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.
High-Resolution and Artifact-Free Measurement and Visualization of Tissue Strain by Processing MRI Using a Deep Learning Approach
Intranasal Nebulizer with Disposable Drug Cartridge for Improved Delivery of Vaccines and Therapeutics
Method for Finding Usable Portion of Sigmoid Curve (the Taylor Method), Improved Assay Readouts, and Enhanced Quality Control/Assurance
Multi-Antigenic Peptide(s) Vaccine and Immunogen for Conferring Streptococcus pneumoniae Immunity
Methods for Amelioration and Treatment of Pathogen-associated Inflammatory Response
Human iPSC-Derived Mesodermal Precursor Cells and Differentiated Cells
Resolution Enhancement for Light Sheet Microscopy Systems
Rabbit Antisera to Various Matrix, Matricellular, and Other Secreted Proteins
The extracellular matrix (ECM) is composed of a group of proteins that regulate many cellular functions, such as cell shape, adhesion, migration, proliferation, and differentiation. Deregulation of ECM protein production or function contributes to many pathological conditions, including asthma, chronic obstructive pulmonary disease, arthrosclerosis, and cancer. Scientists at the NIH have developed antisera against various ECM components such as proteoglycan, sialoprotein, collagen, etc.. These antisera can be used as research tools to study the biology of extracellular matrix molecules.