A Novel Transgenic Zebrafish Line Reporting Dynamic Epigenetic Changes
Currently, there is no other whole-animal reporter for epigenetic regulation established in any vertebrate.
A3 Adenosine Receptor Positive Allosteric Modulators
Selective A3AR agonists are sought as potential agents for treating inflammatory diseases,
chronic pain, cancer and non-alcoholic steatohepatitis (NASH). NIDDK investigators have invented
new chemical composition as positive allosteric modulators (PAMs) of the A3AR. These chemical
compounds contain sterically constrained, bridged modifications and cycloalkyl rings of various
sizes, as well as modifications of the 4-arylamino group. The compounds have added
Gene Therapy Vector for the Treatment of Glycogen Storage Disease Type Ia (GSD-Ia)
GSD-Ia is an inherited disorder of metabolism associated with life-threatening hypoglycemia, hepatic malignancy, and renal failure caused by the deficiency of glucose-6-phosphatase-alpha (G6Pase-alpha or G6PC). Current therapy, which primarily consists of dietary modification, fails to prevent long-term complications in many patients, including growth failure, gout, pulmonary hypertension, renal dysfunction, osteoporosis, and hepatocellular adenomas (HCA).
Assay to Screen Anti-metastatic Drugs
Scientists at the NCI developed a research tool, a murine cell line model (JygMC(A)) with a reporter construct, of spontaneous metastatic mammary carcinoma that resembles the human breast cancer metastatic process in a triple negative mammary tumor. The assay is useful for screening compounds that specifically inhibit pathways involved in mammary carcinoma and can improve clinical management of of triple negative breast cancer that are greatly refractory to conventional chemo and radiotherapy.
Robotic Exoskeleton for Treatment of Crouch Gait in Children with Cerebral Palsy (CP)
Crouch gait is a common disorder in pediatric cerebral palsy (CP). Effective treatment of crouch during childhood is critical to maintain mobility into adulthood. Current interventions do not alleviate crouch gait long-term for most patients. This technology relates to a powered exoskeleton designed for gait assistance. The powered assistance may provide a physical therapy-type intervention to improve and maintain mobility.
Method and System of Building Hospital-Scale Medical Image Database
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.
Computer-Aided Diagnostic for Use in Multiparametric MRI for Prostate Cancer
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.
Progressive and Multipath Neural Network for Medical Image Segmentation
The technology relates to software, systems, and methods for automated medical image segmentation via deep learning. Standard holistically-nested networks (HNNs) used in computer vision and medical imaging for segmentation and edge detection have a problem with coarsening resolution. The described technology addresses this.
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Medical image datasets are an important clinical resource. Effectively referencing patient images against similar related images and case histories can inform and produce better treatment outcomes. Labeling and identifying disease features and relations between images within a large image database has not been a task capable of automation. Rather, it is a task that must be performed by highly trained clinicians who can identify and label the medically meaningful image features.
Convolutional Neural Networks for Organ Segmentation
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.