Molecular Analysis of Pancreatic Cyst Fluid for the Management of Intraductal Papillary Mucinous Neoplasms.

Pancreatic cancer is one of the most lethal human cancers. Early detection and diagnosis of precursor lesions for pancreatic malignancy is essential to improve the morbidity and mortality associated with this diagnosis. Of the cystic precursor lesions, branch duct intraductal papillary mucinous neoplasm (IPMN) is the most frequently identified lesion and has a wide range of malignant potential. Currently, Carcinogenic embryonic antigen (CEA) levels in the cyst fluid and cytology are the two most often utilized tools to diagnose these lesions; however, their diagnostic and risk stratification capabilities are somewhat limited. Within the last decade, the use of endoscopic ultrasound-guided fine-needle aspiration has opened the door for molecular analysis of cystic fluid as an option to enhance both the diagnosis and risk stratification of these lesions. The first step is to differentiate branch duct IPMNs from other lesions. KRAS and GNAS alterations have been shown to be accurate markers for this purpose. Following cyst type identification, mutational analysis, telomere fusion, microRNAs, long non-coding RNA, and DNA methylation have been identified as potential targets for stratifying malignant potential using the cystic fluid. In this review, we will examine the various targets of cyst fluid molecular analysis and their utility in the diagnosis and risk stratification of branch duct IPMNs.

Predictors of change in hypoglycemia unawareness among patients with type 1 diabetes.

AIMS: To analyse predictors of hypoglycemia unawareness (HU) and improvement in Clarke score in clinical practice. MATERIALS AND METHODS: We retrospectively identified patients with type 1 diabetes (T1D) at an academic T1D clinic who completed HU surveys 6-12 months apart. HU (Clarke score ≥4) and improvement in Clarke score (decrease by ≥1 point or more clinically relevant ≥2 point) were assessed in univariable relationships and using multivariable logistic regression. RESULTS: Of the 300 participants, median diabetes duration was 19 years, 47 had HU at baseline, and 91 had an improvement by 1 point while 21 had an improvement by 2 points. Patients with baseline Clarke score ≥4 who had ≥1 or ≥2 point improvement had lower filtration rate (eGFR) than those who did not. After adjustment for other variables, gender (male OR 0.33, 95% CI 0.15, 0.74), log diabetes duration (OR 6.40, 95% CI 2.84, 14.5), and eGFR <60 ml/min/1.73 m2 (5.56, 95% CI 1.98, 15.6) were independent predictors of baseline HU. Continuous glucose monitoring use (OR 2.04, 95% CI 1.20, 3.48) and log diabetes duration (OR 1.78, 95% CI 1.22, 2.60) were independent predictors of 1 point improvement and eGFR <60 ml/min/1.73 m2 (OR 10.5, 95% CI 3.64, 30.0) and an education visit (OR 2.64, 95% CI 1.01, 6.89) were independent predictors of 2 point improvement in Clarke score. CONCLUSIONS: Diabetes duration, gender, and eGFR were independent predictors of HU. Improvement in Clarke score is possible in patients with long-standing T1D, underscoring the need for additional study.

Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions.

The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34-68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25-64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.