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Your Environment. Your Health.

News Items: Boston University

Superfund Research Program

Bioinformatics and Molecular Modeling Core

Project Leader: Stefano Monti
Co-Investigator: Sandor Vajda
Grant Number: P42ES007381
Funding Period: 2000-2021
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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News Items List

  • Chemicals alter fat cells, disrupt healthy metabolism
    Environmental Factor - November 2019
    Chemicals can work differently through the same biological receptor to generate different types of fat cells and disrupt healthy metabolic functions, according to Stephanie Kim, Ph.D., in an Oct. 21 lecture at NIEHS.
  • New Method Quickly Screens Chemicals for Cancer Risk
    Research Brief - June 2019
    Boston University (BU) researchers, in collaboration with researchers at the National Toxicology Program (NTP) and the Broad Institute, have developed and evaluated a new approach to assess whether exposure to a chemical increases a person's long-term cancer risk. The fast, cost-effective method uses gene expression profiling, which measures the activity of a thousand or more genes to capture what is happening in a cell. Based on gene expression profiling data, the researchers were able to infer specific biological changes at the cellular level and predict potential carcinogenicity of chemicals, or the ability of chemicals to cause cancer.
  • SRP Highlighted at Data Science Symposium
    SRP News Page - June 2017
    Superfund Research Program (SRP) representatives provided the environmental health perspective on big data at an NIH data science symposium May 16 - 18 in Cincinnati. The symposium was hosted by the NIH Big Data to Knowledge (BD2K) and Library of Integrated Network-Based Cellular Signatures (LINCS) Data Coordination and Integration Center.
  • A new approach to deterine cancer risk of chemicals
    Environmental Factor - September 2014
    A new study by NIEHS-funded researchers at Boston University (BU) and the NIEHS National Toxicology Program (NTP) has shown that computational models of short-term exposure to a chemical can predict long-term cancer risk. 
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