Nanocrystals with advanced magnetic or optical properties have been actively pursued for potential biological applications, including integrated imaging, diagnosis and therapy. Among various magnetic nanocrystals, FeCo has superior magnetic properties, but it has yet to be explored owing to the problems of easy oxidation and potential toxicity. Previously, FeCo nanocrystals with multilayered graphitic carbon, pyrolytic carbon or inert metals have been obtained, but not in the single-shelled, discrete, chemically functionalized and water-soluble forms desired for biological applications. Here, we present a scalable chemical vapour deposition method to synthesize FeCo/single-graphitic-shell nanocrystals that are soluble and stable in water solutions. We explore the multiple functionalities of these core-shell materials by characterizing the magnetic properties of the FeCo core and near-infrared optical absorbance of the single-layered graphitic shell. The nanocrystals exhibit ultra-high saturation magnetization, r1 and r2 relaxivities and high optical absorbance in the near-infrared region. Mesenchymal stem cells are able to internalize these nanoparticles, showing high negative-contrast enhancement in magnetic-resonance imaging (MRI). Preliminary in vivo experiments achieve long-lasting positive-contrast enhancement for vascular MRI in rabbits. These results point to the potential of using these nanocrystals for integrated diagnosis and therapeutic (photothermal-ablation) applications.
Macrophages play important roles in the immunological defense system, but at the same time they are involved in inflammatory diseases such as atherosclerosis. Therefore, imaging macrophages is critical to assessing the status of these diseases. Toward this goal, a recombinant human H chain ferritin (rHFn)-iron oxide nano composite has been investigated as an MRI contrast agent for labeling macrophages. Iron oxide nanoparticles in the form of magnetite (or maghemite) with narrow size distribution were synthesized in the interior cavity of rHFn. The composite material exhibited the R 2 relaxivity comparable to known iron oxide MRI contrast agents. Furthermore, the mineralized protein cages are readily taken up by macrophages in vitro and provide significant T2* signal loss of the labeled cells. These results encourage further investigation into the development of the rHFn-iron oxide contrast agent to assess inflammatory disease status such as macrophage-rich atherosclerotic plaques in vivo. Magn Reson Med 60:1073-1081, 2008.
We present an interpretable machine learning algorithm called ‘eARDS’ for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88–0.90), sensitivity of 0.77 (95% CI = 0.75–0.78), specificity 0.85 (95% CI = 085–0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81–0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18–40) (AUROC = 0.93 [0.92–0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81–0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.
Acute-on-chronic liver failure and shock on intensive care unit admission were risk factors for ARDS development, whereas etiology of cirrhosis and alcohol were not. Mortality from ARDS was markedly increased in patients with cirrhosis. Early recognition and treatment for infection might be important for improving the high mortality in this group of patients.
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