PubMed · 11649364
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Barton J Bernstein. The artificial heart program.. https://pubmed.ncbi.nlm.nih.gov/11649364/
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A novel multi-coaxial bioreactor for three-dimensional cultures of adherent cell types, such as liver, is described. It is composed of four tubes of increasing diameter placed one inside the other, creating four spatially isolated compartments. Liver acinar structure and physiological parameters are mimicked by sandwiching cells in the space between the two innermost semi-permeable tubes, or hollows fibers, and creating a radial flow of media from an outer compartment (ECC), through the cell mass compartment, and to an inner compartment (ICC). The outermost compartment is created by gas-permeable tubing, and the housing is used to oxygenate the perfusion media to periportal levels in the ECC. Experiments were performed using distilled water to correlate the radial flow rate (Q(r)) with (1) the pressure drop (DeltaP) between the media compartments that sandwich the cell compartment and (2) the pressure in the cell compartment (P(c)). These results were compared with the theoretical profile calculated based on the hydraulic permeability of the two innermost fibers. Phase-contrast velocity-encoded magnetic resonance imaging was used to visualize directly the axial velocities inside the bioreactor and confirm the assumptions of laminar flow and zero axial velocity at the boundaries of each compartment in the bioreactor. Axial flow rates were calculated from the magnetic resonance imaging results and were similar to the measured axial flow rates for the previously described experiments.
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This paper describes the use of a microsphere sensor technology that allows simple fabrication of vapor sensor arrays with reproducible response patterns. Microsphere sensor fabrication protocols are uncomplicated and yield billions of highly reproducible sensors. Microsphere sensor arrays combined with a generalized Whitney-Mann-Wilcoxen (GWMW) classifier were used to discriminate between the presence and absence of nitroaromatic compounds in high background vapor mixtures. The classifier was trained on one sensor array and then used to obtain 98.2 and 93.7% correct classification rates with data collected using two subsequent arrays made up to six months after the initial training was performed. These results represent an advance in the ability to transfer training data between multiple sensor arrays with a fluorescence-based artificial nose.