The proposed project will develop and optimize an algorithm to help brain scientists identify causes and treatments for neurological disease and mental illness. Though the proposed technology will initially target research studies to understand and treat all neurological conditions, it can be useful for automatic assessments of cells in all tissues, including cancer screening and diagnosis from biopsies. Stereology plays an important role in investigating many conditions affecting the brain and assessing the efficacy and safety of possible treatments. In contrast, children with autism are born with too many brain cells, which leads to life-long problems in processing complex streams of information. For reasons that are currently unknown, Alzheimer's disease, Parkinson's disease and Amyotrophic Lateral Sclerosis are all associated with a progressive loss of brain cells. Unbiased stereology allows neuroscientists to accurately analyze the size and number of brain cells, which are altered in many neurological disorders and mental illnesses. The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is in automating the process of unbiased stereology, the state-of-the-method used in the life sciences for counting stained cells on tissue sections.
Primary Place of Performance Congressional District: Peter Mouton (Principal Investigator) Dmitry Goldgof (Co-Principal Investigator).
Ruth Shuman (703)292-2160 IIP Div Of Industrial Innovation & Partnersh ENG Directorate For Engineering IIP Div Of Industrial Innovation & Partnersh STTR Phase I: Microscope-based Technology For Automatic Brain Cell Counts Using Unbiased Methods NSF Org:
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