Seed grants are awarded with funding provided by the Binghamton University Road Map through the Provost's Office and the Division of Research.
The goal of these seed grants is to encourage faculty to develop collaborative projects that stimulate the advancement of new ideas that can build Binghamton University's expertise toward a national reputation in the broad area of health sciences. This competitive, peer-reviewed program is providing initial support for proposed long-term programs of collaborative research that have strong potential to attract external funding.
The call for proposals for seed grant funding for the 2020-2021 academic year, including an overview, an explanation of the process and eligibility, a proposal cover page and a proposal budget page is available on this website. The deadline to apply for a Health Sciences TAE seed grant is March 1, 2020.
In addition to the standard seed grants, the Health Sciences TAE is also offering one Community Partnership Seed Grant for 2019-2020 that may be funded for up to $25,000 for one year. Specific eligibility requirements for this seed grant are noted in the following addendum to the above call for proposals.
- Proposals must be used to support a project with a new community partner; they may not be used to support existing collaborations.
- Community partnerships must be with entities broadly in health sciences (e.g., health care providers, community agencies and organizations, etc.) within Broome, Tioga, Delaware and Chenango counties.
- Proposals should define the roles and responsibilities of the Binghamton University researcher(s) and Southern Tier community partner(s).
- Seed grant funds may only be applied to Binghamton University efforts.
- Up to $25,000 in funds for one year may be requested for support.
- A letter of support must be provided by the community partner.
- “Community Partnership Seed Grant Proposal:” must be indicated at the beginning of the project title on the cover page.
For the 2018–2019 academic year, the following seed grants were awarded:
Engineering a Gut Microbiome
Gretchen Mahler, biomedical engineering, and Claudia Marques, biological sciences
Microorganisms colonize the human gastrointestinal (GI) tract and comprise the resident human microbiota. A healthy gut microbiome is critical to regulating metabolism, promoting immune function, and eliminating xenobiotics, and changes in the number or composition of microbes may lead to pathophysiologic conditions. In this work we will engineer a mock community of upper GI bacteria, incorporate this community into a human GI tract model, and determine the effects of food additive exposure on microbial community dynamics and epithelial cell function. This system, which will be the first to model upper GI conditions using a physiologically realistic, reproducible method with human-derived cells, will allow quantitative assessment of the contributions of bacteria toward GI function and the ability to determine how what we eat governs microbial dynamics.
Smart Computer-Aided Diagnosis: Machine Learning Framework for Bio-Medical Image Segmentation
Dae Han Won, systems science and industrial engineering, and Fake (Frank) Lu, biomedical engineering
Advances in imaging technology have led to a proliferation of various bio-medical image. Simultaneously, computer-aided diagnosis (CAD) is beginning to be applied widely in the detection and differential diagnosis of abnormalities in medical images. Image segmentation, which delineates the specified regions (e.g., tumors), is one of the important component of CAD. In this study, we propose to develop fully automated two-dimensional (2D) and three-dimensional (3D) medical image segmentation pipeline using deep convolutional neural networks (CNNs). We will employee the data from two-cutting edge medical optical technologies: optical coherence tomography (OCT) and stimulated Raman scattering (SRS) microscopy to develop and validate our models. Research aims of this project are: (1) designing baseline architectures of CNNs for both 2D and 3D medical image segmentation, (2) modifying and optimizing the 2D/3D CNN models for medical imaging applications, and (3) applying the models to actual imaginary data and segmenting cancerous regions for Head and Neck cancer in OCT and glioma brain tumor in SRS. Eventually, our computational framework will provide a valuable guideline for pathological investigation as well as for surgical interventions with a great potential for future clinical use.
Understanding Antibody-Drug-Conjugate (ADC) Internalization via Stimulated Raman Scattering (SRS) Imaging of Alkyne Tags in Live Cancer Cells
Fake (Frank) Lu, biomedical engineering, and L. Nathan Tumey, pharmaceutical sciences
Antibody-drug-conjugate (ADC) technology is a rapidly growing area of pharmaceutical research that has resulted in more than 60 clinical trials and four approved drugs. This "targeted delivery" technology is deceptively simple: the antibody serves as a drug-carrier that directs the agent to antigen-expressing tissues of interest whereby the ADC is internalized, trafficked to the lysosome, and degraded thus resulting in drug-release. In spite of the conceptual elegance of this approach, the process of ADC internalization and processing remains poorly understood. Most of what we know about the internalization process relies on microscopic imaging using large fluorescent tags. The heavy fluorescent tags with high molecular weight may affect pharmacodynamics and pharmacokinetics of the drugs, thus impacting our understanding of the internalization and trafficking process. New imaging techniques are needed to allow for spatial and temporal resolution of the internalization process. Stimulated Raman scattering (SRS) microscopy is an emerging technology for rapid chemical bond imaging based on Raman contrasts, without the use of fluorescent tags. Imaging speed of SRS microscopy is several orders of magnitude faster than Raman confocal mapping, making it a powerful tool for fast imaging of fresh and dynamic samples, up to video rate. The goal of this project is to develop and optimize SRS imaging methodology for the study of ADCs that contain small Raman tags, such as an alkyne or cyano group. The ADCs will be administered to human breast cancer cells and we will perform time-lapse imaging of the live cells to investigate the processes and dynamics of ADCs cell surface binding, internalization, trafficking and payload release in the lysosomes. We believe that SRS microscopy will enable studies of ADC internalization and trafficking that are either not possible or not practical using existing fluorescence and mass-spectroscopy techniques.