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Biomedical subjects

P E Danielsson

Publications and source records attributed to P E Danielsson.

3 recordsLinked to original sources

A comparison of primary cultures of rat cerebral microvascular endothelial cells to rat aortic endothelial cells.

A method to culture rat cerebral microvascular endothelial cells (RCMECs) was developed and adapted to concurrently obtain cultures of rat aortic endothelial cells (RAECs) without subculturing, cloning, or "weeding." The attachment and growth requirements of endothelial cell clusters from isolated brain microvessels were first evaluated. RCMECs required fetal bovine serum to attach efficiently. Attachment and growth also depended on the matrix provided (fibronectin approximately laminin much greater than gelatin greater than poly-D-lysine approximately Matrigel greater than hyaluronic acid approximately plastic) and the presence of endothelial cell growth supplement and heparin in the growth medium. Non-endothelial cells are removed by allowing these cells to attach to a matrix that RCMECs attach to poorly (e.g., poly-D-lysine) and then transferring isolated endothelial cell clusters to fibronectin-coated dishes. These cell cultures, labeled with 1,1'-dioctadecyl-3,3,3',3'-tetramethyl-indocarboxyamine perchlorate (DiI-Ac-LDL) and analyzed using flow cytometry, were 97.7 +/- 2.6% (n = 6) pure. By excluding those portions designed to isolate brain microvessels, the method was adapted to obtain RAEC cultures. RAECs do not isolate as clusters and have different morphology in culture, but respond similarly to matrices and growth medium supplements. RCMECs and RAECs have Factor VIII antigen, accumulate DiI-Ac-LDL, contain Weibel-Palade bodies, and have complex junctional structures. The activities of gamma-glutamyl transferase and alkaline phosphatase were measured as a function of time in culture. RCMECs had higher enzymatic activity than RAECs. In both RCMECs and RAECs enzyme activity decreased with time in culture. The function of endothelial cells is specialized depending on its location. This culture method allows comparison of two endothelial cell cultures obtained using very similar culture conditions, and describes their initial characterization. These cultures may provide a model system to study specialized endothelial cell functions and endothelial cell differentiation.

Animals

TULIPS: the Uppsala-Linkoping Image Processing System.

The Uppsala-Linkoping Image Processing System, TULIPS, is described. TULIPS, a hardware-software system designed for cell image processing, was developed at Uppsala University Hospital in cooperation with the Department of Electrical Engineering at Linkoping University. The hardware part of the image processing system is built around a high-speed data bus with a capacity of about 40 M byte/sec connected to a PDP-11/55 host computer. An image memory, an LSI-11 microcomputer and a video interface for displaying the image memory content on a TV monitor are also connected to the high-speed bus. An automated microscope and a "Poulsen processor" for low resolution segmentation, both to be attached to the high-speed bus, are being developed. A monitor and an interpreter for an image processing language have been implemented on the host computer. This software system allows interactive, as well as batch, processing. The degree of user interaction is easily adapted to the user's needs. The image processing language is command oriented, and it is easily expanded by adding new commands. The system has been used both for studies in the field of quantitative microscopy and as a platform for development and testing of new image processing algorithms.

Computers

Evaluation of methods for shaded surface display of CT volumes.

There are several ways to compute a shaded surface display of radiological 3D density volumes. In this paper we evaluate 12 methods which are different combinations of principles for detection of the surface to be displayed (gray-value threshold, gradient threshold, zero-crossing of 2nd derivative), localizing this surface in space (grid-point accuracy, subvoxel accuracy) and finally estimating the direction of the surface normal (from the gradient in the 2D depth image, from the gradient in the 3D-volume). The best quality is obtained by zero-crossing detection, subvoxel localization, and 3D-gradient orientation.

Data Display