Hollow fiber integrated microfluidic platforms for <Emphasis Type="Italic">in vitro</Emphasis> Co-culture of multiple cell types

This study demonstrates a rapid prototyping approach for fabricating and integrating porous hollow fibers (HFs) into microfluidic device. Integration of HF can enhance mass transfer and recapitulate tubular shapes for tissue-engineered environments. We demonstrate the integration of single or multiple HFs, which can give the users the flexibility to control the total surface area for tissue development. We also present three microfluidic designs to enable different co-culture conditions such as the ability to co-culture multiple cell types simultaneously on a flat and tubular surface, or inside the lumen of multiple HFs. Additionally, we introduce a pressurized cell seeding process that can allow the cells to uniformly adhere on the inner surface of HFs without losing their viabilities. Co-cultures of lung epithelial cells and microvascular endothelial cells were demonstrated on the different platforms for at least five days. Overall, these platforms provide new opportunities for co-culturing of multiple cell types in a single device to reconstruct native tissue micro-environment for biomedical and tissue engineering research.     

Treatment of lentigo maligna with combination laser therapy: recurrence at 8 months after initial resolution

This is a case report of an 89‐year‐old woman with a lentigo maligna treated with alexandrite and ruby lasers. The lentigo maligna never completely resolved despite multiple treatments. Marked cosmetic improvement, however, was obtained. No adverse sequelae have occurred.     

Outcomes of Donation After Circulatory Death Heart Transplantation in Australia

Background

Transplantation of hearts retrieved from donation after circulatory death (DCD) donors is an evolving clinical practice.

Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses

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Effect of Simulation-Based Training and Neurofeedback Interventions on Radiation Technologists’ Workload,Situation Awareness,and Performance

PurposeOur purpose was to assess the effect of a combined intervention – simulation-based training supported by neurofeedback sessions – on radiation technologists’ (RTs’) workload, situation awareness, and performance during routine quality assurance and treatment delivery tasks.Methods and MaterialsAs part of a prospective institutional review board approved study, 32 RTs previously randomized to receive versus not receive simulation-based training focused on patient safety were again randomized to receive versus not receive a 3-week neurofeedback intervention (8 sessions of alpha-theta protocol) focused on stress reduction as well as conscious precision, strong focus, and ability to solve arising problems. Perceived workload was quantified using the NASA Task Load Index. Situation awareness was quantified using the situation awareness rating technique. Performance score was calculated using procedural compliance with time-out components and error detection.ResultsRTs randomized to simulation-based training followed by neurofeedback sessions demonstrated no significant changes in perceived workload or situation awareness scores, but did have better performance compared with other study groups (P < .01).ConclusionsThis finding is encouraging and provides basis for using neurofeedback as means to possibly augment performance improvements gained during simulation-based training.     

Functional evaluation of out-of-the-box text-mining tools for data-mining tasks

Objective The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug–drug interactions, and learning used-to-treat relationships between drugs and indications.Materials We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks.Results There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets.Conclusions For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.