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Shao K. and A.J. Shapiro (2018). A Web-Based System for Bayesian Benchmark Dose Estimation. Environ Health Perspect. 126(1):017002. https://doi.org/10.1289/EHP1289.

Alamri FS, Boone EL, Edwards DJ: A monotonic nonparametric dose-response model. Human and Ecological Risk Assessment 2021, 27(8) https://doi.org/10.1080/10807039.2021.1956298.

Chang Y, Celine Thanh Thu H, Bastin KM, Rivera BN, Siddens LK, Tilton SC: Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures. Toxicology In Vitro 2020, 69:104991. https://doi.org/10.1016/j.tiv.2020.104991.

Chen Q, Chou W-C, Lin Z: Integration of Toxicogenomics and Physiologically Based Pharmacokinetic Modeling in Human Health Risk Assessment of Perfluorooctane Sulfonate. Environmental Science & Technology 2022, 56(6):3623-3633 https://doi.org/10.1021/acs.est.1c06479.

Chiu WA, Axelrad DA, Dalaijamts C, Dockins C, Shao K, Shapiro AJ, Paoli G: Beyond the RID: Broad Application of a Probabilistic Approach to Improve Chemical Dose Response Assessments for Noncancer Effects. Environmental Health Perspectives 2018, 126(6):067009 https://doi.org/10.1289/ehp3368.

Chiu WA, Paoli GM: Recent Advances in Probabilistic Dose-Response Assessment to Inform Risk-Based Decision Making. Risk Anal 2021, 41(4):596-609 https://doi.org/10.1111/risa.13595.

Chou W-C, Lin Z: Probabilistic human health risk assessment of perfluorooctane sulfonate (PFOS) by integrating in vitro, in vivo toxicity, and human epidemiological studies using a Bayesian-based dose-response assessment coupled with physiologically based pharmacokinetic (PBPK) modeling approach. Environment International 2020, 137:105581 https://doi.org/10.1016/j.envint.2020.105581.

De Pretis F, Landes J, Osimani B: E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance. Front Pharmacol 2019, 10:1317 https://doi.org/10.3389/fphar.2019.01317.

De Pretis F, Osimani B: New Insights in Computational Methods for Pharmacovigilance: E-Synthesis, a Bayesian Framework for Causal Assessment. Int J Environ Res Public Health 2019, 16(12) https://doi.org/10.3390/ijerph16122221.

Green JW, Foudoulakis M, Fredricks T, Bean T, Maul J, Plautz S, Valverde P, Schapaugh A, Sopko X, Gao Z: Statistical analysis of avian reproduction studies. Environmental Sciences Europe 2022, 34(1):31 https://doi.org/10.1186/s12302-022-00603-5.

Hatherell S, Baltazar MT, Reynolds J, Carmichael PL, Dent M, Li H, Ryder S, White A, Walker P, Middleton AM: Identifying and Characterizing Stress Pathways of Concern for Consumer Safety in Next-Generation Risk Assessment. Toxicological Sciences 2020, 176(1):11-33 https://doi.org/10.1093/toxsci/kfaa054.

Hsieh N-H, Chen Z, Rusyn I, Chiu WA: Risk Characterization and Probabilistic Concentration-Response Modeling of Complex Environmental Mixtures Using New Approach Methodologies (NAMs) Data from Organotypic in Vitro Human Stem Cell Assays. Environmental Health Perspectives 2021, 129(1):017004 https://doi.org/10.1289/ehp7600.

Jensen SM, Kluxen FM, Streibig JC, Cedergreen N, Ritz C: bmd: an R package for benchmark dose estimation. PeerJ 2020, 8:e10557 https://doi.org/10.7717/peerj.10557.

Ji C, Weissmann A, Shao K: A computational system for Bayesian benchmark dose estimation of genomic data in BBMD. Environment International 2022, 161:107135 https://doi.org/10.1016/j.envint.2022.107135.

Jo S, Park B, Chung Y, Kim J, Lee E, Lee J, Choi T: Bayesian semiparametric mixed effects models for meta-analysis of the literature data : An application to cadmium toxicity studies. Statistics in Medicine 2021, 40(16):3762-3778 https://doi.org/10.1002/sim.8996.

Korchevskiy A: Using benchmark dose modeling for the quantitative risk assessment: Carbon nanotubes, asbestos, glyphosate. Journal of Applied Toxicology 2021, 41(1) https://doi.org/10.1002/jat.4063.

Kullar SS, Shao K, Surette C, Foucher D, Mergler D, Cormier P, Bellinger DC, Barbeau B, Sauve S, Bouchard MF: A benchmark concentration analysis for manganese in drinking water and IQ deficits in children. Environment International 2019, 130:104889 https://doi.org/10.1016/j.envint.2019.05.083.

Kvasnicka J, Stylianou KS, Nguyen VK, Huang L, Chiu WA, Burton GA, Jr., Semrau J, Jolliet O: Human Health Benefits from Fish Consumption vs. Risks from Inhalation Exposures Associated with Contaminated Sediment Remediation: Dredging of the Hudson River. Environmental Health Perspectives 2019, 127(12):127004 https://doi.org/10.1289/ehp5034.

Lent EM, Leach G, Johnson MS: Development of health-based environmental screening levels for insensitive munitions constituents. Human and Ecological Risk Assessment 2021, 27(6) https://doi.org/10.1080/10807039.2020.1859352.

Lent EM, Sussan TE, Leach GJ, Johnson MS: Using Evidence Integration Techniques in the Development of Health-Based Occupational Exposure Levels. International Journal of Toxicology 2021, 40(2):1091581820970494 https://doi.org/10.1177/1091581820970494.

