NAB directed against IFN-beta have in vivo biological consequences in patients with MS. The frequency with which MS patients develop NAB against IFN-beta is significantly greater with IFN-beta-1b therapy compared with IFN-beta-1a therapy. Treatment decisions in MS patients treated with IFN-beta should take into account development of NAB.
The effects of age, breed, and diet on fecal chemistry, enzyme activity, and bacterial populations of dogs were studied. Eighteen dogs from two age groups (young: 2.5 +/- 0.5 years, old: 10.9 +/-0.7 years) and three different breeds (German shepherds, miniature schnauzers, and English setters) were rotated through a Latin Square design such that every dog was fed each of the diets. The test diets included a low-fiber (control) diet and a 10% fiber diet which contained 5% soybean hulls and 5% beet pulp. Inclusion of 10% fiber in the diet decreased the fecal concentration of ammonia, sulfide, and indole. Fiber inclusion significantly increased acetic, propionic, and butyric acid concentrations, while fecal pH decreased by 0.4 units. Fresh fecal samples were plated on selected aerobic and anaerobic culture media and DNA extracted for denaturing gradient gel electrophoresis (DGGE) analysis of PCR-amplified 16S ribosomal DNA fragments. Plate counts showed significant effects of breed (p < or = 0.05) and age (p < or = 0.01) on selected aerobic and anaerobic bacterial counts, while no significant effect of diet was found. Analysis of PCR-DGGE banding patterns showed there was a tendency for individual dogs to cluster together according to age (young or old dogs) and also for size (large or small dogs). However, the outstanding conclusion obtained from the DGGE analysis of fecal bacterial profiles was that individual dogs had their own characteristic banding pattern which was unique and stable. The relative stability and individuality of the patterns indicates that each individual harbored a characteristic fecal bacterial community which was independent of diet.
The design of the system facilitates education on a range of time-sensitive topics and will make it possible easily to incorporate other models, new management recommendations or information from new sensors as they are developed.
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