BSC Weekly Meeting: Dr. Anahid Jewett and Dr. Ariana Anderson

  • Tuesday, October 22, 2013
  • 11:30-12:30
  • UCLA
    BSRB 154
  • ,
We will have two UCLA faculty present their technologies to the class. If you are interested in the technology presented, you can join a Venture Team to explore the feasibility and commercialization potential of the technology. Your participation in a Venture Team will count towards our Certificate Program.
Dr. Anahid Jewett 
Improvement of Dental Resins: Decreased Toxicity and Improved Biocompatibility 
UCLA investigators have discovered that the presence of a confidential chemical inhibitor (CI) can inhibit HEMA, and TEGDMA-mediated apoptosis in various cell lines, including human and rat dental pulp stromal cells, immortalized human Oral Keratinocytes, human fibroblast cell line, the murine RAW 264.7 cell line, the human THP1 macrophage cell line, and the HaCaT skin keratinocyte cell line. Not only was cell death inhibited, but the presence of the CI also led to an increased viability and function of HEMA and TEGDMA treated cells. This in vitro data has been confirmed with in vivo rat models demonstrating that this CI can inhibit cell death induced by composites and bleaching systems. In addition, these rat models show the ability of this CI to restore the function of dental pulp stromal cells. The results indicate that CI prevents adverse effects mediated by HEMA, TEGDMA and bleaching agents.
Dr. Ariana Anderson 
Reducing Clinical Trial Costs by Detecting and Measuring the Placebo Effect and Treatment Effect Using Brain Imaging 
Researchers at UCLA have developed a technique that optimizes and identifies “placebo networks” in the brain using functional magnetic resonance imaging (fMRI), but these methods are applicable in general to many brain-imaging modalities such as EEG and PET.Using Blind Signal Separation (BSS) methods, a brain scan can be decomposed into underlying signal sources that operate in either time or space and brain networks corresponding to the placebo effect(s) can be identified using computational machine learning techniques (sometimes called, “pattern recognition”), providing a means to quantitatively measure the placebo effect(s) in the brain. Subjects would be scanned pre and post-treatment using a functional brain imaging technique. The relative change in activity in the “placebo networks” between treatment conditions would be a measure of the placebo’s power, and these values would be used to update estimates of the treatment effect and placebo effect(s).
All are welcome! We look forward to working together to support innovation and entrepreneurship at UCLA.