Focal Spot, Winter 2006/2007

https://digitalcommons.wustl.edu/focal_spot_archives/1104/thumbnail.jp

Pulsatile flow does not improve efficacy in ex vivo lung perfusion.

Introduction Ex vivo lung perfusion (EVLP) has the potential to increase the donor pool for lung transplantation by facilitating extended evaluation of marginal organs. Current methodology employs continuous flow pumps for perfusion. In vivo, continuous flow has been shown to increase pulmonary vascular resistance (PVR). Thus, pulsatile flow EVLP may reduce PVR and improve organ preservation by providing physiologic flow morphology. Methods Lung blocks harvested from male, Yorkshire pigs were allocated into continuous (CF, n=3) and pulsatile flow (PF, n=4) groups. Lungs were ventilated at 4-5 mL/kg, 30% FiO2 and perfused with an acellular, albumin-based solution corrected for osmolarity, acid/base balance, and CO2 concentration (=19 hours at 30°C). Prostaglandin E2 and 30% albumin were infused continuously at 250 ?g/hr and 100 mL/hr, respectively. Hemodynamic, respiratory, and blood gas parameters were recorded hourly. Parenchymal biopsies were used for quantification of wet: dry ratio and IL-6, IL-8, and TNF-a using ELISA. Results ?PO2/FoO2 in mmHg was 261±47 and 313±37 at baseline and 174±36 and 152±36 at hour 12 for CF and PF, respectively. Wet: dry ratio was 5.53±0.56 and 6.06±0.09 at baseline and 5.27±0.48 and 5.12±0.40 at hour 12 for CF and PF, respectively. Average PVR in Woods Units was 15.17±1.33 and 13.60±1.91 over the 12 hour test period for CF and PF groups, respectively. Peak airway pressure (PAWP) in cm H2O was 17±1.15 and 16±0.75 at baseline and 21±1.67 and 21±0.41 at hour 12 for CF and PF, respectively. There were no discernable differences in TNF-a, IL-6, and IL-8 concentrations, PVR, ?PO2/FiO2, wet: dry ratio, and PAWP between CF and PF. Conclusion EVLP system successfully maintained lungs up to 19 hours using a modified perfusate. These data suggest PF does not offer benefits over CF for prolonged ex vivo lung preservation

A new telemedicine system for Chronic Obstructive Pulmonary Disease: validation of the sensing wearable framekork

The work of this thesis is focused on the validation of the wearable framework of the Chronious platform. Chronious is a FP7 European Community project which works on an innovative telemedicine system for monitoring COPD patients. The wearable system of the Chronious project is composed of a shirt made of washable stretch-material into which are sewn several sensors and of a microcontroller-based acquisition system devoted to data collection. Three phases of test are performed to assess accuracy and reliability of the whole wearable platform: two sessions of test on healthy subjects and a final session on COPD patients. We developed and tested two method for the calibration of the respiratory bands (RIP) inserted in the shirt of the wearable platfor

Proceedings Virtual Imaging Trials in Medicine 2024

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday

Distributed Computing in a Pandemic: A Review of Technologies Available for Tackling COVID-19

The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks.Comment: 21 pages (15 excl. refs), 2 figures, 3 table

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974

USSR Space Life Sciences Digest, issue 21

This is the twenty-first issue of NASA's USSR Space Life Sciences Digest. It contains abstracts of 37 papers published in Russian language periodicals or books or presented at conferences and of a Soviet monograph on animal ontogeny in weightlessness. Selected abstracts are illustrated with figures and tables from the original. A book review of a work on adaptation to stress is also included. The abstracts in this issue have been identified as relevant to 25 areas of space biology and medicine. These areas are: adaptation, biological rhythms, body fluids, botany, cardiovascular and respiratory systems, cytology, developmental biology, endocrinology, enzymology, equipment and instrumentation, exobiology, gravitational biology, habitability and environmental effects, hematology, human performance, life support systems, mathematical modeling, metabolism, microbiology, musculoskeletal system, neurophysiology, operational medicine, perception, psychology, and reproductive system

Robotic right ventricle is a biohybrid platform that simulates right ventricular function in (patho)physiological conditions and intervention

The increasing recognition of the right ventricle (RV) necessitates the development of RV-focused interventions, devices and testbeds. In this study, we developed a soft robotic model of the right heart that accurately mimics RV biomechanics and hemodynamics, including free wall, septal and valve motion. This model uses a biohybrid approach, combining a chemically treated endocardial scaffold with a soft robotic synthetic myocardium. When connected to a circulatory flow loop, the robotic right ventricle (RRV) replicates real-time hemodynamic changes in healthy and pathological conditions, including volume overload, RV systolic failure and pressure overload. The RRV also mimics clinical markers of RV dysfunction and is validated using an in vivo porcine model. Additionally, the RRV recreates chordae tension, simulating papillary muscle motion, and shows the potential for tricuspid valve repair and replacement in vitro. This work aims to provide a platform for developing tools for research and treatment for RV pathophysiology.</p

Distributed Computing in a Pandemic

The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks