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.

National Cancer Institute dosimetry system for Computed Tomography (NCICT) Computer Program

Description of Technology:

About half of the per capita dose of radiation due to medical exposures is provided by computed tomography (CT) examinations. Approximately 80 million CTs are performed annually in the United States. CT scans most commonly look for internal bleeding or clots, abscesses due to infection, tumors and internal structures. Although CT provides great patient benefit, concerns exist about potential associated risks from radiation doses – especially in pediatric patients more sensitive to radiation.

Automated Cancer Diagnostic Tool of Detecting, Quantifying and Mapping Mitotically-Active Proliferative Cells in Tumor Tissue Histopathology Whole-Slide Images

Summary:

The National Cancer Institute (NCI) Laboratories of Pathology and Cancer Biology and Genetics are seeking parties interested in licensing and/or partnering in co-development research of a software tool for commercialization in the field of clinical immunohistochemistry quantification.

Methods of Predicting Patient Treatment Response and Resistance via Single-Cell Transcriptomics of Their Tumors

Summary:

The NCI is currently seeking research co-development partners for this first-in-kind computational method that is predictive of therapeutic response based on clonal SC gene expression of tumors.

Description of Technology:

Tailoring the best treatments to cancer patients remains a highly important endeavor in the oncology field. However, personalized treatment courses are challenging to determine, and technologies or methods that can successfully be employed for precision oncology are lacking.

Generation of Artificial Mutation Controls for Diagnostic Testing

This technology relates to a method of generating artificial compositions that can be used as positive controls in a genetic testing assay, such as a diagnostic assay for a particular genetic disease. Such controls can be used to confirm the presence or absence of a particular genetic mutation. The lack of easily accessible, validated mutant controls has proven to be a major obstacle to the advancement of clinical molecular genetic testing, validation, quality control (QC), quality assurance (QA), and required proficiency testing.

Auscultatory Training System and Telemedicine Tool with Accurate Reproduction of Physiological Sounds

This CDC developed auscultatory training apparatus includes a database of prerecorded physiological sounds (e.g., lung, bowel, or heart sounds) stored on a computer for playback. Current teaching tools, which utilize previously recorded sounds, suffer from the disadvantage that playback environments cause considerable distortion and errors in sound reproduction. For example, to those trainees using such systems, the reproduced respiratory sounds do not “sound” as if they are being generated by a live patient.
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