Superfund Research Program

Adam T. Szafran

Baylor College of Medicine
One Baylor Plaza
Cullen Rm. 125A
Houston, Texas 77030-0000
Phone: 713-798-5589
Email: adam.szafran@bcm.edu

Projects

• Texas A&M University: Single Cell, Multi-Parametric High Throughput Platform to Classify Endocrine Disruptor Potential of Mixtures (2017-2022)

Publications

2024

• Abbott D, Mancini MG, Bolt MJ, Szafran AT, Neugebauer K, Stossi F, Gorelick D, Mancini MA. 2024. A novel ERβ high throughput microscopy platform for testing endocrine disrupting chemicals. Heliyon 10(1):doi:10.1016/j.heliyon.2023.e23119 PMID:38169792 PMCID:10758781

2023

• Aghayev Z, Szafran AT, Tran A, Ganesh HS, Stossi F, Zhou L, Mancini MA, Pistikopoulos EN, Beykal B. 2023. Machine learning methods for endocrine disrupting potential identification based on single-cell data. Chem Eng Sci 281:119086. doi:10.1016/j.ces.2023.119086 PMID:37637227 PMCID:PMC10448728
• Aghayev Z, Walker GF, Iseri F, Ali M, Szafran AT, Stossi F, Mancini MA, Pistikopoulos EN, Beykal B. 2023. Binary classification of the endocrine disrupting chemicals by artificial neural networks. ESCAPE 52:2631-2636. doi:10.1016/b978-0-443-15274-0.50418-2 PMID:37575176 PMCID:PMC1041341

2022

• Stossi F, Singh PK, Mistry R, Johnson H, Dandekar RD, Mancini MG, Szafran AT, Rao A, Mancini MA. 2022. Quality control for single cell imaging analytics using endocrine disruptor-induced changes in estrogen receptor expression. Environ Health Perspect 130(2):27008. doi:10.1289/EHP9297 PMID:35167326 PMCID:PMC8846386
• Szafran AT, Mancini MG, Stossi F, Mancini MA. 2022. Sensitive image-based chromatin binding assays using inducible ER-alpha to rapidly characterize estrogenic chemicals and mixtures. iScience 25(10):105200. doi:10.1016/j.isci.2022.105200 PMID:36238893 PMCID:PMC9550643

2021

• Bolt MJ, Singh PK, Obkirchner CE, Powell R, Mancini MG, Szafran AT, Stossi F, Mancini MA. 2021. Endocrine disrupting chemicals differentially alter intranuclear dynamics and transcriptional activation of estrogen receptor-(alpha). iScience 24(11):103227. doi:10.1016/j.isci.2021.103227 PMID:34712924 PMCID:PMC8529556
• Ganesh HS, Beykal B, Szafran AT, Stossi F, Zhou L, Mancini MA, Pistikopoulos EN. 2021. Predicting the estrogen receptor activity of environmental chemicals by single-cell image analysis and data-driven modeling. ESCAPE 50:481-486. doi:10.1016/b978-0-323-88506-5.50076-0 PMID:34355221 PMCID:PMC8331057

2020

• Mukherjee R, Beykal B, Szafran AT, Onel M, Stossi F, Mancini MG, Lloyd D, Wright FA, Zhou L, Mancini MA, Pistikopoulos EN. 2020. Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms. PLoS Comput Biol 16:24. doi:10.1371/journal.pcbi.1008191 PMID:32970665 PMCID:PMC7538107
• Stossi F, Mistry R, Singh PK, Johnson H, Mancini MG, Szafran AT, Mancini MA. 2020. Single cell distribution analysis of AR levels by high throughput microscopy in cell models: Application for testing endocrine disrupting chemicals. SLAS Discov 11:doi:10.1177/2472555220934420 PMID:32552291
• Szafran AT, Bolt MJ, Obkirchner CE, Mancini MG, Helsen C, Claessens F, Stossi F, Mancini MA. 2020. A mechanistic high-content analysis assay using a chimeric androgen receptor that rapidly characterizes androgenic chemicals. SLAS Discov 14:doi:10.1177/2472555220922917 PMID:32392092

2019

• Kuthurua S, Szafran AT, Stossi F, Mancini MA, Rao A. 2019. Leveraging image-derived phenotypic measurements for drug-target interaction predictions. Cancer Inform doi:10.1177/1176935119856595 PMID:31217689 PMCID:PMC6563400
• Mukherjee R, Onel M, Beykal B, Szafran AT, Stossi F, Mancini MA, Zhou L, Wright FA, Pistikopoulos EN. 2019. Development of the Texas A&M Superfund Research Program computational platform for data integration, visualization, and analysis. Computer Aided Chemical Engineering 46:967-972. doi:10.1016/B978-0-12-818634-3.50162-4 PMID:31612156 PMCID:PMC6791821
• Xia J, Chiu L, Nehring R, Bravo Nunez M, Mei Q, Perez M, Zhai Y, Fitzgerald D, Pribis J, Wang Y, Hu C, Powell R, LaBonte S, Jalali A, Matadamas Guzman M, Lentzsch A, Szafran AT, Joshi M, Richters M, Gibson J, Frisch R, Hastings P, Bates D, Queitsch C, Hilsenbeck SG, Coarfa C, Hu JC, Siegele DA, Scott KL, Liang H, Mancini MA, Herman C, Miller KM, Rosenberg SM. 2019. Bacteria-to-human protein networks reveal origins of endogenous DNA damage. Cell 176(1-2):127-143. doi:10.1016/j.cell.2018.12.008 PMID:30633903 PMCID:PMC6344048

2017

• Szafran AT, Stossi F, Mancini MG, Walker CL, Mancini MA. 2017. Characterizing properties of non-estrogenic substituted bisphenol analogs using high throughput microscopy and image analysis. PLoS One 12(7):e0180141. doi:10.1371/journal.pone.0180141 PMID:28704378 PMCID:PMC5509144