consistent with the long term observations.

Allergenic pollen season variations in the past two decades under changing climate in the United States.

Many diseases are linked with climate trends and variations. In particular, climate change is expected to alter the spatiotemporal dynamics of allergenic airborne pollen and potentially increase occurrence of allergic airway disease. Understanding the spatiotemporal patterns of changes in pollen season timing and levels is thus important in assessing climate impacts on aerobiology and allergy caused by allergenic airborne pollen. Here, we describe the spatiotemporal patterns of changes in the seasonal timing and levels of allergenic airborne pollen for multiple taxa in different climate regions at a continental scale. The allergenic pollen seasons of representative trees, weeds and grass during the past decade (2001-2010) across the contiguous United States have been observed to start 3.0 [95% Confidence Interval (CI), 1.1-4.9] days earlier on average than in the 1990s (1994-2000). The average peak value and annual total of daily counted airborne pollen have increased by 42.4% (95% CI, 21.9-62.9%) and 46.0% (95% CI, 21.5-70.5%), respectively. Changes of pollen season timing and airborne levels depend on latitude, and are associated with changes of growing degree days, frost free days, and precipitation. These changes are likely due to recent climate change and particularly the enhanced warming and precipitation at higher latitudes in the contiguous United States.

Manganese concentrations in soil and settled dust in an area with historic ferroalloy production.

Ferroalloy production can release a number of metals into the environment, of which manganese (Mn) is of major concern. Other elements include lead, iron, zinc, copper, chromium, and cadmium. Mn exposure derived from settled dust and suspended aerosols can cause a variety of adverse neurological effects to chronically exposed individuals. To better estimate the current levels of exposure, this study quantified the metal levels in dust collected inside homes (n=85), outside homes (n=81), in attics (n=6), and in surface soil (n=252) in an area with historic ferroalloy production. Metals contained in indoor and outdoor dust samples were quantified using inductively coupled plasma optical emission spectroscopy, whereas attic and soil measurements were made with a X-ray fluorescence instrument. Mean Mn concentrations in soil (4600 µg/g) and indoor dust (870 µg/g) collected within 0.5 km of a plant exceeded levels previously found in suburban and urban areas, but did decrease outside 1.0 km to the upper end of background concentrations. Mn concentrations in attic dust were ~120 times larger than other indoor dust levels, consistent with historical emissions that yielded high airborne concentrations in the region. Considering the potential health effects that are associated with chronic Mn inhalation and ingestion exposure, remediation of soil near the plants and frequent, on-going hygiene indoors may decrease residential exposure and the likelihood of adverse health effects.

Modeling in vitro cellular responses to silver nanoparticles.

Engineered nanoparticles (NPs) have been widely demonstrated to induce toxic effects to various cell types. In vitro cell exposure systems have high potential for reliable, high throughput screening of nanoparticle toxicity, allowing focusing on particular pathways while excluding unwanted effects due to other cells or tissue dosimetry. The work presented here involves a detailed biologically based computational model of cellular interactions with NPs; it utilizes measurements performed in human cell culture systems in vitro, to develop a mechanistic mathematical model that can support analysis and prediction of in vivo effects of NPs. The model considers basic cellular mechanisms including proliferation, apoptosis, and production of cytokines in response to NPs. This new model is implemented for macrophages and parameterized using in vitro measurements of changes in cellular viability and mRNA levels of cytokines: TNF, IL-1b, IL-6, IL-8, and IL-10. The model includes in vitro cellular dosimetry due to nanoparticle transport and transformation. Furthermore, the model developed here optimizes the essential cellular parameters based on in vitro measurements, and provides a "stepping stone" for the development of more advanced in vivo models that will incorporate additional cellular and NP interactions.