This invention describes a general way to trigger the release of a bioactive small molecule from a targeting antibody. The key “trigger” is a fluorescent linker that is chemically disassembled upon irradiation with light in the near-IR range (~800 nm).
Immunotherapy is a promising method of treating cancer that leverages the immune system to promote tumor rejection. However, certain somatic mutations in cancer cells confer resistance to T cell-mediated cytolysis. To improve the effectiveness of immunotherapies for cancer, there exists a need to prospectively identify patients who are most likely to respond to such therapies.
The invention relates to novel lipid-based nanoparticles (liposomes) for use in targeted, on demand and on site drug delivery. The particles include a wall surrounding a cavity, wherein the wall is comprised of:
phosphoethanolamine-N-[methoxy(polyethylene glycol)-2000] (DSPE-PEG2000), and
Tissue obtained for both clinical and research purposes is routinely frozen, commonly in Optimal Cutting Temperature (OCT), an embedding media, for eventual downstream analysis, commonly including sectioning on a cryostat. Though OCT is the standard compound used for freezing, there is no standard freezing protocol. Thus, current methods of handling, labeling, and storing OCT-embedded tissue vary widely, and specimens are often damaged or degraded due to undesirable temperature fluctuations during handling and freezing.
Adoptive cell transfer (ACT) is a promising immunotherapeutic approach for cancer treatment. During ACT, if a patient is subjected to lymphodepletion prior to cell transfer, there is an observed improvement in a patient’s response to treatment. However, lymphodepletion is associated with detrimental effects, including severe immune dysfunction that persists after treatment.
Accurate automated organ and disease feature segmentation is a challenge for medical imaging analysis. The pancreas, for example, is a small, soft, organ with low uniformity of shape and volume between patients. Because of the lack of uniform image patterns, there are few features that can be used to aid in automated identification of anatomy and boundaries. Segmentation of high variability features is uniquely difficult for a computer to perform.
Multiparametric MRI improves image detail and prostate cancer detection rates compared to standard MRI. Computer aided diagnostics (CAD) used in combination with multiparametric MRI images may further improve prostate cancer detection and visualization. The technology, developed by researchers at the National Institutes of Health Clinical Center (NIHCC), is an automated CAD system for use in processing and visualizing prostate lesions on multiparametric MRI images.
Accurate automated organ and disease feature segmentation is a challenge for medical imaging analysis. The pancreas, for example, is a small, soft, organ with low uniformity of shape and volume between patients. Because of the lack of uniform image patterns, there are few features that can be used to aid in automated identification of anatomy and boundaries. Segmentation of high variability features is uniquely difficult for a computer to perform.
Developing computer systems that can recognize and locate image features associated with disease is a challenge for developing fully-automated and high precision computer assisted diagnostics. Joint learning of language tasks in association with vision tasks (association of image features with text annotation) adds an additional level of challenge. Furthermore, scaling-up approaches from small to large datasets presents additional issues, particularly related to medical images.
Microduplications of the GPR101 gene (located on chromosome Xq26.3 and encodes a G-protein coupled receptor) can result in an excess of growth hormone causing gigantism, that has an onset in early childhood. It is also associated with the growth of sporadic growth hormone producing adenomas in some patients with acromegaly.