Functional and molecular biological evidence of SGLT-1 in the ruminal epithelium of sheep.

Because of the effective catabolism of D-glucose to short-chain fatty acids by intraruminal microorganisms, the absorption of D-glucose from the rumen was thought to be of minor importance. However, clinical studies suggested that significant quantities of D-glucose are transported from the ruminal contents to the blood. We therefore tested the ruminal epithelium of sheep for the presence of Na(+)-glucose cotransporter 1 (SGLT-1) on both the functional and mRNA levels. In the absence of an electrochemical gradient, 3-O-methylglucose (3-OMG) was net absorbed across isolated ruminal epithelia mounted in Ussing chambers. The net transport of 3-OMG followed Michaelis-Menten kinetics and was sensitive to phlorizin or decreasing Na(+) concentrations. The mucosal addition of 10 mM D-glucose induced an immediate, phlorizin-sensitive increase in short-circuit current (I(sc)). I(sc) could also be increased by serosal addition of D-glucose or D-mannose, but electrogenic uptake of D-glucose or 3-OMG added on the mucosal side was still detectable after serosal stimulation of I(sc). RT-PCR using primers specific for the ovine intestinal SGLT-1 with subsequent TA cloning and sequencing revealed 100% identity between the cloned cDNA and mRNA fragment 187-621 of ovine intestinal SGLT-1. In conclusion, the ruminal epithelium has a high-affinity SGLT-1, which indicates that it maintains the capacity for D-glucose absorption.

Point counting on the Macintosh. A semiautomated image analysis technique.

In image analysis, point counting is used to estimate three-dimensional quantitative parameters from sets of measurements made on two-dimensional images. Point counting is normally conducted either by hand only or manually through a planimeter. We developed a semiautomated, Macintosh-based method of point counting. This technique could be useful for any point counting application in which the image can be digitized. We utilized this technique to demonstrate increased vacuolation in white matter tracts of rat brains, but it could be used on many other types of tissue. Volume fractions of vacuoles within the corpus callosum of rat brains were determined by analyzing images of histologic sections. A stereologic grid was constructed using the Claris MacDraw II software. The grid was modified for optimum line density and size in Adobe Photoshop, electronically superimposed onto the images and sampled using version 1.37 of NIH Image public domain software. This technique was further automated by the creation of a macro (small program) to create the grid, overlay the grid on a predetermined image, threshold the objects of interest and count thresholded objects at intersections of the grid lines. This method is expected to significantly reduce the amount of time required to conduct point counting and to improve the consistency of counts.

Artificial intelligence in automated classification of rat vaginal smear cells.

Microscopic examination of vaginal smears has been used routinely to determine the stage of the estrous cycle of female rats in reproductive research. The stage of the estrous cycle is based on relative counts of nucleated epithelial cells, cornified epithelial cells and leukocytes. The purpose of this project was to explore automation of vaginal smear analysis using image processing and artificial intelligence techniques. A fully connected back-propagation neural network was used to locate all potential objects in a digitized scene. A unique algorithm was then employed to center a subsequent sampling box to collect pixel intensity values from the red and green components of each image. A final neural network was used in the classification of cell type. Neural networks were used because of their ability to generalize among input patterns and to tolerate extraneous noise due to variations in staining artifacts and aberrant illumination of the microscope field. This preliminary cell diagnosing system not only provides the basis for the fully automated system but also provides a method by which many other cytologic image processing problems can be automated.