Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: (1) a centralized, open-access platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and (2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics.
Analysis at the single cell level has becoming an increasingly important procedure to diagnose cancer tissue biopsies. These tissue samples are often heterogeneous and consist of 1000–15,000 cells. We study the use of centrifugal microfluidics to isolate single cells into micro chambers. We describe the optimization of our microfluidics flow device, characterize its performance using both polystyrene beads as a cell analogue and MCF-7 breast cancer cells, and discuss potential applications for the device. Our results show rapid isolation of ~2000 single cell aliquots in ~20 min. We were able to occupy 65% of available chambers with singly occupied cancer cells, and observed capture efficiencies as high as 80% using input samples ranging from 2000 to 15,000 cells in 20 min. We believe our device is a valuable research tool that addresses the unmet need for massively parallel single cell level analysis of cell populations.
Isolation of circulating tumor cells (CTCs) has generated clinical and academic interest due to the important role that CTCs play in cancer metastasis and diagnosis. Here, we present a PDMS and glass prototype of a microfluidic device for the immunomagnetic, immiscible phase filtration based capture, and isolation of MCF-7 breast cancer cells, from various sample matrices including PBS-based buffer, blood plasma, and unprocessed whole blood. Following optimization of surface energy of an oil-water interface, microfluidic geometry, and bead-binding kinematics, our microfluidic device achieved 95 AE 4% recovery of target cells from PBS-based buffer with 95% purity, 90 AE 3% recovery of target cells from blood plasma and recovery of~70 AE 5% from unprocessed whole blood with purity >99% with 1 ml blood samples with 1,000 spiked target cells. From quantitative studies to assess the nonspecific carryover of contaminants from whole blood, we found that our system accomplishes a >175 fold depletion in platelets, >900 fold depletion in erythrocytes, and >1,700 fold depletion in leukocytes with respect to unprocessed whole blood, enabling us to avoid sample pre-processing. In addition, we found that~95% of the isolated target cells were viable, making them suitable for subsequent molecular and cellular studies. We quantify and propose mechanisms for the carryover of platelet, erythrocyte, and leukocyte contamination in purified samples, rather than relying on sample pre-processing. These results validate the continued study of our platform for extraction of CTCs from patient samples and other rare cell isolation applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.