Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that are released from cells. EVs may contain proteins derived from their cells of origin with the potential as diagnostic biomarkers indicating the state of the cells when released. However, due to their small size (50-1000nm), the methods currently used to phenotype EVs have limited sensitivity and scale. A need exists for development of novel technologies improving EV detection and phenotyping.
Denoising of Dynamic Magnetic Resonance Spectroscopic Imaging Using Low Rank Approximations in the Kinetic Domain
Accurate measurement of low metabolite concentrations produced by medically important enzymes is commonly obscured by noise during magnetic resonance imaging (MRI). Measuring the turnover rate of low-level metabolites can directly quantify the activity of enzymes of interest, including possible drug targets in cancer and other diseases. Noise can cause the in vivo signal to fall below the limit of detection. A variety of denoising methods have been proposed to enhance spectroscopic peaks, but still fall short for the detection of low-intensity signals.
Cancer diagnosis depends on the assessment of patient biopsies to determine tumor type, grading, and stage of malignancy. Pathologists visually review specimens and count mitotic figures (MF) in a variety of cancer types to help gauge aggressiveness, guide treatment, and inform patient prognosis. Current technology for recording MF counts in surgical pathology is lacking in objectivity, and enumeration of MF by microscopy can be error prone. In particular, a lack of systematic means for recording contributes to recognized variability.
Automated Cancer Diagnostic Tool of Detecting, Quantifying and Mapping Mitotically-Active Proliferative Cells in Tumor Tissue Histopathology Whole-Slide Images
Cancer diagnosis is based on the assessment of patient biopsies to determine the tumor type, grade, and stage of malignancy. The proliferative potential of tumors correlates to their growth and metastasis. Visually identifying and quantifying mitotic figures (MF) in cancer biopsy tissue can be used as a surrogate for proliferative activity in tumors.
Scientists at the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) have developed a method implemented as pulse sequences and software to be used with magnetic resonance imaging (MRI) scanners and systems. This technology is available for licensing and commercial development. The method allows for measuring and mapping features of the bulk or average apparent diffusion coefficient (ADC) of water in tissue – aiding in stroke diagnosis and cancer therapy assessment.
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.
Medical imaging is an important resource for early diagnostic, detection, and effective treatment of cancers. However, the screening and review processes for radiologists have been shown to overlook a certain percentage of potentially cancerous image features. Such review errors may result in misdiagnosis and failure to identify tumors. These errors result from human fallibility, fatigue, and from the complexity of visual search required.
Machine Learning and/or Neural Networks to Validate Stem Cells and Their Derivatives for Use in Cell Therapy, Drug Delivery, and Diagnostics
Many biological and clinical procedures require functional validation of a desired cell type. Current techniques to validate rely on various assays and methods, such as staining with dyes, antibodies, and nucleic acid probes, to assess stem cell health, death, proliferation, and functionality. These techniques potentially destroy stem cells and risk contaminating cells and cultures by exposing them to the environment; they are low-throughput and difficult to scale-up.
Researchers at the National Institute on Drug Abuse (NIDA) have developed software that provides personalized feedback for treating drug dependence, alcoholism, smoking cessation, pain management, and associated risky behaviors. The tool is designed for both healthcare providers at the point-of-care and for self-help. Many people who could benefit from treatment do not receive it because of its low availability and high cost.
The National Institute on Drug Abuse (NIDA), Biomedical Informatics section, developed the Mobile Interconnected Evaluation & Learning (MIEL) as a mobile health application. MIEL is used for real-time communication of patient-reported assessment data associated with automatically collected geolocation data on one side and the user’s enterprise-scale patient record system on the other.