Background and Aims: Given the increased risk of post-transplant metabolic syndrome (PTMS; defined by hypertension, diabetes mellitus and hyperlipidemia), we aimed to identify the potential role of food addiction in the development of metabolic complications in the post-liver transplant population.Methods: Inclusion criteria included adult liver transplant recipients followed at our institution between June 2016 and November 2016. Participants were administered a demographic survey as well as the Yale Food Assessment Scale 2.0, a 35-item questionnaire used to assess frequency of food addiction in accordance with the DSM-V guidelines of substance use disorders. Demographic and clinical data were collected.Results: Our study included 236 liver transplant recipients (139 males, 97 females). The median (interquartile range [IQR]) BMI of participants was 26.8 kg/m2 (24.2, 30.4), and median (IQR) time since transplantation was 50.9 months (19.6, 119.8). The prevalence rates of hypertension, hypercholesterolemia and diabetes mellitus were 54.7%, 25.0% and 27.1%, respectively. Twelve participants (5.1%) were found to have a diagnosis of food addiction. A diagnosis of food misuse was made in 94 (39.8%) of the transplant recipients.Conclusions: Our findings are consistent with prior data that indicate high prevalence of metabolic complications among liver transplant recipients. Food addiction was not predictive of metabolic complications within this population. Nevertheless, we found that this population was at high risk of demonstrating symptoms of food misuse, and they were not likely to appreciate the risks of pathologic patterns of eating. Given the increasing risk of cardiovascular morbidity and mortality in this population, efforts should be made to identify risk factors for the development of PTMS.

CRISPR/Cas9-mediated genome editing in mammalian cells can generate undesired chromosomal alterations, including deleterious on-target large deletions and chromosomal translocations. Currently few approaches effectively prevent these on-target DNA damage. Here we show that the association of engineered phage DNA polymerases with Cas9 protein substantially inhibits the production of undesired on-target chromosomal alterations. Our "CasPlus" editing platform significantly increases the proportion of precise 1- to 3-base-pair insertions at target sites. CasPlus efficiently corrected common mutations in the DMD (del exon 52) and CFTR (F508del) genes by generating precise insertions without an exogenous template with fewer deleterious edits than Cas9 alone. CasPlus offers safer and more efficient targeting strategies optimized for human applications.

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Glioblastoma multiforme (GBM), the most common form of glioma, is a malignant tumor with a high risk of mortality. By providing accurate survival estimates, prognostic models have been identified as promising tools in clinical decision support. In this study, we produced and validated two machine learning-based models to predict survival time for GBM patients. Publicly available clinical and genomic data from The Cancer Genome Atlas (TCGA) and Broad Institute GDAC Firehouse were obtained through cBioPortal. Random forest and multivariate regression models were created to predict survival. Predictive accuracy was assessed and compared through mean absolute error (MAE) and root mean square error (RMSE) calculations. 619 GBM patients were included in the dataset. There were 381 (62.9%) cases of recurrence/progression and 53 (8.7%) cases of disease-free survival. The MAE and RMSE values were 0.553 and 0.887 years respectively for the random forest regression model, and they were 1.756 and 2.451 years respectively for the multivariate regression model. Both models accurately predicted overall survival. Comparison of models through MAE, RMSE, and visual analysis produced higher accuracy values for random forest than multivariate linear regression. Further investigation on feature selection and model optimization may improve predictive power. These findings suggest that using machine learning in GBM prognostic modeling will improve clinical decision support. *Co-first authors.