The National Cancer Institute (NCI) seeks research co-development opportunities and/or licensees for a new biomedical device for biopsy tissue collection and storage in a sterile, well-defined environment.
The National Cancer Institute (NCI) seeks research licensees for a process that reduces nucleic acid (RNA and DNA) degradation and improves protein integrity in tissue preserved as fixed paraffin embedded specimens.
The National Cancer Institute (NCI) seeks research, co-development, or licensing partners for software that uses computational approaches in cancer diagnosis. NCI researchers have recently developed a computational approach for detecting, quantifying, and mapping Mitotic Hotspots in whole slide images of tumor tissue. This technology has demonstrated high reproducibility that is unaffected by diagnostic skill or fatigue, allowing standardization of tumor cell proliferation assessment across institutions.
The National Cancer Institute (NCI) seeks licensees for an automated digital pathology device which integrates tissue sectioning, staining, and image acquisition. The device is compatible with high-throughput data analyses.
Extracellular vesicles (EVs) are lipid spheres released from cells. EVs contain proteins that can serve as diagnostic biomarkers indicating the cell state at time of release. Improved detection and phenotyping of EVs and their protein cargo could lead to better cancer diagnostic and prognostic tests, as well as improved therapeutic uses. The National Cancer Institute (NCI) seeks research co-development partners and/or licensees for a software package that performs high-throughput multi-dimensional analysis of EV biomarkers.
The National Cancer Institute''s Laboratory of Cell Biology is seeking statements of capability or interest from parties interested in collaborative research to further develop, evaluate, or commercialize bodipy conjugated tyrosine kinase inhibitors that are currently used in the clinic for the treatment of CML or gastric cancers.
Ultrasound-based cancer screening and biopsy imaging technique are a critical clinical need. Ultrasound based biopsy imaging can provide a real-time modality for lower cost that is comparable to, or complimentary to MRI imaging. Researchers at the NIH Clinical Center seek licensing and/or co-development research collaborations for Tissue Characterization with Acoustic Wave Tomosynthesis.
Scientists at The National Cancer Institute (NCI) and The National Institute of Neurological Disorders and Stroke (NINDS) have invented a method of imaging glucose metabolism in vivo using MRI chemical shift imaging (CSI) experiments that relies on a simple, but robust and efficient, post-processing procedure by the higher dimensional analog of singular value decomposition, tensor decomposition. This new technology is denoising software for MRIs that significantly improves the measurement of low-intensity signals without the need for dynamic nuclear polarization (DNP). The scientists seek research co-development partners and/or licensees for their invention.
Device is used to guide a stream of oxygen or carbon dioxide over a dish of cells during fluorescence microscopy. Invention includes the 3D printing software to create the device. The device makes it possible to easily provide a steady source of oxygen or carbon dioxide to cells while operating a fluorescent microscope to oxidize fluorophores for later visualization in electron microscopy. NCI seeks commercial partners to license this technology.
The National Cancer Institute (NCI) seeks licensees for humanized mice that express the human isomer of mesothelin (MSLN) in the thyroid. NCI created Bl6/TPO mice for studies of mesothelin as a target for research, diagnostic, or therapeutics involving human cancers.
The National Cancer Institute seeks licensees or research collaborators to develop and commercialize transgenic mice having immunocompetent rat growth hormone-firefly Luciferase-enhanced green fluorescent protein.
The National Institutes of Health - Clinical Center (NIH-CC) seeks to license and/or co-develop methods of reading chest x-rays using a deep learning models to detect a disease and describe its contents.