Background Clostridium difficile infection (CDI) is a growing problem in the community and hospital setting. Its incidence has been on the rise over the past two decades, and it is quickly becoming a major concern for the health care system. High rate of recurrence is one of the major hurdles in the successful treatment of C. difficile infection. There have been few studies that have looked at patterns of recurrence. The studies currently available have shown a number of risk factors associated with C. difficile recurrence (CDR); however, there is little consensus on the impact of most of the identified risk factors.MethodsOur study was a retrospective chart review of 198 patients diagnosed with CDI via Polymerase Chain Reaction (PCR) from January 2009 to Jun 2013. In our study, we decided to use a machine learning algorithm called the Random Forest (RF) to analyze all of the factors proposed to be associated with CDR. This model is capable of making predictions based on a large number of variables, and has outperformed numerous other models and statistical methods.ResultsWe came up with a model that was able to accurately predict the CDR with a sensitivity of 83.3%, specificity of 63.1%, and area under curve of 82.6%. Like other similar studies that have used the RF model, we also had very impressive results.ConclusionsWe hope that in the future, machine learning algorithms, such as the RF, will see a wider application.

Aims Valve interstitial cells are active and aggressive players in aortic valve calcification, but their dynamic mediation of mechanically-induced calcific remodeling is not well understood. The goal of this study was to elucidate the feedback loop between valve interstitial cell and calcification mechanics using a novel three-dimensional culture system that allows investigation of the active interplay between cells, disease, and the mechanical valve environment. Methods & Results We designed and characterized a novel bioreactor system for quantifying aortic valve interstitial cell contractility in 3-D hydrogels in control and osteogenic conditions over 14 days. Interstitial cells demonstrated a marked ability to exert contractile force on their environment and to align collagen fibers with the direction of tension. Osteogenic environment disrupted interstitial cell contractility and led to disorganization of the collagen matrix, concurrent with increased αSMA, TGF-β, Runx2 and calcific nodule formation. Interestingly, RhoA was also increased in osteogenic condition, pointing to an aberrant hyperactivation of valve interstitial cells mechanical activity in disease. This was confirmed by inhibition of RhoA experiments. Inhibition of RhoA concurrent with osteogenic treatment reduced pro-osteogenic signaling and calcific nodule formation. Time-course correlation analysis indicated a significant correlation between interstitial cell remodeling of collagen fibers and calcification events. Conclusions Interstitial cell contractility mediates internal stress state and organization of the aortic valve extracellular matrix. Osteogenesis disrupts interstitial cell mechanical phenotype and drives disorganization, nodule formation, and pro-calcific signaling via a RhoA-dependent mechanism.

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Diabetic retinopathy is one of the leading causes of blindness worldwide. Optical coherence tomography angiography (OCTA) is a non-invasive technology that provides depth-resolved images of the chorioretinal vasculature and allows for the understanding of the changes in vasculature with diabetic retinopathy. Not only can it provide qualitative information, but OCTA can also provide quantitative information about the vasculature in patients with diabetic retinopathy. Macular vessel density is one of the quantitative metrics that can be obtained from OCTA images. This is a repeatable and non-subjective measurement that can provide valuable insight into the pathophysiology of diabetic retinopathy. In this non-systematic review, the measurement of macular vessel density in diabetic retinopathy and the reasons for its importance in the diagnosis and management of patients with diabetes and varying severities of diabetic retinopathy is discussed.