Abstract: Provided herein are methods, systems and kits for use in identifying a subject with an increased risk of developing severe forms of inflammatory bowel disease (IBD), based at least in part, on an expression of one or more biomarkers detected in a biological sample obtained from the subject. Also provided are methods, systems and kits for treating, or optimizing the treatment for, the IBD based, at least in part, on the expression the one or more biomarkers. In some embodiments, the one or more biomarkers is angiotensin-converting enzyme 2 (ACE2), the host receptor for severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2).

Abstract: The present disclosure describes methods, devices and systems of diagnosing, prognosing, and treating subjects with moderate to severe forms of Crohn's disease (CD) that is characterized by stricturing and internal penetrating disease phenotypes. Also described are methods and kits for characterizing a subtype of CD, and identifying a subject as being suitable for a therapy to treat the CD.

Abstract: Described herein is a method and system for facilitating segmentation of images. A difference image is received and processed to extract at least one histogram (402). A noise component is determined by fitting a symmetric Gaussian distribution to the extracted histogram such that the negative portion of the Gaussian distribution coincides with the negative portion of the histogram (403). The noise component is then subtracted from the histogram to generate a probability distribution function (404), which may be converted to a cumulative distribution function (406) and applied to the difference image to generate a probabilistic representation of contrast enhancement (408).

Abstract: Described herein is a method and system for facilitating segmentation of images. A difference image is received and processed to extract at least one histogram (402). A noise component is determined by fitting a symmetric Gaussian distribution to the extracted histogram such that the negative portion of the Gaussian distribution coincides with the negative portion of the histogram (403). The noise component is then subtracted from the histogram to generate a probability distribution function (404), which may be converted to a cumulative distribution function (406) and applied to the difference image to generate a probabilistic representation of contrast enhancement (408).