Rajagopal, Anjali

Enteropathy-associated T cell lymphoma (EATL) is an aggressive subtype of non-Hodgkin’s lymphoma often associated with coeliac disease (CD). We describe a previously healthy man in his 50 s who presented with a history of abdominal pain, failure to thrive and significant weight loss over a 3-month period. Investigations revealed a positive coeliac serology, diffuse duodenal atrophy with multiple duodenal and jejunal ulcers on endoscopy and mesenteric lymphadenopathy on CT imaging. Duodenal tissue biopsy confirmed a diagnosis of EATL Stage IVB. Chemotherapy with cyclophosphamide, doxorubicin, vincristine and prednisone regimen was initiated. This case highlights the need for greater awareness and consideration of EATL in individuals with worsening malabsorption and abdominal pain, irrespective of coeliac history.

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.