OCR 5339 is a novel serum-based biomarker assay that determines disease severity and provides a prediction of the progression of scleroderma.
The present invention includes a method for diagnosing cancer and predicting recurrent cancer comprising detecting the presence of survivin in the biological fluid of a patient.
The present invention provides methods for identifying patients at risk of developing preeclampsia. In further embodiments, the present invention relates to methods for the diagnosis of patients with preeclampsia.
The present invention provides methods and compositions related to biomarker profiles for each trimester of pregnancy. The present invention also provides methods for identifying patients at risk of developing a complication of pregnancy, such as preeclampsia. In further embodiments, the present invention relates to methods for the diagnosis of patients with preeclampsia.
Yale researchers, using state of the art mass spectrometry on urine samples, were able to identify 13 biomarkers whose pattern generated by proteomic analysis clearly differentiates patients with preeclampsia from patients without preeclampsia.
8 genetic loci for intracranial aneurysm susceptibility have been identified. These common variants can be utilized in the development of cost-effective and easily applicable genetic screening tests. In particular, the genotype of the patients at a particular locus, such as EDNRA, can be used not only for risk prediction but also for treatment guidance, such as whether a patient is likely to respond to a specific medication.
Yale University investigators have developed a new MR imaging method that accelerates image acquisition beyond conventional and parallel imaging methods. Rather than using linear encoding gradients as employed by current parallel imaging methods, O-space imaging utilizes nonlinear fields as encoding gradients and eliminates phase encoding. Since the spatial encoding gradient shapes are tailored to an existing surface coil array, more efficient use is made of the spatial information in the coil profiles. As an added benefit, nonlinear gradients may be ramped faster than linear gradients, further reducing image acquisition times.
Many Magnetic Resonance Imaging (MRI) techniques are time constrained (e.g. MRI of solids, or functional MRI (fMRI)). As a result, they may try to use a compressed data sampling approach to speed the image acquisition. In compressed sampling, an under-sampled k-space data set is acquired, instead of the full, dense-sampling of k-space points. However, missing k-space points introduce artifacts in the Fourier-transform approach to image reconstruction, which can make the approach unusable. Yale researchers in the Barrett lab have recently developed a method for image reconstruction that uses reasonable constraints on the sparse data, along with a difference map algorithm, in order to 'fill in' the missing k-space data. The approach results in an image that is nearly artifact-free, and that can lead to higher spatial resolution. The method makes Difference Mapping (DM) applicable to MRI images for the first time. It is faster and can handle much larger data sets than commonly used interpolation tools. Most importantly, it makes much higher image quality possible for the most common reconstruction method in use, the Fast Fourier Transform.