Diagnostics

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Vanderbilt researchers have identified five key metabolites that, in combination with routine clinical tests, can serve as viable biomarkers for early-stage CKD. This technology offers a robust, minimally invasive, and accurate diagnostic tool that can be deployed in a variety of healthcare settings to improve care and slow or prevent progression to end-stage renal disease.

This genetic test identifies cattle at high risk of developing pulmonary hypertension at high altitudes (often called "brisket disease").  Brisket disease afflicts about 5% of cattle at high altitudes and the current predictive test for at-risk cattle is a measure of pulmonary arterial pressure (PAP).  This current PAP test has some major drawbacks.  First, it is an invasive test.  Secondly, it is not accurate at lower elevations -- so at-risk cattle cannot identified before incurring the cost of transport to high altitude.  There is no treatment for the disease except prompt removal of the animal to lower elevations.  This technology measures genetic variants that confer susceptibility to brisket disease, and could be developed into a diagnostic or a prognostic test for use prior to shipping cattle to higher elevations or in breeding operations.

The standard for kidney stone detection is through the use of computed tomography (CT). However, CT is expensive and delivers harmful ionizing radiation into the body. Ultrasound would be the ideal way to detect kidney stones except that it performs poorly in detecting and accurately sizing stones. Vanderbilt researchers inventors have developed a technique that is able to separate hard, mineralized material (i.e kidney stones) from soft tissue in a way that is both cheaper and safer than CT and performs better than conventional ultrasound imaging.

A research team at Vanderbilt University Mass Spectrometry Research Center has developed the Molecular Image Fusion software system, that by fusing spatial correspondence between histology and imaging mass spectrometry (IMS) measurements and cross-modality modeling, can predict ion distributions in tissue at spatial resolutions that exceed their acquisition resolution. The prediction resolution can even exceed the highest spatial resolution at which IMS can be physically measured. This software has been successfully tested on different IMS datasets and can be extended to other imaging modalities like MRI, PET, CT, profilometry, ion mobility spectroscopy, and different forms of microscopy.