Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Development of a Simulation-Based Mastery Learning Curriculum for Breaking Bad News

Introduction

Physician communication impacts patient outcomes. However, communication skills, especially around difficult conversations, remain suboptimal, and there is no clear way to determine the validity of entrustment decisions. The aims of this study were to 1) describe the development of a simulation-based mastery learning (SBML) curriculum for breaking bad news (BBN) conversation skills and 2) set a defensible minimum passing standard (MPS) to ensure uniform skill acquisition among learners.

Carboxylesterase catalyzed 18O-labeling of carboxylic acid and its potential application in LC-MS/MS based quantification of drug metabolites

LC-MS quantification of drug metabolites is sometimes impeded by the availability of internal standards that often requires customized synthesis and/or extensive purification. Although isotopically labeled internal standards are considered ideal for LC-MS/MS based quantification, de novo synthesis using costly isotope-enriched starting materials makes it impractical for early stage of drug discovery. Therefore, quick access to these isotope-enriched compounds without chemical derivatization and purification will greatly facilitate LC-MS/MS based quantification. Herein, we report a novel ¹⁸O-labeling technique using metabolizing enzyme carboxylesterase (CES) and its potential application in metabolites quantification study. Substrates of CES typically undergo a two-step oxygen exchange with H₂¹⁸O in the presence of the enzyme, generating singly- and doubly-¹⁸O-labeled carboxylic acids; however, unexpected hydrolytic behavior was observed for three of the test compounds – indomethacin, piperacillin and clopidogrel. These unusual observations led to the discovery of several novel hydrolytic mechanisms. Finally, when used as internal standard for LC-MS/MS based quantification, these in situ labeled compounds generated accurate quantitation comparable to the conventional standard curve method. The preliminary results suggest that this method has potential to eliminate laborious chemical synthesis of isotope-labeled internal standards for carboxylic acid-containing compounds, and can be developed to facilitate quantitative analysis in early-stage drug discovery.     

Long-term health and socioeconomic outcome of obstructive sleep apnea in children and adolescents

BackgroundThere is limited information about the long-term outcome of obstructive sleep apnea (OSA) diagnosed in children and adolescents for educational and social factors. Here, we estimate the long-term socioeconomic outcome and health care costs of OSA.MethodsThe historical case-control cohort study included Danish individuals with OSA diagnosed in childhood or adolescence between 1994 and 2015. Health care costs and socioeconomic data were obtained from nationwide administrative and health registers. A total of 5419 were diagnosed during this period; of these we traced 1004 patients who we compared with 4085 controls (mean index age, 10.2 years; Standard Deviation (SD), 5.6 years) until the age of 20 years. Controls were matched for age, gender, and residency.ResultsComparing the OSA patient and control groups at age 20 years we found: 1) lower parental educational level; 2) significantly lower educational level also after adjustment for parental educational level; 3) lower school grade-point averages; 4) lower employment rate and lower income, which was not fully compensated when transfer payments were considered; and 5) patients' initial health care costs were higher due to higher morbidity. Patients showed higher mortality rates than controls (Hazard Ratio (HR) = 7.63, 95% CI = 4.87–11.95, P < 0.001).ConclusionsOSA in children and adolescent is associated with a significant influence on morbidity, mortality, educational level, grading, social outcome, and welfare consequences.     

Inactivated influenza vaccine formulated with single-stranded RNA-based adjuvant confers mucosal immunity and cross-protection against influenza virus infection

Influenza vaccination is considered the most valuable means to prevent and control seasonal influenza infections, which causes various clinical symptoms, ranging from mild cough and fever to even death. Among various influenza vaccine types, the inactivated subunit type is known to provide improved safety with reduced reactogenicity. However, there are some drawbacks associated with inactivated subunit type vaccines, with the main ones being its low immunogenicity and the induction of Th2-biased immune responses. In this study, we investigated the role of a single-stranded RNA (ssRNA) derived from the intergenic region in the internal ribosome entry site of the Cricket paralysis virus as an adjuvant rather than the universal vaccine for a seasonal inactivated subunit influenza vaccine. The ssRNA adjuvant stimulated not only well-balanced cellular (indicated by IgG2a, IFN-γ, IL-2, and TNF-α) and humoral (indicated by IgG1 and IL-4) immune responses but also a mucosal immune response (indicated by IgA), a key protector against respiratory virus infections. It also increases the HI titer, the surrogate marker of influenza vaccine efficacy. Furthermore, ssRNA adjuvant confers cross-protective immune responses against heterologous influenza virus infection while promoting enhanced viral clearance. Moreover, ssRNA adjuvant increases the number of memory CD4⁺ and CD8⁺ T cells, which can be expected to induce long-term immune responses. Therefore, this ssRNA-adjuvanted seasonal inactivated subunit influenza vaccine might be the best influenza vaccine generating robust humoral and cellular immune responses and conferring cross-protective and long-term immunity.