Li X, He X, Chen S, Guo X, Bryant MS, Guo L, Manjanatha MG, Zhou T, Witt KL, Mei N: Evaluation of pyrrolizidine alkaloid-induced genotoxicity using metabolically competent TK6 cell lines. Food and Chemical Toxicology 2020, 145:111662 https://doi.org/10.1016/j.fct.2020.111662.

Li X, He X, Le Y, Guo X, Bryant MS, Atrakchi AH, McGovern TJ, Davis-Bruno KL, Keire DA, Heflich RH et al: Genotoxicity evaluation of nitrosamine impurities using human TK6 cells transduced with cytochrome P450s. Archives of Toxicology 2022, 96(11) https://doi.org/10.1007/s00204-022-03347-6.

Li X, Ni M, Xiong W, Tian L, Zhang L, Chen J: Transcriptomics analysis and benchmark concentration estimating-based in vitro test with IOSE80 cells to unveil the mode of action for female reproductive toxicity of bisphenol A at human-relevant levels. Ecotoxicology and Environmental Safety 2022, 237:113523 https://doi.org/10.1016/j.ecoenv.2022.113523.

Mikkonen AT, Martin J, Dourson ML, Hinwood A, Johnson MS: Suggestions for Improving the Characterization of Risk from Exposures to Per and Polyfluorinated Alkyl Substances. Environmental Toxicology and Chemistry 2021, 40(3, Sp. Iss. SI) https://doi.org/10.1002/etc.4931.

Pham LL, Watford S, Friedman KP, Wignall J, Shapiro AJ: Python BMDS: A Python interface library and web application for the canonical EPA dose-response modeling software. Reproductive Toxicology 2019, 90:102-108 https://doi.org/10.1016/j.reprotox.2019.07.013.

Shao K, Chen Q, Wang Z: Quantifying association between liver tumor incidence and early-stage liver weight increase - An NTP data analysis. Toxicol Rep 2019, 6:674-682 https://doi.org/10.1016/j.toxrep.2019.07.001.

Shao K, Shapiro AJ: A Web-Based System for Bayesian Benchmark Dose Estimation (vol 126, 017002, 2018). Environmental Health Perspectives 2022, 130(3):039002 https://doi.org/10.1289/ehp11205.

Shao K, Zhou Z, Xun P, Cohen SM: Bayesian benchmark dose analysis for inorganic arsenic in drinking water associated with bladder and lung cancer using epidemiological data. Toxicology 2021, 455:152752 https://doi.org/10.1016/j.tox.2021.152752.

Shi P, Yan H, Fan X, Xi S: A benchmark dose analysis for urinary cadmium and type 2 diabetes mellitus. Environmental Pollution 2021, 273:116519 https://doi.org/10.1016/j.envpol.2021.116519.

Simeone FC, Costa AL: Quantifying uncertainty in dose-response screenings of nanoparticles: a Bayesian data analysis. Nanotoxicology 2022, 16(2) https://doi.org/10.1080/17435390.2022.2038298.

Wang S, Zhang T, Liu X, Yang Z, Li L, Shan D, Gao Y, Li Y, Li Y, Zhang Y et al: Toxicity and toxicokinetics of the ethanol extract of Zuojin formula. BMC Complement Med Ther 2022, 22(1):220 https://doi.org/10.1186/s12906-022-03684-0.

Wang T, Tu Y, Zhang G, Gong S, Wang K, Zhang Y, Meng Y, Wang T, Li A, Christiani DC et al: Development of a benchmark dose for lead-exposure based on its induction of micronuclei, telomere length changes and hematological toxicity. Environment International 2020, 145:106129 https://doi.org/10.1016/j.envint.2020.106129.

Wheeler MW, Abrahantes JC, Aerts M, Gift JS, Davis JA: Continuous model averaging for benchmark dose analysis: Averaging over distributional forms. Environmetrics 2022, 33(5):e2728 https://doi.org/10.1002/env.2728.

Wheeler MW, Piegorsch WW, Bailer AJ: Quantal Risk Assessment Database: A Database for Exploring Patterns in Quantal Dose-Response Data in Risk Assessment and its Application to Develop Priors for Bayesian Dose-Response Analysis. Risk Anal 2019, 39(3):616-629 https://doi.org/10.1111/risa.13218.

Yang G, Li J, Wang Y, Chen C, Zhao H, Shao K: Quantitative ecotoxicity analysis for pesticide mixtures using benchmark dose methodology. Ecotoxicology and Environmental Safety 2018, 159:94-101 https://doi.org/10.1016/j.ecoenv.2018.04.055.

Yao M, Zeng Q, Luo P, Sun B, Liang B, Wei S, Xu Y, Wang Q, Liu Q, Zhang A: Assessing the risk of coal-burning arsenic-induced liver damage: a population-based study on hair arsenic and cumulative arsenic. Environmental Science and Pollution Research International 2021, 28(36) https://doi.org/10.1007/s11356-021-14273-y.

Yasuhiko Y, Ishigami M, Machino S, Fujii T, Aoki M, Irie F, Kanda Y, Yoshida M: Comparison of the lower limit of benchmark dose confidence interval with no-observed-adverse-effect level by applying four different software for tumorigenicity testing of pesticides in Japan. Regulatory Toxicology and Pharmacology 2022, 133:105201 https://doi.org/10.1016/j.yrtph.2022.105201.

Zou X, Wang R, Yang Z, Wang Q, Fu W, Huo Z, Ge F, Zhong R, Jiang Y, Li J et al: Family Socioeconomic Position and Lung Cancer Risk: A Meta-Analysis and a Mendelian Randomization Study. Front Public Health 2022, 10:780538 https://doi.org/10.3389/fpubh.2022.780538.