Saint Francis University

About: Saint Francis University is a education organization based out in Loretto, Pennsylvania, United States. It is known for research contribution in the topics: Population & Osteoblast. The organization has 1694 authors who have published 2038 publications receiving 87149 citations.

Characteristics of blunt and personal violent injuries.

Abstract: This study was designed to analyze the characteristics and behavior patterns of individuals involved in nonfatal, traumatic injuries. There were 547 patients included in the study with 363 sustaining blunt trauma injuries, 144 sustaining personal violence injuries, and 40 being burn victims. Motor vehicle accident victims tended to be young, single, white, employed males: substance use was detected in 32%, and 57% were unbelted. Motorcycle accident victims tended to be young, single, unemployed males: substance use was detected in 25% and 90% were not wearing helmets. Pedestrians struck tended to be single, unemployed males. Penetrating injuries involved mostly young, single, black males: substance use was detected in 35% of patients and most incidents occurred from 4:00 P.M. to 8:00 A.M. Assault victims were mostly young, single, black males with substance use detected in 48%. Suicide patients tended to be white males. The incidence of repeat victims was one out of ten for blunt trauma, and one out of five for personal violence injuries and burns.

Mood-stabilizing effects of rapamycin and its analog temsirolimus: relevance to autophagy.

Abstract: Accumulated data support a relationship between mood disorders and cellular plasticity and resilience, some suggesting relevance to autophagy. Our previous data show that pharmacological enhancement of autophagy results in antidepressant-like effects in mice. The current study was designed to further examine the effects of autophagy enhancement on mood by testing the effects of subchronic treatment with the mammalian target of rapamycin (mTOR) inhibitors and autophagy enhancers rapamycin and temsirolimus in a model for mania and in a model for antidepressant action, respectively. The results show that rapamycin reduced mania-like aggression and reward-seeking behaviors, with no effects on locomotion. Temsirolimus reduced depression-related immobility in the forced-swim test without effects on locomotion in the open field or on anxiety-related measures in the elevated plus maze. Taken together with our previous findings, these data support the notion that enhancing autophagy may have mood-stabilizing effects.

Water-fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network.

Abstract: BACKGROUND Water-fat separation is a postprocessing technique most commonly applied to multiple-gradient-echo magnetic resonance (MR) images to identify fat, provide images with fat suppression, and to measure fat tissue concentration. Recently, Numerous advancements have been reported. In contrast to early methods, the process of water-fat separation has become complicated due to multiparametric analytic models, optimization methods, and the absence of a unified framework for diverse source data. PURPOSE To determine the feasibility and performance of MRI water-fat separation and parametric mapping via deep learning (DL) with a range of inputs. STUDY TYPE Retrospective data usage. POPULATION/SUBJECTS Ninety cardiac MR examinations from normal control, acute, subacute, and chronic myocardial infarction subjects were obtained, providing 1200 multiple gradient-echo acquisitions. FIELD STRENGTH/SEQUENCE 1.5 T/2D multiple gradient-echo pulse sequence ASSESSMENT: Ground-truth training and validation water-fat separation were obtained using a graph cut method with R2 *, off-resonance correction, and a multipeak fat spectrum. U-Net DL training with single and multiecho, complex, and magnitude inputs were compared using quantitative and three-observer subjective analysis. STATISTICAL TESTS DL methods' image structural similarity, and quantitative proton density fat fraction (PDFF), R2 *, and off-resonance quantitative values were statistically compared with the GraphCut reference standard using Student's t-test and Pearson's correlation. RESULTS Myocardial fat deposition in chronic myocardial infarction and intramyocardial hemorrhage in acute myocardial infarction were well visualized in the DL results. Predicted values for R2 *, off-resonance, water, and fat signal intensities were well correlated with a conventional model-based water fat separation (R2 ≥ 0.97, P < 0.001) with appropriate inputs. DL parameter maps had a 14% higher signal-to-noise ratio (P < 0.001) when compared with a conventional method. DATA CONCLUSION DL water-fat separation is feasible with a wide range of inputs, while R2 * and off-resonance mapping requires multiple echoes and complex images. With appropriate inputs, DL provides quantitative and subjective results comparable to conventional model-based methods. LEVEL OF EVIDENCE 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;50:655-665.