Searchable abstracts of presentations at key conferences in endocrinology

ea0037ep614 | Obesity and cardiovascular endocrinology | ECE2015

Effects of omega-3 fatty acid on pre- and post-prandial triglyceride and metabolic parameters with standard meals in patients with hypertriglyceridemia: open, multicentre study

Kim Won Jun , Hong Sangmo , Kang Jun Goo , Lee Chang Beom

Introduction: Nonfasting duration is much longer than fasting time in a day. Although there is a few reports on the importance of postprandial triacylglycerol (TG) on cardiovascular outcome, TG variation after meal is one of main obstacle of clinical trial. The purpose of this study is to determine effects of a 6-week period of omega-3 fatty acid supplementation on fasting and postprandial TG and metabolic parameters in response to standard test meals.Me...

ea0089b6 | Basic Science | NANETS2022

Detecting Cell Surface Expression of Calreticulin in Pancreatic Neuroendocrine Tumors Using a Novel [68Ga]-Radiolabeled Peptide

Guenter Rachael , Ducharme Maxwell , Herring Brendon , Montes Odalyz , McCaw Tyler , Lee Goo , Dhall Deepti , MacVicar Caroline , Chen Herbert , Lapi Suzanne E. , Larimer Benjamin , Bart Rose J.

Background: Current theragnostic techniques for pancreatic neuroendocrine tumors (pNETs) exploit the overexpression of somatostatin receptors (SSTRs) on the cell surface. However, approximately 25% of low-grade and most high-grade pNETs do not express SSTRs, requiring alternative theranostics. Calreticulin (CALR) is a protein linked to reticular calcium homeostasis and immunogenic cell death. Upon sufficient cellular insult, CALR translocates from the endoplasmic reticulum (ER...

ea0098b23 | Basic Science | NANETS2023

Calreticulin is associated with clinical characteristics in pancreatic neuroendocrine tumors

Herring Brendon , Macvicar Caroline , Guenter Rachael , Chen Weisheng , Elhussin Isra , Yates Clayton , Dhall Deepti , Chen Herbert , Lee Goo , Bart Rose J.

Background: Following IHC of CALR, H-scoring was performed by a pathologist. H-scoring was validated by MIF of the same tissue, wherein random-forest machine learning (ML) classifiers were employed to classify cells. ML classifiers were trained to distinguish between pNET cells and tumor stroma using approximately 30% of cells for the respective cell population of interest in each TMA core. Pearson’s correlations were used to evaluate the relationship between H-scoring an...